# A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

## 1 Introduction to Active Learning in Remote Sensing

### 1.1 Definition and Principles of Active Learning

Active learning represents a paradigm shift in the traditional machine learning workflow, wherein the machine assumes a proactive role in the data annotation process, thereby optimizing the utilization of limited labeled data [1]. Unlike passive learning, which relies on a fixed set of labeled data, active learning iteratively selects the most informative data points for annotation to maximize learning efficiency and minimize labeling effort. At the core of this iterative process is the acquisition function, designed to identify data points that are most likely to improve the model’s performance when labeled.

Central to active learning is the principle of informativeness, which focuses on selecting samples that offer the greatest marginal benefit to the model’s predictive accuracy [2]. This informativeness is often closely tied to the concept of uncertainty, where samples that the model is most uncertain about are preferred for labeling. Typically, these samples lie in the decision boundaries of the classifier, where predictions are least certain, leading to a more refined understanding of the underlying data distribution. By concentrating on these critical areas, the model can enhance its generalization capabilities and reduce the reliance on a large volume of labeled data.

Another crucial aspect of active learning is the reduction of redundancy. In remote sensing image classification, where vast datasets cover similar regions, redundancy can lead to inefficient learning. Active learning strategies thus incorporate mechanisms to avoid selecting redundant samples, ensuring that each labeled instance provides unique and valuable information to the model [3].

The process of active learning operates through a continuous feedback loop involving the model and the annotator. Initially, a small set of labeled data is used to train an initial model, which then predicts on a pool of unlabeled data. The acquisition function evaluates these predictions to identify the most informative samples, which are subsequently annotated and added to the training set. This cycle continues until a predefined stopping criterion is reached, such as the convergence of the model’s performance [2]. The stopping criterion is essential for determining the optimal point to terminate the active learning process, ensuring that additional annotations do not yield diminishing returns.

In the context of remote sensing, the application of active learning is especially significant due to the scarcity of labeled data and the high costs associated with manual labeling [3]. Labeling satellite images for specific land use categories requires substantial domain expertise and time, making the active learning approach highly advantageous. By strategically choosing samples most likely to improve the model’s performance, active learning can notably reduce the labeling burden while preserving or even improving the accuracy of the final model.

Moreover, the effectiveness of active learning in remote sensing is bolstered by the integration of domain-specific knowledge and preprocessing techniques. Leveraging prior knowledge about the spatial and spectral characteristics of remote sensing data can refine selection criteria. Additionally, preprocessing steps such as normalization, filtering, and feature extraction can enhance the quality of the unlabeled pool, resulting in more accurate and meaningful selections.

A key challenge in applying active learning to remote sensing is the need for scalable and computationally efficient acquisition functions. Traditional active learning approaches often involve computationally intensive processes, such as evaluating the uncertainty of every sample in the unlabeled pool, which can become impractical for large-scale datasets [4]. Recent advancements have focused on developing lightweight and efficient acquisition strategies, including approximations and parallelization techniques, to ensure the practicality of active learning in high-resolution remote sensing applications [3].

Furthermore, the success of active learning in remote sensing hinges on the model’s ability to generalize well beyond the selected samples. This necessitates a robust understanding of the underlying data distribution and the capability to extrapolate from the labeled data to the entire dataset. Incorporating diverse sampling methods, such as random sampling alongside uncertainty-based sampling, can help ensure that the model learns a broad spectrum of patterns and variations present in the data [3].

In conclusion, the principles of active learning – prioritizing informative samples, reducing redundancy, and integrating domain-specific knowledge – are essential for optimizing the annotation process in remote sensing image classification. By thoughtfully designing and implementing active learning strategies, researchers and practitioners can enhance the efficiency and effectiveness of machine learning models, ultimately leading to more accurate and insightful analyses of remote sensing data.

### 1.2 Active Learning in the Context of Remote Sensing

Active learning (AL) plays a crucial role in enhancing the efficiency and effectiveness of machine learning models, particularly in scenarios where labeled data are scarce, expensive, or challenging to obtain. Remote sensing image classification is a prime example of such a scenario, where the acquisition of labeled data is often a significant bottleneck. The vast geographic coverage involved in remote sensing, coupled with the intricate details contained within high-resolution satellite images, necessitates specialized expertise in the labeling process. Additionally, the sheer volume of images collected from various sensors across the globe further exacerbates the labeling challenge. Consequently, traditional supervised learning approaches often falter due to the high costs and logistical constraints associated with generating sufficient labeled datasets.

One of the foremost challenges in remote sensing image classification is the vast geographic coverage of the Earth's surface. This necessitates the collection of data from multiple sensors, including optical, radar, and thermal imagery. Each type of sensor captures different aspects of the Earth’s surface, and their integration into a unified dataset can be computationally intensive. Moreover, the diverse environmental conditions across different regions—such as varying climate zones, topographies, and urban versus rural settings—further complicate the classification task. These factors contribute to the need for extensive, high-quality labeled datasets to train robust and accurate classifiers. However, acquiring such datasets can be prohibitively expensive and time-consuming, given the requirement for specialized knowledge in interpreting and annotating the images. For instance, accurate labeling of remote sensing images often requires expertise in fields such as geography, ecology, and environmental science [5].

Another significant challenge in remote sensing image classification is the complexity and variability inherent in the data. High-resolution satellite images capture a multitude of features at varying scales, ranging from individual objects like buildings and vehicles to broader patterns such as land cover changes. This heterogeneity requires sophisticated algorithms capable of distinguishing between subtle differences in image content, which can be particularly challenging without extensive labeled data. Furthermore, the presence of occlusions, shadows, and variations in lighting conditions adds another layer of complexity to the classification task. As a result, even state-of-the-art deep learning models may struggle to achieve satisfactory performance when trained on small or biased datasets.

Active learning offers a promising solution to these challenges by enabling the selection of the most informative samples for labeling, thereby optimizing the use of limited labeled data. By focusing on samples that are likely to provide the greatest improvement in model performance, AL can significantly reduce the labeling burden while maintaining high classification accuracy. Several active learning techniques have been adapted or developed specifically for remote sensing image classification. For example, region-level active detector learning (RADL) [6] proposes a novel strategy that extends beyond conventional image-level or object-level approaches by promoting spatial diversity in the selection of samples. This method avoids redundant queries from the same image and minimizes context switching for the labeler, leading to more efficient and effective labeling efforts.

Active learning can also leverage domain adaptation techniques to address the issue of class imbalance prevalent in remote sensing datasets. Class imbalance arises when certain classes are significantly underrepresented compared to others, leading to biased models that perform poorly on minority classes. To mitigate this, active learning strategies can prioritize the selection of minority class samples for labeling, ensuring that the model receives balanced exposure to all classes. This is particularly important in applications such as aircraft detection, where the objective is to identify relatively rare objects amidst vast amounts of background data [7]. By focusing on informative samples from underrepresented classes, AL can help train models that are more robust and reliable across all categories.

Moreover, active learning can facilitate the integration of self-supervised learning (SSL) techniques, which have gained prominence for their ability to learn meaningful representations from unlabeled data. SSL methods, such as those explored in "CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning," enable the construction of powerful feature extractors that can be fine-tuned on smaller labeled datasets [8]. This dual approach not only enhances the efficiency of the labeling process but also improves the generalization capabilities of the final models. By utilizing SSL, AL can identify the most informative samples that benefit from additional labels, thereby maximizing the impact of the limited labeling resources.

Additionally, active learning can enhance the use of ensemble methods in remote sensing applications. Ensemble techniques, which combine multiple models to improve robustness and accuracy, can be particularly beneficial in AL frameworks. For instance, leveraging ensemble self-supervised pre-trained models can provide a more robust feature representation that is less prone to overfitting. This is critical in remote sensing, where the variability in data sources and environmental conditions can lead to noisy or inconsistent features. By combining multiple models trained on different subsets of data, AL can ensure that the final classification results are more consistent and reliable.

By optimizing the labeling process and enhancing the efficiency of data utilization, active learning can significantly alleviate the burdens associated with generating large labeled datasets. Through the integration of advanced techniques such as self-supervised learning, ensemble methods, and domain adaptation, active learning can pave the way for more robust and accurate remote sensing models, ultimately facilitating more informed decision-making and deeper understanding of the Earth’s surface.

### 1.3 Active Learning Techniques in Object Detection

Active learning techniques have emerged as powerful tools for reducing the labeling workload and improving detection accuracy in object detection tasks involving high-resolution satellite images. These techniques aim to select the most informative samples for annotation, thereby maximizing the efficiency of the training process. Building upon the foundation of active learning's ability to optimize labeled data usage, this section delves into several key approaches specifically tailored for object detection in remote sensing imagery.

One pioneering approach is the introduction of region-level active detector learning [6], which introduces a novel strategy for selecting informative regions rather than entire images or individual objects for annotation. This method emphasizes spatial diversity and minimizes context switching for the annotator by avoiding nearby redundant queries from the same image. Such an approach significantly decreases labeling effort and improves the detection of rare objects in scenarios characterized by class imbalance and visual clutter. The effectiveness of region-level active detector learning lies in its ability to strike a balance between minimizing labeling effort and ensuring comprehensive coverage of diverse regions within the dataset.

Another notable technique is MuRAL (Multi-Scale Region-based Active Learning) [9], which identifies informative regions of various scales to reduce annotation costs and enhance training performance. MuRAL employs an informative region scoring mechanism that considers both the predicted confidence of instances and the distribution of each object category. By doing so, it focuses more on difficult-to-detect classes, thereby improving overall detection performance. Additionally, MuRAL’s scale-aware selection strategy ensures that regions selected for labeling are diverse, spanning different scales, which helps in maintaining training stability and preventing sampling bias.

The DeLR (Decoupling Localization and Recognition for Active Learning) method [10] presents a unique approach to active learning by decoupling the localization and recognition tasks. This strategy allows for the selective annotation of object regions based on their localization accuracy, potentially freeing up resources for more informative samples. By focusing on region-level annotations instead of exhaustive image-level annotations, DeLR achieves significant reductions in labeling effort while maintaining or even improving detection accuracy. The ability to leverage pseudo-class labels provided by a trained model further reduces the recognition annotation burden, making this method particularly suitable for scenarios where class labeling is challenging or expensive.

Reinforcement-based display-size selection [11] offers another innovative approach to active learning in object detection. This method integrates reinforcement learning to determine the optimal combination of diversity, representativeness, and uncertainty criteria for selecting critical images for annotation. By iteratively updating change detection results based on user-provided annotations, this approach enables more efficient exploration of the dataset and refinement of detection models. The integration of reinforcement learning enhances the adaptability and effectiveness of the active learning process, allowing it to dynamically adjust to the specific characteristics of the satellite imagery being analyzed.

In addition to these methods, MUS-CDB (Mixed Uncertainty Sampling with Class Distribution Balancing) [12] presents a solution specifically tailored to aerial object detection scenarios characterized by long-tailed class distributions and dense small objects. MUS-CDB incorporates both object-level and image-level informativeness criteria to avoid redundant querying and incorporates a class-balancing criterion to favor minority objects. This method also devises a training loss to mine latent knowledge in unlabeled image regions, further enhancing the model’s ability to generalize and detect diverse classes effectively. Empirical results demonstrate that MUS-CDB can achieve comparable performance to other active learning methods while significantly reducing the labeling effort required.

Furthermore, hybrid clustering active learning [13] represents another effective strategy for active learning in remote sensing object detection. This approach combines diversity- and uncertainty-based active learning methods to select the most relevant data for annotation. The hybrid clustering method leverages the strengths of both approaches, enabling more accurate and efficient detection of aircraft in satellite imagery. Experimental evaluations indicate that this method can offer better or competitive results compared to other active learning methods, highlighting its utility in operational settings where precise and reliable detection is paramount.

These active learning techniques not only reduce the labeling workload but also enhance the overall performance of detection models, making them indispensable tools for addressing the challenges associated with object detection in high-resolution satellite images. As remote sensing applications continue to evolve, the continued development and refinement of active learning methods will be crucial in addressing the ongoing challenges of data scarcity and the need for accurate, efficient detection systems.

### 1.4 Role of Self-Supervised Learning in Active Learning

Self-supervised learning (SSL) has gained significant traction in recent years as a powerful technique for pre-training models, enabling them to extract useful features from raw data without the need for extensive labeled datasets. In the context of remote sensing, SSL can serve as a foundational step in the pipeline for active learning, particularly when labeled data are scarce. By leveraging SSL, active learning strategies can become more effective at identifying informative samples for labeling, thereby optimizing the allocation of limited labeling resources.

One of the key advantages of SSL in the realm of active learning is its ability to generate rich and meaningful representations from large volumes of unlabeled data. These representations can then be utilized to inform the selection of samples that are most likely to yield substantial improvements in model performance. For instance, the paper "PT4AL: Using Self-Supervised Pretext Tasks for Active Learning" [14] highlights how SSL can be combined with active learning to enhance the identification of informative samples. The authors propose a novel active learning approach that integrates self-supervised pretext tasks with a unique data sampler. The pretext task learner is trained on the entire unlabeled dataset, and the samples are subsequently ranked according to their loss from the pretext task. During active learning iterations, the most uncertain samples within each batch are selected for labeling. This approach not only leverages the inherent structure of the data captured by the SSL model but also ensures that the labeled samples chosen are both challenging and representative, leading to improved performance across various image classification and segmentation benchmarks.

Another critical aspect of SSL in active learning is its potential to address the cold-start problem, where initial labeled data are insufficient to drive effective learning. Traditional active learning approaches often struggle to perform well with a very small initial labeled set, as they rely heavily on the initial seed set to guide the selection of subsequent samples. However, by employing SSL, the initial labeled set can be augmented with a richer set of pre-trained features, facilitating a smoother start to the active learning process. This is demonstrated in "Reducing Label Effort: Self-Supervised meets Active Learning" [14], where the authors integrate active learning with self-training, showing that the combination is particularly beneficial when the labeling budget is high. The integration of SSL and active learning helps mitigate the effects of a limited initial labeled set, as the SSL model provides a more informed basis for selecting informative samples early in the learning process.

Furthermore, SSL can significantly reduce the labeling effort required for active learning by improving the model’s capacity to generalize from a smaller number of labeled examples. This is crucial in remote sensing applications where acquiring labeled data can be time-consuming and resource-intensive. The use of SSL in conjunction with active learning can thus lead to more efficient learning processes, as the models are pre-informed about the underlying data structure before the active learning phase begins. As noted in "Combining Self-labeling with Selective Sampling" [14], naive self-labeling approaches can introduce biases and skew class distributions, potentially harming model performance. However, by incorporating SSL, these issues can be mitigated, as the pre-trained representations provide a more balanced view of the data distribution, aiding in the selection of truly informative samples.

The synergy between SSL and active learning also addresses the challenge of class imbalance issues commonly found in remote sensing datasets. Class imbalance can severely hinder the performance of machine learning models, as they tend to favor the majority class over minority classes. SSL can help alleviate this issue by generating more nuanced representations that capture the variability within each class, even when the class distribution is skewed. By utilizing SSL-generated representations, active learning algorithms can more accurately assess the informativeness of samples across different classes, leading to a more balanced and effective learning process. For example, in "On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake" [14], the authors demonstrate that self-supervised pre-training significantly improves semi-supervised learning, especially in scenarios with few labeled examples. This underscores the potential of SSL to enhance the effectiveness of active learning in handling imbalanced datasets.

Moreover, SSL facilitates the integration of active learning with ensemble methods, further boosting the robustness and accuracy of remote sensing classification models. By pre-training ensemble models with SSL, the individual components of the ensemble can be initialized with a richer understanding of the data, enabling them to make more informed decisions during the active learning process. This approach not only enhances the representativeness of the labeled samples but also promotes diversity among the ensemble members, leading to improved overall performance. As highlighted in "Reducing Label Effort: Self-Supervised meets Active Learning" [14], the combination of SSL and active learning can lead to significant performance improvements, particularly when the labeling budget is high. The enhanced initialization provided by SSL allows the ensemble members to converge more rapidly and achieve higher accuracy with fewer labeled samples.

In summary, the integration of SSL into active learning strategies represents a promising avenue for improving the efficiency and effectiveness of remote sensing image classification tasks. Through its ability to generate rich and meaningful representations from unlabeled data, SSL can significantly enhance the selection of informative samples for labeling, reduce labeling effort, and improve model performance. As SSL continues to advance, its potential to revolutionize active learning in remote sensing and other domains will undoubtedly grow, paving the way for more sophisticated and data-efficient machine learning systems.

### 1.5 Graph-Based Approaches for Handling Imbalance

Graph-based approaches have emerged as powerful tools for managing class imbalance issues in remote sensing datasets, offering innovative solutions to the challenge of balancing class representations during the active learning process. Building upon the synergies between SSL and active learning discussed previously, these methods leverage the inherent structural properties of graph data to facilitate more equitable learning among minority and majority classes. One notable approach is the GALAXY method [15], which introduces a novel strategy for active learning in extreme class-imbalance scenarios by blending ideas from graph-based active learning and deep learning. This method demonstrates significant improvements in balancing class representations compared to traditional active learning techniques, thus enhancing the overall performance of models trained on imbalanced datasets.

Class imbalance is a pervasive issue in remote sensing, where the scarcity of labeled data exacerbates the problem. Traditional methods often struggle to achieve balanced performance across all classes, particularly when dealing with extremely imbalanced datasets. To address this, researchers have developed several graph-based techniques that aim to mitigate the adverse effects of class imbalance. For instance, the GALAXY method [15] proposes a refined form of uncertainty sampling that focuses on gathering a more class-balanced dataset. By utilizing graph-based principles, GALAXY selects more representative and informative samples for labeling, thereby ensuring that the training process is more inclusive of minority classes.

Another significant contribution in this domain is the VIGraph method [16]. This method employs generative self-supervised learning to address class imbalance issues in graph data. Unlike traditional methods such as SMOTE, which can struggle with constructing imbalanced graphs effectively, VIGraph introduces a novel approach that relies on the Variational GAE (VGAE) as its fundamental model. Through variational inference, VIGraph generates minority nodes directly from the data, eliminating the need for manual integration and retraining steps. This process not only helps in creating balanced training sets but also ensures that the generated nodes are high-quality and directly usable for classification tasks.

In the context of active learning, graph-based methods can be particularly advantageous due to their ability to adaptively select informative samples for labeling. By considering the connectivity and relationships within the graph, these methods can identify samples that are most likely to contribute to improving the model's performance across all classes. For example, the BuffGraph method [17] introduces a novel concept of inserting buffer nodes into the graph structure to modulate the impact of majority classes on minority nodes. This approach aims to reduce the bias towards majority classes by isolating the influence of majority nodes through the use of buffer nodes. Extensive experiments have shown that BuffGraph outperforms existing baseline methods in both natural and imbalanced settings, underscoring the potential of graph-based techniques in enhancing the performance of active learning models in class-imbalanced scenarios.

The use of graph information bottlenecks is another area where graph-based methods have shown promise. For instance, the Graph Information Bottleneck (GIB) method [18] introduces a novel contrastive vision GNN (SC-ViG) architecture designed to maximize task-related information while minimizing task-independent redundancy. This method constructs node-masked and edge-masked graph views to obtain an optimal graph structure representation, allowing for adaptive masking of nodes and edges. By integrating these techniques, the GIB method improves the segmentation and classification tasks of remote sensing images, demonstrating superior performance compared to state-of-the-art methods. This highlights the versatility of graph-based approaches in addressing various challenges in remote sensing, including class imbalance and irregular object modeling.

Furthermore, the integration of geographical awareness into self-supervised learning offers new opportunities for handling class imbalance. The Geography-Aware Self-Supervised Learning (GASSL) method [19] leverages the spatio-temporal structure of remote sensing data to construct temporal positive pairs and design pretext tasks. By exploiting the geographic location and temporal variations in the data, GASSL can effectively close the performance gap between contrastive and supervised learning methods. This method not only enhances the quality of the learned representations but also facilitates the transfer of knowledge across different geographic regions, thereby improving the robustness and generalizability of the models in handling class-imbalanced datasets.

In addition to these advancements, the application of graph-based techniques to active learning frameworks has led to the development of novel methods for handling class imbalance in remote sensing datasets. For instance, the Scalable Data Balancing for Unlabeled Satellite Imagery [20] method presents an iterative approach to balancing unlabeled data by utilizing image embeddings as proxies for labels. This method enables the automatic balancing of data without requiring extensive manual labeling efforts, thus facilitating the efficient utilization of unlabeled data in active learning processes. By leveraging the intrinsic properties of the data, such as the distribution of land and water in Earth imagery, this method demonstrates the potential of graph-based approaches in addressing the challenges posed by large-scale, unlabeled datasets.

Moreover, the combination of graph-based techniques with semi-supervised learning methods has shown promising results in tackling class imbalance issues. The Land Cover and Land Use Detection using Semi-Supervised Learning [21] method utilizes a distribution alignment technique to iteratively redistribute classes and create artificial labels. This approach not only reduces the reliance on labeled data but also mitigates the bias introduced by class imbalance. By balancing the classes through resampling and leveraging semi-supervised learning, this method achieves improved accuracy and consistency across different datasets, illustrating the efficacy of graph-based methods in enhancing the robustness and reliability of remote sensing models.

Overall, graph-based approaches have demonstrated significant potential in addressing class imbalance issues in remote sensing datasets through active learning. By leveraging the structural properties of graph data, these methods can efficiently balance class representations and improve the overall performance of machine learning models. Whether through the generation of minority nodes, the isolation of majority class influence, or the adaptation of learning frameworks to handle imbalanced data, graph-based techniques offer versatile solutions for overcoming the challenges posed by class imbalance in remote sensing. As research continues to advance in this domain, it is anticipated that these methods will play an increasingly important role in enabling more accurate and reliable classification and segmentation of remote sensing images.

### 1.6 Active Label Refinement and Semantic Segmentation

Active label refinement represents a promising direction for improving the quality of semantic segmentation in remote sensing applications. Given the labor-intensive and costly nature of obtaining pixel-level annotations for satellite images, researchers have developed active learning strategies to refine initial labels acquired through low-cost means, such as crowdsourcing or pretrained models, thereby enhancing the precision and reliability of subsequent semantic segmentation tasks.

One notable approach is the active label refinement strategy outlined in "[22]." This method initiates with a low-cost initial labeling phase using either crowdsourced workers or pretrained networks. Recognizing the potential inaccuracies in these initial labels, an active learning loop is employed to iteratively select and correct mislabeled regions based on the model's uncertainty. Specifically, the algorithm identifies areas where the model's confidence is lowest, indicating high uncertainty, and these regions are then reviewed and corrected by human annotators. Over time, this iterative process leads to a gradual enhancement in the accuracy of the segmentation network. Experiments on satellite images of Bengaluru, India, illustrate the significant improvement in segmentation performance through this active refinement process.

Another innovative approach is described in "[23]." This paper introduces a method that integrates active learning with path planning to enhance semantic segmentation quality in unknown environments. The algorithm uses an adaptive map-based planner to guide the acquisition of training data, focusing on regions characterized by high model uncertainty and substantial semantic variation. Additionally, the system combines sparse high-quality human labels with pseudo-labels generated from areas of high certainty within the environment map. This dual-labeling strategy ensures that the model receives both high-quality and diverse training data, thereby boosting its generalization and robustness. Experimental results show that this approach achieves segmentation performance comparable to fully supervised methods while substantially reducing the human labeling effort.

Moreover, "[24]" presents a novel framework that leverages self-supervised pretext tasks to enhance active learning in semantic segmentation. This framework first trains a pretext task learner on the unlabeled dataset before commencing active learning iterations. During these iterations, the model selects the most uncertain samples from the dataset for annotation, guided by the loss of the self-supervised pretext task. This method ensures that the selected samples are both challenging and representative, thereby contributing to the improvement of the segmentation model. Experiments conducted on various benchmarks, including CIFAR10, Caltech-101, ImageNet, and Cityscapes, confirm the effectiveness of this approach in enhancing model performance while minimizing the number of labeled samples required.

Additionally, "[25]" introduces an active semi-supervised learning approach that integrates active learning with a teacher-student framework to improve semantic segmentation. This method minimizes the number of annotations needed per image by focusing on the most informative regions rather than entire images. The teacher model generates pseudo-labels for unlabeled data, which are subsequently refined by the student model to enhance segmentation accuracy. Experiments on the CamVid and CityScapes datasets reveal that this method achieves over 95% of the network's performance on the full-training set using less than 17% of the training data, demonstrating its efficiency in reducing annotation efforts while maintaining high segmentation accuracy.

These advancements in active label refinement for semantic segmentation not only alleviate the dependency on extensive manual labeling but also bolster the robustness and adaptability of segmentation models in remote sensing applications. The integration of active learning with various labeling strategies, such as self-supervised learning, path planning, and semi-supervised learning, marks a significant step toward more efficient and effective training processes. Continuous refinement of initial labels ensures that final segmentation models are both accurate and reliable, rendering them indispensable tools for a broad spectrum of remote sensing applications, ranging from environmental monitoring to urban planning.

However, several challenges persist in the deployment of active label refinement for semantic segmentation. Firstly, the variability in quality and consistency of initial labels obtained through low-cost means, like crowdsourcing, can introduce errors into the refinement process. Secondly, the computational intensity involved in selecting informative samples for labeling, particularly when dealing with large and high-resolution satellite images, poses another hurdle. Lastly, maintaining model unbiasedness and performance consistency across different geographical regions and image conditions remains a critical concern.

Future research should concentrate on developing more sophisticated algorithms to address these challenges. Incorporating advanced uncertainty quantification techniques and robust optimization strategies could enhance the accuracy and reliability of active label refinement processes. Furthermore, investigating the integration of domain adaptation and transfer learning techniques may improve the generalizability of segmentation models across diverse environments. Addressing these challenges holds the potential to significantly advance the field of remote sensing and pave the way for more efficient and precise semantic segmentation solutions.

### 1.7 Utilizing Variational Autoencoders for Informative Sample Selection

Utilizing Variational Autoencoders (VAEs) for identifying informative samples for labeling is a burgeoning area of research that leverages the unique capabilities of VAEs in high-dimensional data spaces. These generative models excel in learning compressed representations of data in a lower-dimensional latent space, making them particularly useful in capturing the essential features of complex data distributions. This section explores the application of VAEs for selecting informative samples in the context of active learning, specifically for remote sensing image classification.

One of the primary challenges in active learning for remote sensing image classification is efficiently identifying the most informative samples for labeling. These samples should provide maximum information gain to improve the model’s predictive performance. Traditional methods for selecting informative samples often rely on heuristics that may not fully account for the complexities inherent in high-dimensional data. However, VAEs offer a principled approach to navigating these complexities and identifying both informative and representative samples.

In "The Effectiveness of Variational Autoencoders for Active Learning," researchers propose an innovative approach to active learning by leveraging VAEs to select a core-set of labeled data points that are diverse and representative. This involves training a VAE on the entire dataset and then projecting the data into a lower-dimensional latent space. Within this space, a geometric technique is applied to select a subset of data points that form a core-set. These selected points maximize the diversity of the latent representation, ensuring that the labeled data adequately cover the complexity of the underlying data distribution. The experimental results underscore the significant improvements in accuracy compared to conventional active learning methods, highlighting the potential of VAEs in enhancing the efficiency of active learning.

The application of VAEs extends beyond simple data representation tasks, with recent advancements integrating them into more complex scenarios, such as high-dimensional Bayesian optimization. In "High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning," researchers combine VAEs with deep metric learning techniques to optimize functions in high-dimensional spaces. The latent space constructed by VAEs facilitates Gaussian process fitting, a critical component of Bayesian optimization. This method operates effectively in semi-supervised regimes where labeled data are scarce, aligning well with the challenges faced in remote sensing datasets.

Another promising approach involves the concept of evidential sparsification of multimodal latent spaces. In "Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders," the authors investigate the idea of filtering out less relevant latent classes in a trained conditional VAE. This sparsification technique simplifies the latent space while retaining its multimodal nature, ensuring that the selected samples for labeling are both informative and representative. Experiments on tasks like image generation and human behavior prediction demonstrate the efficacy of this approach in maintaining the quality of learned representations despite reduced latent space complexity.

Latent space geometry also plays a crucial role in the effectiveness of VAEs for active learning. In "Latent Variables on Spheres for Autoencoders in High Dimensions," the authors introduce Spherical Auto-Encoders (SAEs), which employ spherical normalization on the latent space. This approach leverages spherical geometry for improved inference precision while maintaining the ability to perform stochastic sampling from priors. The enhanced inference capabilities of SAEs make them particularly suitable for high-dimensional and complex data structures typical in remote sensing, leading to more accurate and robust sample selection in active learning scenarios.

Furthermore, integrating ensemble methods with VAEs enhances their utility in active learning. In "Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs," researchers present an ensemble of lightweight VAEs to address the challenge of learning representations in high-dimensional, low-sample-size (HDLSS) tasks. By dividing the feature space into subsets and training individual VAEs on each, the ensemble approach aggregates information from diverse parts of the data more efficiently. This method not only improves the quality of learned representations but also demonstrates robustness to partial feature availability, a common issue in remote sensing datasets due to sensor malfunctions or data corruption.

While VAEs offer significant advantages, they also face challenges such as KL-vanishing, where the KL divergence between the approximate posterior and the prior becomes negligible, leading to suboptimal learning. In "Improve Diverse Text Generation by Self Labeling Conditional Variational Auto Encoder," the authors propose mitigating KL-vanishing by introducing a self-labeling mechanism that guides the VAE towards a more expressive latent space. This method, termed Self Labeling CVAE (SLCVAE), incorporates a labeling network to provide continuous labels in the latent space, facilitating a closer alignment between latent variables and target attributes. While primarily focused on text generation, the principles of self-labeling and continuous labeling can enhance VAEs' effectiveness in active learning for remote sensing.

Additionally, advancements in unsupervised learning of features can further benefit the use of VAEs in active learning. In "Learning Latent Subspaces in Variational Autoencoders," researchers propose Conditional Subspace VAE (CSVAE), which extracts features correlated with specific labels through mutual information minimization. This structuring of the latent space makes it easier to interpret and manipulate, a valuable capability in remote sensing for extracting meaningful features from complex imagery. By focusing on specific aspects of the data, CSVAE aids in the selection of informative samples pertinent to the classification task.

In conclusion, VAEs offer a robust framework for identifying informative samples in active learning for remote sensing image classification. Through diverse approaches such as core-set selection, latent space sparsification, and feature extraction, VAEs provide a versatile toolkit for enhancing the efficiency and effectiveness of active learning. Future research should explore integrating VAEs with advanced techniques, such as ensemble methods and reinforcement learning, to optimize the active learning process in remote sensing.

### 1.8 Active Learning for Data-Efficient Change Detection

Active learning strategies tailored for change detection in remote sensing play a crucial role in enhancing the efficiency and accuracy of detecting surface changes in high-resolution satellite images. Change detection involves identifying modifications in land use, infrastructure, vegetation, and other features across two or more time points. Given the vastness of the Earth's surface and the rapid pace at which changes occur, acquiring large volumes of accurately labeled data for training models is often impractical. Thus, active learning provides a solution by enabling the identification and labeling of highly informative samples that contribute significantly to model performance, thereby reducing the overall labeling burden.

A key aspect of active learning for change detection is its ability to find highly informative samples that are representative of the underlying distribution of changes. For instance, the paper titled "Deep Active Learning in Remote Sensing for data efficient Change Detection" [26] introduces a framework where active learning is employed to identify such informative samples based on the uncertainty of predictions made by deep neural networks. This uncertainty can be quantified through variance or entropy across explicit or implicit model ensembles, allowing the selection of samples that are likely to improve model performance the most. By selectively choosing these samples, active learning ensures that the model receives the most beneficial information for learning, ultimately achieving the same performance as models trained on larger, pre-labeled datasets but with approximately 99% fewer annotated samples.

Balancing training distributions is another critical aspect of active learning in change detection, especially given the imbalanced datasets often encountered in this field. The paper "GALAXY: Graph-based Active Learning at the Extreme" [15] addresses this issue by proposing a graph-based approach that automatically and adaptively selects more class-balanced examples for labeling. GALAXY performs a refined form of uncertainty sampling that gathers a more balanced dataset than vanilla uncertainty sampling, ensuring that all types of changes are adequately represented in the training process. This balanced representation is essential for improving the model’s ability to generalize across different types of changes and for handling the variability in the appearance of changes.

Reinforcement learning (RL) has also been explored to enhance active learning strategies in change detection. The paper "Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection" [11] demonstrates the potential of RL in optimizing the selection of display sizes for active learning iterations. This approach uses a probabilistic framework to assign relevance measures to each unlabeled sample, obtained by minimizing an objective function that incorporates diversity, representativeness, and uncertainty. By leveraging RL, the framework dynamically adjusts the combination of these criteria to maximize the informativeness of the selected samples, leading to better generalization and a reduction in the number of samples needed for labeling.

Moreover, the integration of reinforcement learning with active learning improves the adaptability and robustness of models in handling complex and varying change detection scenarios. The paper "Reinforcement-based Frugal Learning for Satellite Image Change Detection" [27] introduces a novel interactive satellite image change detection algorithm that uses RL to determine the optimal sequence of interactions with an oracle (human annotator) to minimize the number of queries required for achieving satisfactory performance. The framework models the relevance of each unlabeled sample probabilistically and utilizes RL to fine-tune the parameters of this framework over time. This adaptive tuning allows the system to dynamically adjust its criteria for sample selection based on the evolving nature of the dataset and the model's learning progress, improving the overall efficiency of the active learning process.

Combining active learning with self-supervised learning offers a promising approach to address data scarcity issues in change detection. The paper "Frugal Learning of Virtual Exemplars for Label-Efficient Satellite Image Change Detection" [28] proposes a framework that iteratively selects the most representative and diverse virtual exemplars that challenge the current change detection model. These virtual exemplars, generated based on the model’s predictions, are designed to be highly discriminative, providing valuable feedback for refining the model. Leveraging self-supervised learning, the framework generates a rich set of synthetic training samples that are representative of various change scenarios, even when labeled data are sparse.

Ensemble methods integrated within active learning frameworks further enhance the robustness and accuracy of change detection models. Ensemble self-supervised pre-trained models can aggregate diverse perspectives on the data, improving the model's ability to handle the complexity and variability in remote sensing imagery. For example, using consensus predictions from ensemble models can guide the selection of informative samples for labeling, mitigating the risk of overfitting to specific training conditions and leading to more stable and reliable models.

In summary, active learning strategies for data-efficient change detection in remote sensing offer a promising avenue for addressing the challenges posed by vast and rapidly changing environments. These strategies leverage advanced techniques such as uncertainty sampling, reinforcement learning, self-supervised learning, and ensemble methods to efficiently identify and label highly informative samples. By balancing training distributions and dynamically adjusting selection criteria based on evolving data and learning progress, these strategies enable the creation of highly accurate and robust change detection models with minimal labeled data. As remote sensing technology evolves, the integration of these advanced active learning strategies is expected to play an increasingly important role in supporting timely and accurate environmental monitoring and management.

## 2 Metrics and Evaluation Criteria for Active Learning

### 2.1 Localization Tightness and Stability Metrics

In the realm of active learning, the introduction of metrics that evaluate the informativeness of object hypotheses has significantly advanced the process of selecting the most beneficial samples for labeling. Among these, the metrics of localization tightness and stability stand out as particularly impactful in the context of object detection tasks. These metrics are designed to assess not only the relevance of potential object candidates but also the precision of their localization within images, thereby guiding the iterative refinement of the training set in a manner that enhances both the speed and accuracy of the learning process.

Localization tightness refers to the degree to which an object hypothesis aligns closely with the actual position of the object within the image. Practically speaking, a hypothesis with high localization tightness corresponds to a bounding box that accurately encloses the object, minimizing any extraneous space that could introduce noise into the model’s training. This metric is crucial because it directly influences the model's ability to distinguish between objects and the background, which is fundamental to accurate object detection. By prioritizing samples with high localization tightness, active learning frameworks ensure that the training data includes precise examples that refine the model’s understanding of object boundaries and shapes, thereby enhancing its performance over time.

Stability, on the other hand, pertains to the consistency of the object hypothesis across multiple iterations of the model’s training cycle. A stable hypothesis maintains its predicted bounding box relatively unchanged as the model learns from additional data, indicating reliability beyond mere chance. Integrating stability into the evaluation criteria ensures that the selected samples contribute positively to the model’s learning trajectory, avoiding unnecessary fluctuations or confusion. This metric serves as a safeguard against the inclusion of misleading or ambiguous data points, which could otherwise impede the model’s convergence towards optimal performance.

To implement these metrics in practice, active learning systems typically follow a multi-step process. Initially, the system generates object hypotheses using a preliminary model or detector. Each hypothesis is then evaluated based on both localization tightness and stability. For localization tightness, the system compares the predicted bounding box of the hypothesis with ground truth annotations or with the output from a more refined model trained on partially labeled data. Hypotheses showing minimal discrepancy in this comparison are awarded higher scores, indicating their suitability for labeling. Stability is assessed by tracking changes in the bounding box predictions over successive training iterations; hypotheses that remain consistent in their predicted locations across these iterations are considered more stable and receive preference.

Empirical studies have validated the effectiveness of these metrics in reducing the amount of labeled data required to achieve target object detection performance. By focusing on the most informative and reliable samples, these metrics enable active learning frameworks to attain comparable or even superior performance to models trained on fully labeled datasets, albeit with reduced labeling effort. Moreover, their use optimizes the allocation of annotation resources, ensuring that limited labeled data contribute maximally to the model’s learning process.

Challenges do arise with the application of these metrics. Initial models or detectors used to generate object hypotheses must be sufficiently accurate and robust, as their quality directly impacts the evaluation. Additionally, the evolving nature of deep learning models complicates the assessment of stability. As models improve with more training data, what initially seems stable may later reveal inconsistencies. Iterative refinement strategies, where hypotheses are periodically re-evaluated, help address this issue. Another challenge is the computational complexity involved in calculating these metrics for large-scale datasets, requiring efficient algorithms and hardware configurations. Ongoing advancements in computational efficiency and model optimization are gradually overcoming these hurdles, facilitating broader adoption of localization-aware metrics.

In summary, the metrics of localization tightness and stability represent significant advancements in the field of active learning for object detection. By identifying and prioritizing the most informative and reliable object hypotheses, these metrics enhance the efficiency and effectiveness of active learning frameworks. As remote sensing applications increasingly require precise and efficient object detection capabilities, integrating such metrics holds great promise for advancing the field.

### 2.2 Hybrid Informative and Representative Criteria

The field of active learning has seen significant advancements, particularly in developing sophisticated evaluation criteria to guide the selection of informative samples for labeling. Building on the metrics of localization tightness and stability discussed previously, another notable approach is the hybrid informative and representative criterion, which provides a comprehensive framework for active learning that effectively balances the dual objectives of informativeness and representativeness, making it suitable for both binary and multi-class datasets.

At the core of this methodology lies the integration of informativeness and representativeness into a unified criterion, thereby addressing the inherent trade-off between these two dimensions. Informativeness refers to the capacity of a sample to reduce the uncertainty of the model, whereas representativeness pertains to the sample's ability to capture the underlying distribution of the data. By merging these two concepts, the hybrid criterion offers a more balanced approach to active learning, ensuring that the selected samples not only contribute to the model's accuracy but also reflect the diversity of the dataset. This alignment with the principles of localization tightness and stability further enhances the robustness of active learning frameworks, particularly in scenarios where precise object detection and classification are paramount.

The hybrid criterion is formulated as a weighted sum of the informativeness and representativeness measures, allowing researchers to adjust the balance according to specific requirements or constraints of the dataset. This flexibility is crucial given the variability in datasets across different domains, including remote sensing. The weighting factor, denoted as \( \alpha \), is a tunable parameter that reflects the relative importance assigned to each component. Specifically, a higher value of \( \alpha \) indicates a stronger emphasis on representativeness, while a lower value favors informativeness.

The concept of empirical risk minimization (ERM) plays a pivotal role in understanding the theoretical underpinnings of this approach. ERM is a fundamental principle in machine learning, wherein the goal is to minimize the expected risk, or the generalization error, of a model over the entire distribution of the data. In the context of active learning, ERM is extended to account for the dynamic nature of the data selection process. The hybrid criterion, by combining informativeness and representativeness, aligns with the ERM objective by ensuring that the model's performance is optimized over a diverse and representative subset of the data.

To illustrate the application of the hybrid criterion, consider a scenario involving remote sensing image classification, where the objective is to classify images into multiple categories based on the presence of different land cover types. Here, the informativeness of a sample might be assessed through measures such as entropy, mutual information, or margin sampling, which quantify the potential reduction in uncertainty contributed by the sample. On the other hand, representativeness could be evaluated using clustering-based methods or spectral analysis to ensure that the selected samples span the full spectrum of land cover variations present in the dataset.

One of the key advantages of the hybrid criterion is its versatility and applicability to both binary and multi-class datasets. This is achieved by generalizing the informativeness and representativeness measures to accommodate the complexities of multi-class classification problems. For instance, the informativeness measure can be extended to account for multi-label uncertainty, while the representativeness measure can be adapted to capture the diversity across multiple classes. This generalization allows the hybrid criterion to effectively guide the active learning process in scenarios where the dataset contains a large number of classes, a common situation in remote sensing applications.

Furthermore, the hybrid criterion facilitates the integration of domain-specific knowledge into the active learning process. In remote sensing, the geographical context and the nature of the land cover types play critical roles in determining the informativeness and representativeness of samples. For example, the inclusion of geographical coordinates and topographical features in the evaluation process can enhance the relevance and diversity of the selected samples, thereby improving the overall performance of the active learning algorithm.

The hybrid criterion has been empirically validated across various datasets and tasks, demonstrating its efficacy in enhancing the efficiency and accuracy of the active learning process. For instance, in the context of multi-class object detection in high-resolution satellite images [7], the hybrid criterion has shown promising results in reducing the labeling workload while maintaining high detection accuracy. Similarly, in multi-label classification tasks, the hybrid criterion has proven beneficial in selecting a diverse set of informative samples, leading to improved model generalization [29].

However, the implementation of the hybrid criterion also presents several challenges. One major challenge is the computational complexity associated with evaluating the representativeness measure, particularly in high-dimensional feature spaces commonly encountered in remote sensing. Additionally, the choice of appropriate weighting factors for the hybrid criterion can significantly influence the performance of the active learning algorithm. Researchers may need to conduct extensive experimentation to determine optimal values for these parameters based on the specific characteristics of the dataset and the learning task.

Despite these challenges, the hybrid informative and representative criterion offers a robust and flexible framework for active learning, particularly in the context of remote sensing image classification. By promoting a balanced consideration of informativeness and representativeness, this approach enables the selection of high-quality samples that are both informative and diverse, thereby enhancing the overall performance and efficiency of the active learning process.

### 2.3 Variance Maximization Criterion

The variance maximimization criterion, as introduced in "A Variance Maximization Criterion for Active Learning" [30], offers a robust and versatile approach for evaluating the informativeness and representativeness of unlabeled data in the context of active learning. This criterion, denoted as MVAL (Maximization of Variance), aims to maximize the variance of predictions across different models trained on various subsets of the data, thereby identifying samples that are likely to be both informative and representative. By focusing on the variance of model outputs and utilizing retraining information matrices, MVAL provides a quantitative framework for selecting samples that are most beneficial for model training.

At its core, the variance maximization criterion operates under the principle that unlabeled data points significantly affecting the output variance of a model are likely to be more informative. These data points typically reside in regions of high uncertainty or ambiguity, where the model's predictions exhibit wide variation, indicating insufficient information for accurate classification. Such samples are crucial for refining the model's understanding of the underlying data distribution, thereby enhancing its generalization capabilities. MVAL captures this concept by measuring the variance in model predictions across different subsets of the training data, providing a direct quantification of the model's uncertainty concerning the unlabeled samples.

To implement MVAL, one initiates by selecting a subset of the unlabeled data and training multiple models, each initialized with slightly perturbed sets of parameters. This perturbation ensures that the models encompass a range of possible solutions, reflecting the inherent uncertainty in the training process. Subsequently, the predictions made by these models on the entire dataset are recorded, and the variance in these predictions is calculated for each unlabeled sample. High variance in predictions signals a higher degree of uncertainty and, consequently, greater informativeness of the sample.

Furthermore, MVAL integrates the concept of retraining information matrices to refine the selection process. These matrices encapsulate the impact of incorporating a particular sample into the training set on the model's performance. By constructing these matrices for different samples, MVAL can quantify how much each sample contributes to the model's learning process, allowing for a more nuanced assessment of informativeness beyond simple output variance. This additional layer of analysis aids in identifying not just the samples that increase output variance but also those that significantly influence the model's overall learning trajectory, ensuring a more balanced and representative selection of samples for labeling.

Logistic regression and support vector machines (SVMs) are commonly utilized in MVAL due to their effectiveness in handling high-dimensional data and providing interpretable results. For logistic regression, MVAL measures the variance in predicted probabilities for each sample, capturing the model's uncertainty regarding the class membership of the sample. Similarly, for SVMs, MVAL evaluates the variance in decision boundaries, reflecting the model's confidence in assigning samples to specific classes. These measures of variance provide a direct indication of the informativeness of each sample, guiding the selection process toward those samples that offer the greatest potential for improving model performance.

The use of logistic regression in MVAL capitalizes on its ability to generate probabilistic outputs, essential for quantifying uncertainty. Logistic regression predicts the probability of a sample belonging to a particular class, and the variance in these probabilities across different models trained on perturbed subsets reveals the model's uncertainty. High variance in predicted probabilities indicates a sample located in a region of the feature space where the model's predictions are highly sensitive to changes in the training data, suggesting that the sample is informative. Moreover, logistic regression's simplicity facilitates efficient computation of these variances, making it a practical choice for implementing MVAL.

Support vector machines (SVMs) offer another perspective on measuring informativeness through the variance in decision boundaries. SVMs aim to maximize the margin between different classes by identifying an optimal hyperplane that separates the data. Within MVAL, the variance in the position of this hyperplane across different models trained on perturbed subsets signifies the sensitivity of the decision boundary to the inclusion of specific samples. High variance in decision boundaries suggests that the sample in question is pivotal in shaping the hyperplane, implying that it is highly informative. SVMs' focus on maximizing margins aligns well with MVAL's goal of selecting samples that contribute to better generalization by refining the model's decision boundary.

Incorporating retraining information matrices into MVAL enhances the selection process by offering a more comprehensive view of each sample's impact on the model's learning. These matrices are generated by training multiple models on subsets of the data that exclude specific samples and then measuring the changes in model performance upon re-inclusion of these samples. The entries of these matrices reflect the contribution of each sample to the model's learning process, enabling MVAL to prioritize samples that hold the greatest potential for improving model performance. This approach ensures that the selection process is not solely driven by output variance but also considers the sample's influence on the model's overall learning trajectory, fostering a more balanced and representative selection of samples.

Empirical evaluations of MVAL have demonstrated its effectiveness in reducing the labeling effort required to achieve comparable or superior performance compared to fully supervised learning. On various benchmark datasets, including PASCAL VOC and MS COCO, MVAL has consistently outperformed traditional active learning methods in terms of reducing the amount of labeled data needed to reach target performance levels. These results underscore the utility of MVAL in scenarios where labeled data are scarce, making it a valuable tool for optimizing the annotation process in remote sensing image classification tasks.

Despite its advantages, MVAL also encounters certain challenges and limitations. Notably, the computational complexity associated with training multiple models and calculating variance across different subsets of the data can be substantial. This complexity can be mitigated through the use of approximations and optimizations, such as random subsampling of the training data and parallel processing of model training. Additionally, the effectiveness of MVAL may vary based on the specific characteristics of the dataset and the chosen model architecture. For instance, in high-resolution satellite imagery with complex and heterogeneous data distributions, careful tuning of MVAL's parameters might be necessary to achieve optimal performance.

In conclusion, the variance maximization criterion (MVAL) presents a powerful framework for active learning that focuses on identifying informative and representative samples through the lens of output variance and retraining information matrices. Its application with logistic regression and SVMs underscores the versatility and efficacy of MVAL in different contexts, positioning it as a valuable addition to the active learning toolkit. As the demand for efficient and effective annotation methods continues to rise in the field of remote sensing image classification, MVAL holds promise as a solution for optimizing the use of limited labeled data while maintaining high performance standards.

### 2.4 Least Disagree Metric

The least disagree metric (LDM) proposed in "Querying Easily Flip-flopped Samples for Deep Active Learning" introduces a novel approach to selecting samples for labeling in active learning settings. Unlike traditional active learning strategies that focus on uncertainty or diversity alone, LDM evaluates the smallest probability of disagreement in predicted labels to guide the selection of samples that are most informative for improving model performance. This metric is particularly useful in deep learning models where the decision boundaries can be highly non-linear and complex.

Building on the concept of variance maximization discussed in the previous section, LDM extends the idea of measuring uncertainty by focusing specifically on the stability of the model's predictions. The core idea behind LDM is to identify those instances where the model is least confident about its predictions, and yet, the smallest perturbation can cause significant changes in the predictions. This metric effectively captures the regions of high uncertainty where the model's decision boundaries are unstable, similar to how MVAL identifies samples that significantly affect the output variance across different models.

The evaluation of the LDM involves analyzing the model's predictions over a set of unlabeled samples. For each sample, the model computes the probability distribution over all possible classes. The LDM then calculates the probability of disagreement between these predicted probabilities, which essentially reflects the instability or volatility of the model’s decision boundary around that particular sample. Mathematically, this can be formalized as finding the minimum difference in predicted probabilities for any pair of classes for a given input sample. Formally, let \( p_i \) denote the predicted probability for class \( i \) for a given sample \( x \). Then, the LDM for sample \( x \) can be defined as:

\[
\text{LDM}(x) = \min_{i \neq j} |p_i(x) - p_j(x)|
\]

where \( i \) and \( j \) are different classes. A lower value of LDM indicates higher disagreement among the predicted probabilities, suggesting that the model is less confident about its classification for that sample. Conversely, a higher LDM value implies that the model is more certain about its predictions, and thus, such samples might not be as informative for improving the model.

One of the key advantages of LDM is its computational efficiency. Since it relies on evaluating the differences in predicted probabilities rather than requiring complex computations or simulations, it can be computed quickly even for large datasets. This efficiency makes LDM a practical choice for active learning scenarios where real-time or near-real-time decision-making is crucial. Furthermore, the simplicity of the LDM calculation allows it to be easily integrated into existing active learning pipelines, enhancing their performance without significantly increasing computational overhead.

Empirical evaluations of LDM demonstrate its effectiveness across various datasets. For instance, when applied to the CIFAR-10 and CIFAR-100 image classification benchmarks, LDM consistently outperforms other commonly used active learning metrics such as entropy and margin sampling. These improvements are attributed to LDM's ability to effectively capture the instability in the model's decision boundaries, leading to more targeted and effective selection of samples for labeling. In the context of remote sensing image classification, LDM has shown promising results in enhancing the accuracy of object detection tasks, especially in scenarios where the labeled data are sparse and the decision boundaries are complex.

Moreover, LDM's performance is not limited to image classification tasks alone. It has also been successfully applied to other domains such as natural language processing and wireless communications, further underscoring its versatility and broad applicability. For instance, in wireless communication scenarios, where deep learning models are employed for tasks such as mmWave beam selection, LDM has proven effective in reducing the labeling overhead while maintaining or even improving the accuracy of the models. This is particularly beneficial in wireless communication systems, where real-time decision-making is critical, and the availability of labeled data is often constrained by the high costs and complexities involved in manual labeling.

In summary, the least disagree metric (LDM) represents a significant advancement in the field of active learning, offering a computationally efficient and effective approach to sample selection. By focusing on the smallest probability of disagreement in predicted labels, LDM identifies those samples that are most likely to benefit from labeling efforts, thereby enhancing the overall performance of active learning models. As the field of remote sensing continues to evolve, with increasingly complex datasets and challenging tasks, LDM stands out as a promising tool for optimizing the annotation process and improving the efficiency of data utilization. This approach lays the groundwork for the subsequent discussion on Query-augmented Active Metric Learning (QAML), which further explores the benefits of considering inter-instance relationships in the active learning process.

### 2.5 Query-Augmented Active Metric Learning

Query-augmented Active Metric Learning (QAML) is an innovative approach designed to enhance clustering performance by actively querying informative instance pairs and incorporating unlabeled data into the learning process. Unlike traditional active learning methods that focus solely on selecting individual instances, QAML leverages the relationships between pairs of instances to refine the learned metric space, thereby improving clustering outcomes. Building on the advancements discussed in the previous section regarding the least disagree metric (LDM), QAML further advances the active learning paradigm by considering the inter-instance relationships in remote sensing image classification tasks.

At the heart of QAML lies the concept of query-augmented active learning, which integrates the principles of active learning with metric learning to address the challenges posed by class-imbalanced datasets. The process begins by initializing a metric learning model, typically based on a graph-based framework or a distance-based similarity measure, capable of capturing the intrinsic structure of the data. Similar to the LDM approach, QAML aims to identify samples that are most informative for improving model performance, but instead of focusing on individual sample uncertainties, it emphasizes the relationships between pairs of instances.

A key aspect of QAML is its ability to dynamically select informative instance pairs for labeling. This selection process is guided by a set of criteria that prioritize pairs whose labels would yield the most substantial improvement in the learned metric. One such criterion involves measuring the disagreement among the nearest neighbors of each instance pair. If the nearest neighbors of two instances belong to different clusters, labeling the pair is likely to reduce the overlap between these clusters, thereby enhancing the separation and improving clustering accuracy. Another criterion focuses on the proximity of instances to decision boundaries. By querying pairs close to these boundaries, QAML ensures that the learned metric captures the subtle differences between classes, leading to more precise cluster definitions.

Once informative instance pairs are identified, QAML proceeds to incorporate the labeled data into the learning process through a sequential update mechanism. This mechanism iteratively refines the learned metric by adjusting the parameters based on the newly labeled data. Each iteration aims to minimize the discrepancy between the predicted and true pairwise distances, thereby fine-tuning the metric to better reflect the underlying structure of the dataset. This iterative refinement process is crucial for gradually improving the clustering performance, especially in scenarios where the initial metric might be biased due to class imbalance or other factors.

To further enhance the robustness of the learned metric, QAML incorporates an adaptive penalty for irrelevant features. This penalty is designed to penalize the contribution of features that do not significantly contribute to the discrimination between classes. By suppressing the influence of such features, QAML ensures that the learned metric focuses on the most informative dimensions of the data, leading to more reliable clustering outcomes. The adaptive nature of the penalty allows it to adjust according to the evolving structure of the dataset, ensuring that the metric remains sensitive to changes and variations in the data distribution.

The effectiveness of QAML in handling class imbalance is a notable strength of the approach. Class imbalance, a common issue in remote sensing image classification tasks, can severely impact the performance of clustering algorithms. Traditional clustering methods often struggle to identify minority classes due to the overwhelming presence of majority classes. By actively querying informative instance pairs, QAML ensures that both majority and minority classes receive adequate attention during the clustering process. This balanced representation of classes helps to mitigate the effects of class imbalance, leading to improved clustering accuracy and a more equitable representation of all classes in the final clusters.

Furthermore, the incorporation of unlabeled data into the learning process through query-augmented active learning allows QAML to leverage the vast amounts of unlabeled data commonly available in remote sensing applications. Unlabeled data can provide valuable information about the underlying structure of the dataset, which can be harnessed to improve the learned metric. By utilizing this additional information, QAML can achieve better generalization and robustness, making it particularly well-suited for scenarios where labeled data are scarce or costly to obtain.

In summary, Query-augmented Active Metric Learning represents a significant advancement in the field of active learning, offering a powerful tool for enhancing clustering performance in remote sensing image classification tasks. Through the dynamic selection of informative instance pairs and the sequential update of the learned metric, QAML addresses the challenges of class imbalance and limited labeled data, leading to more accurate and robust clustering outcomes. The incorporation of adaptive penalties for irrelevant features further enhances the robustness of the learned metric, ensuring that it remains sensitive to the nuances of the data distribution. As remote sensing applications continue to expand and the volume of data continues to grow, QAML holds great promise for improving the efficiency and effectiveness of clustering algorithms in this domain.

This subsection bridges the discussion from the least disagree metric (LDM) by highlighting the importance of inter-instance relationships and sets the stage for the subsequent exploration of CLARIFIER, emphasizing the holistic approach to human-machine collaboration in the labeling process.

### 2.6 Comprehensive Human Interaction Framework

The interactive learning framework CLARIFIER, introduced in "Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification," represents a significant advancement in the field of active learning for remote sensing image classification. Building on the advancements highlighted in the previous section regarding Query-augmented Active Metric Learning (QAML), CLARIFIER extends the scope of active learning by integrating a tripartite approach that encompasses auto-labeling, human correction, and human verification, particularly tailored for datasets with numerous classes. This framework addresses the limitations of traditional active learning strategies, such as reliance on heuristic measures of informativeness and the inability to effectively handle imbalanced datasets, thereby enhancing the overall efficiency and effectiveness of the labeling process.

At the core of CLARIFIER lies the concept of auto-labeling, which automates the initial labeling of unlabeled samples. Auto-labeling is achieved through a combination of self-supervised and semi-supervised learning techniques, enabling the generation of high-quality pseudo-labels without the need for extensive manual labeling. Leveraging pre-trained models to generate initial labels for unlabeled samples, CLARIFIER subsequently refines these labels through iterations of human correction and verification. This approach significantly reduces the burden of manual labeling, especially in large-scale remote sensing datasets where labeling efforts can be prohibitively expensive and time-consuming.

Human correction plays a critical role in ensuring the accuracy and reliability of the final labels within CLARIFIER. Unlike traditional active learning approaches that focus solely on the selection of informative samples, CLARIFIER incorporates a feedback loop where human annotators correct errors in the pseudo-labels generated by the auto-labeling phase. This ensures that the labeling process remains grounded in human expertise, enhancing the overall accuracy of the dataset. The human correction phase is particularly beneficial in datasets with numerous classes, where subtle differences between classes can lead to misclassification by automated systems.

CLARIFIER further strengthens its approach by integrating human verification as a safeguard against potential biases and errors introduced during the auto-labeling and human correction phases. Human verification involves a systematic review of the corrected labels by human annotators to ensure consistency and correctness. This step is crucial for maintaining the quality of the final dataset, which is essential for training robust machine learning models. By involving humans in the verification process, CLARIFIER ensures transparency and accountability in the labeling process, fostering trust in the final dataset.

By combining auto-labeling, human correction, and human verification, CLARIFIER streamlines the labeling process in remote sensing image classification, addressing the challenges of class imbalance and high labeling costs. Unlike traditional active learning approaches that often struggle with these issues, CLARIFIER leverages the strengths of both automated and human labeling to create a balanced and representative dataset. This is particularly important in remote sensing applications, where datasets can be highly imbalanced and the labeling process can be resource-intensive.

Moreover, CLARIFIER’s design allows for flexibility and scalability, making it adaptable to a wide range of remote sensing tasks. The framework can be easily customized to accommodate different types of remote sensing data, such as high-resolution satellite images, aerial photographs, and LiDAR data. Additionally, CLARIFIER’s modular structure facilitates the incorporation of new labeling strategies and techniques as they emerge, ensuring that the framework remains up-to-date and relevant in the rapidly evolving field of remote sensing.

CLARIFIER excels in handling datasets with numerous classes, a common challenge in traditional active learning approaches. By providing a structured and systematic approach to multi-class datasets, CLARIFIER enables efficient and accurate labeling even in scenarios with a large number of classes. This is particularly beneficial in applications such as semantic segmentation of satellite images, where distinguishing between numerous classes is essential for accurate interpretation of the data.

Another key advantage of CLARIFIER is its focus on reducing the labeling overhead associated with remote sensing tasks. By automating parts of the labeling process and incorporating human feedback systematically, CLARIFIER minimizes the need for extensive manual labeling. This is particularly advantageous in large-scale remote sensing projects, where the volume of data can make manual labeling infeasible. Through its efficient use of human resources, CLARIFIER bridges the gap between the availability of unlabeled data and the demand for labeled data in machine learning tasks.

Theoretical insights from CLARIFIER highlight the importance of balancing automation and human involvement in the labeling process. By orchestrating the interactions between automated systems and human annotators, CLARIFIER demonstrates how the strengths of both can be combined to achieve superior labeling outcomes. This balance is particularly relevant in the context of remote sensing, where the complexity of the data requires a nuanced approach to labeling.

In summary, CLARIFIER represents a significant advancement in the field of active learning for remote sensing image classification. By optimizing human interaction through auto-labeling, human correction, and human verification, CLARIFIER addresses key challenges associated with labeling large and complex datasets. Its innovative design and flexible architecture position it as a valuable tool for researchers and practitioners in remote sensing, offering a promising solution to the ongoing challenge of efficient and accurate labeling in this field. As remote sensing continues to evolve and expand, frameworks like CLARIFIER will become increasingly vital in supporting the development of robust and reliable machine learning models.

### 2.7 General Fusion of Representativeness and Informativeness

The general active learning framework explored in "Exploring Representativeness and Informativeness for Active Learning" offers a comprehensive approach to combining the concepts of representativeness and informativeness in the selection of samples for labeling. Unlike many existing methods that focus on either aspect alone or make stringent assumptions about data structures, this framework integrates these two dimensions seamlessly, leading to a more holistic and adaptable active learning strategy. This integration is particularly noteworthy given the framework's validation through empirical evaluations on multiple benchmark datasets, demonstrating superior efficiency and accuracy compared to conventional approaches.

At the core of this framework is the recognition that representativeness pertains to capturing the essential characteristics of the underlying data distribution, while informativeness relates to the value of a sample in reducing uncertainty about the model parameters. By optimizing for both criteria simultaneously, the framework ensures that selected samples not only reflect the diversity of the dataset but also significantly contribute to refining the model.

A key innovation of this framework is its flexibility in handling various data structures without requiring strict assumptions about the data distribution. This adaptability is achieved through an iterative selection process that dynamically adjusts its criteria based on the evolving state of the model. During each iteration, the framework evaluates both the representativeness and informativeness of each unlabeled sample, assigning scores that guide the prioritization of samples for labeling. This dynamic adjustment ensures the framework remains effective throughout the entire training cycle.

To quantify these aspects, the framework employs a series of theoretically grounded and computationally feasible metrics. Representativeness is measured using a dissimilarity metric based on the proximity of samples in a latent space derived through techniques such as PCA or t-SNE. Informativeness is assessed through the gradient of the loss function with respect to the model parameters, which quantifies the contribution of each sample to reducing model uncertainty.

The integration of these metrics into the active learning process is facilitated by a sophisticated scoring mechanism that balances the trade-offs between representativeness and informativeness. This ensures the labeled dataset is well-rounded and informative. Furthermore, the framework includes provisions for handling imbalanced datasets and class overlaps, making it particularly useful in complex scenarios.

Empirical evaluations demonstrate the framework's effectiveness across various datasets and tasks. On the MNIST digit recognition task, it surpasses baseline methods focusing solely on informativeness or representativeness, achieving higher classification accuracies with fewer labeled samples. Similarly, on the CIFAR-10 image classification task, the framework improves both the speed of convergence and final accuracy, showcasing its efficiency in guiding the learning process towards better models. Its application to the COCO object detection task further highlights its effectiveness in structured prediction tasks, where it reduces labeling effort while enhancing precision and recall.

These findings underscore the framework’s potential to significantly enhance the efficiency and effectiveness of active learning in remote sensing image classification, where labeled data are often scarce and costly to acquire. This aligns with the broader goals of optimizing the annotation process and leveraging the strengths of both automated and human labeling processes, as exemplified by the CLARIFIER framework discussed previously.

In summary, the fusion of representativeness and informativeness in the active learning framework presented in "Exploring Representativeness and Informativeness for Active Learning" marks a notable advancement. By addressing the limitations of existing approaches and providing a flexible solution applicable to various data types, this framework offers a valuable tool for researchers and practitioners in supervised learning tasks, including remote sensing image classification.

### 2.8 Uncertainty-Based Metrics for Deep Object Detection

Uncertainty-based metrics play a pivotal role in guiding the selection of unlabeled samples for labeling in deep object detection, particularly in contexts characterized by class imbalances. These metrics aim to identify the most informative samples that can enhance the model's performance when included in the training dataset. The concept of uncertainty has been extensively explored in various active learning frameworks for deep object detection, as evidenced in "Active Learning for Deep Object Detection."

By quantifying the model's confidence in its predictions, these metrics help identify samples that pose the greatest challenge to the model, thus offering substantial opportunities for improving model accuracy. At the core of uncertainty-based metrics is the identification of samples located near the decision boundary of the classifier. These samples are the most informative because they reside in regions where the model is least certain, often due to overlapping features of different classes or insufficient training data for specific categories. In scenarios with class imbalances, the minority class frequently exhibits higher levels of uncertainty, making it challenging for the model to distinguish its instances from those of the majority class. This highlights the importance of uncertainty-based metrics in ensuring that the active learning process effectively targets these challenging cases.

One common approach to quantifying uncertainty involves estimating both aleatoric and epistemic uncertainties. Aleatoric uncertainty captures the inherent randomness in the data, reflecting variations in input features that affect the output prediction. Epistemic uncertainty, conversely, is attributed to the model's parameters and can be reduced with additional training data. Differentiating between these two types of uncertainty allows the model to more accurately identify samples contributing to both forms of uncertainty, thereby informing a more strategic selection process. For example, samples with high epistemic uncertainty indicate regions of the input space where the model's predictions are highly sensitive to the parameter configuration, signaling a need for more data to stabilize these predictions.

Another key aspect of uncertainty-based metrics is their capability to manage class imbalances effectively. In deep object detection tasks, class imbalances can severely impede the performance of conventional active learning strategies that rely solely on heuristic measures of informativeness. If a model is predominantly exposed to samples from the majority class during training, it may become biased towards these classes, leading to poor performance on the minority class. Uncertainty-based metrics address this issue by prioritizing the selection of minority class samples, ensuring a more balanced representation of all classes in the training dataset. This balanced representation facilitates fine-tuning the model to recognize patterns specific to the minority class, ultimately improving overall performance.

Moreover, uncertainty-based metrics provide a flexible framework for integrating prior knowledge and domain-specific insights into the active learning process. In remote sensing, where objects of interest often exhibit subtle variations in appearance, these metrics can be adapted to reflect these complexities. By tuning the parameters of these metrics to emphasize features indicative of class membership, the model can more accurately identify and label samples that represent the true variability within each class. This adaptability makes uncertainty-based metrics particularly well-suited for handling the diverse and nuanced challenges encountered in remote sensing applications.

Beyond their effectiveness in managing class imbalances, uncertainty-based metrics have been shown to significantly reduce the amount of labeled data needed to achieve target performance levels. This advantage is particularly beneficial in resource-constrained environments where acquiring large volumes of labeled data is prohibitively expensive or time-consuming. By focusing on the most informative samples, these metrics enable the model to learn more efficiently, thereby accelerating the training process and improving overall efficiency. For instance, in studies on change detection in satellite imagery, researchers found that active learning strategies based on uncertainty metrics could achieve comparable performance to models trained on large, pre-annotated datasets but with approximately 99% fewer labeled samples [26].

Additionally, uncertainty-based metrics can be seamlessly integrated with advanced techniques like self-supervised learning and reinforcement learning to enhance their effectiveness. Self-supervised learning can be used to pre-train models on large, unlabeled datasets, providing a rich feature representation that informs uncertainty estimates in active learning. Reinforcement learning can dynamically adjust the reward functions governing the active learning process, ensuring that the selection of unlabeled samples aligns with the evolving needs of the model as it learns from new data. These integrative approaches not only bolster the robustness of uncertainty-based metrics but also pave the way for more sophisticated active learning frameworks capable of addressing the complexities of real-world remote sensing tasks.

However, uncertainty-based metrics face several challenges. Estimating uncertainty in high-dimensional feature spaces typical of deep object detection models can be computationally complex. Moreover, the effectiveness of these metrics can be influenced by the quality and diversity of the initial labeled dataset; poor initial selections can propagate errors throughout the active learning process. Ongoing research focuses on developing more efficient and robust uncertainty estimation techniques and exploring ways to leverage auxiliary information, such as self-supervised pre-training, to enhance the informativeness of the initial labeled dataset.

In summary, uncertainty-based metrics represent a powerful tool for enhancing the efficiency and effectiveness of active learning in deep object detection tasks, especially in scenarios marked by class imbalances. By identifying and prioritizing the most informative samples, these metrics enable the model to learn more efficiently and achieve target performance levels with minimal labeled data. As remote sensing applications continue to evolve, the integration of uncertainty-based metrics with advanced techniques such as self-supervised learning and reinforcement learning holds great promise for addressing the diverse and complex challenges encountered in this domain.

### 2.9 Learning Dynamics for Sample Selection

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Learning dynamics, a relatively novel concept in the realm of deep learning, offers an innovative perspective for assessing the informativeness of samples in active learning. Specifically, the work described in "When Deep Learners Change Their Mind: Learning Dynamics for Active Learning" introduces a groundbreaking method that leverages the dynamic changes in label assignments during training as a criterion for sample selection in active learning. This approach addresses the limitations of traditional active learning metrics, such as overconfidence in model predictions, which can lead to suboptimal selections of informative samples.

In traditional active learning settings, the informativeness of a sample is often assessed based on the confidence or uncertainty of predictions generated by a pre-trained model. However, as highlighted by the authors, "neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness." [31] This inherent limitation underscores the need for alternative approaches to evaluate sample informativeness, thereby enhancing the efficiency and effectiveness of active learning.

The central idea of the proposed method revolves around the concept of label-dispersion, a metric designed to quantify the variability in label assignments of unlabeled samples throughout the training process. By monitoring the consistency of label assignments for each sample across epochs, the method captures the underlying learning dynamics and utilizes this information to identify samples that exhibit significant changes in their assigned labels. These samples are deemed more informative as they indicate areas where the model is less certain and requires additional guidance for accurate learning. Label-dispersion serves as a reliable predictor of sample informativeness, particularly in scenarios where standard uncertainty measures may fail due to the overconfidence issue of neural networks.

To operationalize the label-dispersion measure, the method involves a series of steps that systematically evaluate the consistency of label assignments for unlabeled samples. Initially, the model is trained on a subset of labeled data, and during this phase, the network generates label predictions for the entire pool of unlabeled samples. As the training progresses, the label predictions for each sample are recorded at regular intervals. Subsequently, the label-dispersion for each sample is computed as a function of the frequency and magnitude of changes in label assignments observed over time. Mathematically, this can be represented as the variance of label assignments across epochs, providing a quantitative measure of the network's uncertainty regarding the true label of each sample.

The effectiveness of the label-dispersion metric lies in its ability to discern samples that are inherently difficult for the model to classify correctly. These challenging samples often belong to class boundaries or exhibit ambiguous features that contribute to the network's indecision, making them prime candidates for labeling. By prioritizing the annotation of these samples, the active learning process can rapidly refine the model's understanding of these challenging cases, leading to improved overall performance. Furthermore, samples prone to misclassification due to their similarity to multiple classes typically exhibit high levels of label-dispersion as the network oscillates between assigning different labels. Consequently, these samples serve as valuable targets for labeling efforts, as their annotation helps to clarify these ambiguities and strengthen the model's discriminative capabilities.

Empirical evaluations conducted on benchmark datasets demonstrate the superior performance of the label-dispersion-based active learning approach. The authors report that an active learning algorithm utilizing label-dispersion achieves excellent results on two widely recognized datasets. The enhanced sample selection facilitated by the label-dispersion metric leads to a marked improvement in the model's performance, underscoring the utility of this novel approach in active learning for object detection tasks.

Importantly, the label-dispersion method is not confined to specific architectures or dataset complexities. It capitalizes on the intrinsic properties of learning dynamics to identify informative samples, offering a versatile solution applicable across various active learning scenarios. This adaptability makes the method particularly valuable for remote sensing applications, where datasets often exhibit high variability and require robust strategies for efficient sample selection.

In conclusion, the introduction of learning dynamics-based active learning represents a significant advancement in the field, particularly for object detection tasks. By leveraging the temporal behavior of neural networks during training, the label-dispersion measure provides a principled approach to assess sample informativeness. This method not only addresses the limitations of conventional uncertainty measures but also offers a powerful tool for enhancing the efficiency and effectiveness of active learning algorithms. As active learning continues to play an increasingly prominent role in remote sensing image classification, the integration of learning dynamics-based metrics holds considerable promise for future research and practical applications.
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### 2.10 Probabilistic Modeling for Deep Object Detection

To conclude the discussion on metrics and evaluation criteria for active learning, we explore an innovative approach for deep object detection based on probabilistic modeling, as presented in "Active Learning for Deep Object Detection via Probabilistic Modeling." This approach integrates probabilistic models to estimate both aleatoric and epistemic uncertainties, offering a nuanced framework for selecting informative samples for labeling. Aleatoric uncertainty, often stemming from inherent randomness in the data, captures variability due to noise or measurement errors. Epistemic uncertainty, however, originates from model limitations and can be reduced through additional data or model improvements.

Building upon the examination of label-dispersion in the previous section, probabilistic modeling provides an alternative and complementary perspective for assessing sample informativeness. Whereas label-dispersion focuses on the temporal dynamics of label assignments, probabilistic models offer a statistical framework that accounts for both intrinsic data variability and model uncertainty. This dual approach enhances the robustness of active learning algorithms by ensuring that the selected samples are not only challenging for the model but also representative of the underlying data distribution.

High-resolution satellite imagery in remote sensing often contains significant variability and complexity, making probabilistic models particularly useful. Aleatoric uncertainty, in this context, highlights regions with high environmental variability or noise, which are crucial for enriching the dataset with diverse examples. Epistemic uncertainty, conversely, points to areas where the model's current architecture or training data are insufficient, indicating a need for further refinement.

Estimating uncertainties in probabilistic models can be achieved through methods such as Monte Carlo dropout, ensembling, and variational inference. Monte Carlo dropout simulates stochastic predictions by dropping out units during the forward pass, thus capturing model variability. Ensembling aggregates predictions from multiple models to quantify uncertainty, while variational inference approximates the posterior distribution over model parameters to estimate uncertainties.

By integrating probabilistic modeling into the active learning process, the algorithm can balance the trade-off between informativeness and representativeness. Informativeness pertains to the potential of a sample to reduce model uncertainty, whereas representativeness ensures that selected samples adequately cover the feature space. This dual consideration helps prevent overfitting and ensures better generalization to unseen data. For example, in remote sensing, identifying areas with high epistemic uncertainty can help uncover subtle variations in land cover types that were previously poorly understood by the model.

Furthermore, probabilistic modeling aids in addressing the challenge of imbalanced datasets, a common issue in remote sensing where certain classes may be underrepresented. By prioritizing the labeling of samples from underrepresented classes based on their estimated uncertainties, the algorithm can mitigate imbalance issues more effectively than traditional methods that ignore uncertainties.

Despite the advantages, probabilistic modeling faces challenges, notably increased computational demands for uncertainty estimation and the complexity of interpreting uncertainties accurately. Nonetheless, the benefits of improved uncertainty quantification and balanced sample selection make probabilistic modeling a valuable tool for enhancing active learning in remote sensing.

In summary, probabilistic modeling offers a promising framework for active learning in deep object detection, improving sample selection efficiency and effectiveness. By explicitly considering both aleatoric and epistemic uncertainties, the approach identifies the most informative samples for labeling, contributing to enhanced model performance on remote sensing tasks.

## 3 Active Learning in High-Resolution Satellite Images

### 3.1 Overview of High-Resolution Satellite Images for Aircraft Detection

High-resolution satellite images offer unparalleled detail and precision, enabling researchers and analysts to detect and monitor various objects and phenomena on Earth's surface. These images, typically captured by satellites orbiting at altitudes ranging from hundreds to thousands of kilometers, boast spatial resolutions ranging from sub-meter to several meters per pixel. This level of detail is invaluable for applications such as environmental monitoring, urban planning, and military surveillance, particularly in the context of aircraft detection. 

One of the primary advantages of high-resolution satellite images for aircraft detection is the fine-grained information they provide. This detail allows for accurate identification and characterization of aircraft, which are small objects when viewed from space. However, this benefit comes with significant challenges. Firstly, the enormous volume of data generated by modern satellites poses substantial analytical difficulties. A single satellite pass can produce terabytes of data, necessitating sophisticated data processing and storage solutions. Additionally, the vast geographic coverage provided by these images complicates the task of detecting and distinguishing aircraft from similar-sized objects like buildings or vehicles, requiring advanced image processing techniques.

Another challenge is the variability in atmospheric conditions, such as cloud cover, haze, and sunlight, which can significantly affect image clarity and reliability. Clouds can obscure the ground surface, while varying sunlight angles can create shadows and highlights that complicate the detection process. To address these issues, researchers often use advanced preprocessing techniques, including atmospheric correction and radiometric normalization, to enhance image quality before applying detection algorithms.

Furthermore, the complexity of the background in high-resolution satellite images presents another significant hurdle. Natural landscapes and urban environments contain numerous features that can interfere with detection algorithms. Trees, buildings, roads, and other structures can mimic the appearance of aircraft, leading to false positives. Robust feature extraction and classification algorithms are therefore essential for discerning subtle differences between similar-looking objects.

The dynamic nature of satellite-captured scenes adds another layer of complexity. High-resolution satellite images often include moving objects, such as aircraft, against a backdrop of stationary features. Detecting moving targets in this dynamic environment requires algorithms that account for motion and temporal changes, further complicating the task. Varying speeds and trajectories of aircraft introduce additional challenges, as optimal detection parameters must adapt quickly.

Despite these challenges, high-resolution satellite images offer significant opportunities for aircraft detection through active learning techniques. Active learning, which focuses on selectively labeling a subset of data to guide the training process, can be especially beneficial in scenarios where labeled data are scarce or expensive to obtain. By targeting areas of the image likely to contain aircraft, active learning ensures that the labeled data are representative and informative. This reduces overall annotation effort while maintaining or even improving detection algorithm performance.

In the context of aircraft detection, active learning addresses several key issues. The rarity and variability of aircraft in satellite images mean that conventional random sampling methods may not yield sufficient labeled data for effective training. Active learning can help by focusing on areas likely to contain aircraft. Additionally, the high cost and resource intensity of manual labeling can be mitigated through active learning, as fewer annotations are required to achieve comparable or superior performance. Lastly, the evolving nature of satellite imagery, with changing weather patterns and seasons, can be better managed through active learning, as the algorithm can adapt to new conditions over time.

Several studies have explored the application of active learning in high-resolution satellite images for aircraft detection. For example, the study titled 'Stopping Criterion for Active Learning Based on Error Stability' provides a stopping criterion that is particularly useful for determining when additional annotations are no longer necessary, thus saving time and resources. This criterion ensures that the generalization error is minimized relative to the annotation cost, crucial in scenarios with limited labeled data.

Moreover, advanced techniques like meta-learning and reinforcement learning can further enhance active learning's effectiveness. Meta-learning, as described in 'Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning,' enables the development of adaptive active learning policies that can be transferred across different datasets and tasks. This flexibility is valuable in remote sensing applications, where object types and environmental conditions vary widely. Reinforcement learning can dynamically adjust the active learning strategy based on environmental feedback, ensuring that the most informative samples are selected for labeling.

In summary, while high-resolution satellite images offer immense potential for aircraft detection, the process is complicated by vast data volumes, variable atmospheric conditions, and complex backgrounds. Active learning techniques can play a pivotal role in overcoming these challenges by efficiently guiding the annotation process and enhancing the accuracy and robustness of detection algorithms. As the field evolves, the integration of advanced techniques such as self-supervised learning and reinforcement learning holds great promise for improving active learning's effectiveness in remote sensing applications.

### 3.2 Traditional Active Learning Approaches in Aircraft Detection

Traditional active learning approaches have been pivotal in addressing the challenges associated with labeling high-resolution satellite images for aircraft detection. These methodologies aim to select the most informative images for manual annotation, thereby minimizing the overall labeling effort and cost. The traditional approaches generally fall into two categories: uncertainty sampling and committee-based methods. Each category offers distinct advantages and faces specific limitations when applied to the intricate context of aircraft detection.

Uncertainty sampling is one of the foundational techniques in active learning, wherein the algorithm identifies samples that the current model finds most ambiguous. By focusing on these uncertain instances, the model can learn more effectively from subsequent annotations. In the realm of aircraft detection, uncertainty sampling has been widely adopted to prioritize images where the detection model is least confident about the presence of aircraft. This approach ensures that the model receives additional training data in regions where it is currently performing poorly, leading to enhanced detection accuracy over time.

One of the key strengths of uncertainty sampling lies in its simplicity and ease of implementation. The approach relies on straightforward heuristics to measure uncertainty, such as entropy or margin-based scores, which can be easily calculated using existing deep learning frameworks. Moreover, uncertainty sampling is computationally efficient since it does not require extensive retraining cycles to generate candidate samples for labeling. Despite these advantages, uncertainty sampling also has notable limitations. The technique may fail to identify samples that are critical for model improvement but do not necessarily appear uncertain according to predefined thresholds. Additionally, the effectiveness of uncertainty sampling can diminish as the model becomes increasingly confident in its predictions, potentially leading to suboptimal selection of samples for annotation.

Committee-based methods involve using multiple classifiers or models to determine which samples are most informative. The underlying premise is that disagreements among committee members indicate a higher level of ambiguity, making such samples ideal candidates for labeling. In aircraft detection, committee-based approaches have been employed to leverage the diverse perspectives of multiple models in selecting images for annotation. This strategy ensures that the final dataset is enriched with a wide variety of samples, thereby improving the model’s robustness against different environmental conditions and aircraft types.

The primary strength of committee-based methods lies in their ability to capture a broader spectrum of information compared to uncertainty sampling. By integrating multiple viewpoints, committee-based approaches can identify a more comprehensive set of samples that span different contexts and variations in the input data. Furthermore, these methods can help mitigate the issue of overfitting to specific patterns by ensuring that the model is exposed to a diverse range of examples during training. However, committee-based methods are not without their drawbacks. They tend to be more computationally intensive than uncertainty sampling, as they require maintaining and updating multiple models simultaneously. Additionally, the effectiveness of committee-based methods heavily depends on the quality and diversity of the constituent models, which can be challenging to achieve in practice.

In addition to uncertainty sampling and committee-based methods, hybrid clustering active learning represents another promising approach for aircraft detection in high-resolution satellite images. This methodology integrates clustering algorithms with active learning to identify informative samples. Specifically, clustering is utilized to group similar images together, allowing the active learning algorithm to focus on diverse clusters rather than individual samples. By selecting representative images from each cluster, the model can benefit from a broader distribution of data, which is particularly beneficial in scenarios where certain types of aircraft or environmental conditions are underrepresented.

The strength of hybrid clustering active learning lies in its ability to address data imbalance issues that are common in remote sensing datasets. Clustering helps to ensure that the selected samples cover a wide range of variations within the dataset, thereby improving the model’s generalization capabilities. Moreover, this approach can be adapted to accommodate different levels of granularity in clustering, allowing for flexible control over the diversity of selected samples. However, hybrid clustering active learning also presents challenges. The quality of clustering results can significantly impact the effectiveness of the active learning process, and choosing appropriate clustering parameters requires careful consideration. Additionally, the computational complexity of clustering algorithms can be substantial, especially when dealing with large datasets, which may limit the scalability of this approach in some settings.

Another innovative approach that has gained attention in the context of aircraft detection is adversarial virtual exemplar learning. This technique leverages adversarial training to generate virtual exemplars that mimic the characteristics of real-world samples. By using these synthetic samples to inform the active learning process, the model can learn from a richer and more diverse set of examples, even when the available labeled data is limited. Adversarial virtual exemplar learning has shown promise in improving the robustness and accuracy of detection models, particularly in scenarios where real-world data is scarce or difficult to obtain.

The key advantage of adversarial virtual exemplar learning is its ability to augment the training dataset without requiring additional real-world annotations. This is particularly valuable in the context of aircraft detection, where labeling efforts can be prohibitively expensive due to the need for specialized expertise. By generating virtual samples that closely resemble real-world conditions, the model can better learn to detect aircraft under various environmental and operational conditions. However, this approach also has its limitations. The quality of the virtual samples heavily depends on the effectiveness of the adversarial training process, which can be sensitive to hyperparameters and architectural choices. Additionally, while adversarial virtual exemplar learning can augment the training dataset, it may not fully replicate the variability present in real-world scenarios, potentially limiting its generalizability.

Finally, mixed uncertainty sampling with class distribution balancing represents a refined approach that combines multiple uncertainty measures with techniques for balancing class distributions. This method aims to address the challenge of imbalanced datasets, which is a common issue in remote sensing applications. By integrating uncertainty sampling with strategies for balancing class distributions, the approach seeks to ensure that the model receives a balanced exposure to different classes of aircraft, thereby improving overall detection performance.

The strength of mixed uncertainty sampling with class distribution balancing lies in its comprehensive approach to addressing both uncertainty and data imbalance. By considering multiple dimensions of informativeness and distribution, the method can provide a more holistic view of the data, leading to more informed sample selection. This is particularly beneficial in aircraft detection, where different types of aircraft may have varying levels of visibility and relevance. However, this approach also introduces additional complexity in terms of parameter tuning and implementation. Ensuring that the various components of the approach are well-calibrated can be challenging, and the effectiveness of the method may vary depending on the specific characteristics of the dataset.

In summary, traditional active learning approaches have made significant contributions to improving aircraft detection in high-resolution satellite images. Each approach—uncertainty sampling, committee-based methods, hybrid clustering active learning, adversarial virtual exemplar learning, and mixed uncertainty sampling with class distribution balancing—offers unique strengths and faces distinct challenges. While uncertainty sampling provides a simple yet effective mechanism for identifying informative samples, committee-based methods enhance this process by integrating diverse perspectives. Hybrid clustering active learning addresses data imbalance issues, adversarial virtual exemplar learning generates synthetic samples to augment training, and mixed uncertainty sampling with class distribution balancing ensures a balanced exposure to different classes. Despite their individual merits, these approaches also face limitations such as computational complexity, sensitivity to parameter choices, and challenges in replicating real-world variability. Future research should continue to explore innovative ways to integrate these methods, potentially leading to more robust and efficient active learning strategies for aircraft detection.

### 3.3 Emerging Techniques and Methodologies

Emerging techniques and methodologies in the field of active learning for high-resolution satellite image classification, particularly for aircraft detection, have shown promise in addressing some of the key challenges associated with this domain. One notable approach is the integration of hybrid clustering active learning, which seeks to optimize the selection of informative samples by combining clustering techniques with active learning strategies. This method aims to reduce redundancy and enhance the diversity of selected samples, thereby improving the overall performance of the classifier. For instance, in a study on aircraft detection in satellite imagery [13], the authors propose a hybrid clustering active learning method that leverages both diversity and uncertainty-based approaches to select the most relevant samples for labeling. By integrating these two strategies, the method not only reduces the amount of data required for labeling but also enhances the performance of the detector. The hybrid clustering method is particularly beneficial in scenarios where labeled data are scarce and the labeling process is costly, as it ensures that the selected samples are both representative and informative, thereby accelerating the learning process.

Adversarial virtual exemplar learning (AVE) represents another innovative approach that introduces an additional layer of complexity by generating adversarial examples to refine the selection process. Inspired by the principles of adversarial training, AVE enhances the robustness of the model by exposing it to a wider range of challenging examples. This methodology creates virtual exemplars that are both representative of the data distribution and challenging for the model to classify correctly. Consequently, the model is compelled to learn more discriminative features, improving its ability to detect aircraft in satellite imagery. AVE has demonstrated particular efficacy in handling the variability and complexity of real-world satellite images, making it a valuable addition to the array of active learning techniques.

Mixed uncertainty sampling with class distribution balancing (MUS-CDB) is a cutting-edge technique that has gained traction in the context of aircraft detection in satellite images. Unlike traditional active learning methods that focus solely on informativeness or representativeness, MUS-CDB integrates both aspects and incorporates a class distribution balancing criterion. This approach is especially advantageous in scenarios characterized by long-tailed class distributions and densely packed small objects, typical in aerial imagery. By considering both object-level and image-level informativeness, MUS-CDB avoids redundant querying and ensures a balanced representation of all classes in the training set. Furthermore, the inclusion of class distribution balancing helps mitigate the effects of class imbalance, a common issue in satellite imagery datasets. Experimental results on benchmarks like DOTA-v1.0 and DOTA-v2.0 have shown that MUS-CDB can significantly reduce labeling costs while maintaining high performance levels. Authors of [12] report that their method can save up to 75% of the labeling cost while achieving comparable performance to other active learning methods in terms of mean average precision (mAP).

Each of these emerging techniques offers distinct advantages in addressing the challenges inherent to aircraft detection in high-resolution satellite images. Hybrid clustering active learning emphasizes the importance of both diversity and informativeness, ensuring that the labeled dataset is both comprehensive and representative. Adversarial virtual exemplar learning pushes the boundaries of active learning by leveraging adversarial training to enhance model robustness and discriminative power. Meanwhile, mixed uncertainty sampling with class distribution balancing provides a holistic approach to sample selection, balancing informativeness with representativeness and addressing the issue of class imbalance. Together, these methodologies mark a significant advancement in the application of active learning to the complex and demanding domain of high-resolution satellite image classification. As research continues to evolve, these techniques are likely to play a pivotal role in improving the efficiency and effectiveness of active learning strategies in remote sensing applications.

### 3.4 Integration of Self-Supervised Learning and Reinforcement Learning

Integrating self-supervised learning (SSL) and reinforcement learning (RL) within active learning frameworks has emerged as a promising approach for enhancing the performance and addressing specific challenges in high-resolution satellite image classification, particularly for tasks such as aircraft detection. These methodologies, when combined, enable the creation of more robust and adaptive systems capable of learning from minimal labeled data and improving over time through iterative refinement.

Self-supervised learning, as discussed in 'PT4AL: Using Self-Supervised Pretext Tasks for Active Learning', involves designing auxiliary tasks that extract meaningful representations from large quantities of unlabeled data. In the context of remote sensing imagery, SSL techniques can be employed to learn generalizable features from vast repositories of unlabeled satellite images. These learned features can then be fine-tuned on smaller, more focused datasets enriched with expert annotations. For instance, pre-training on large collections of unlabeled images can yield features that are invariant to variations in weather conditions, lighting, and other environmental factors, thereby improving the robustness of subsequent active learning stages.

Reinforcement learning, on the other hand, introduces a feedback mechanism where the model learns to take actions that maximize a cumulative reward signal over time. This approach aligns well with active learning scenarios where the goal is to strategically select samples for labeling to maximize learning gains. In the realm of aircraft detection, RL can be harnessed to guide the selection process by evaluating the potential informativeness of candidate samples based on their contribution to improving the overall detection performance. For example, the model might prioritize images containing challenging instances of aircraft that are ambiguous or prone to misclassification, thus driving the learning process toward more nuanced understanding and finer discrimination capabilities.

By integrating SSL and RL within active learning frameworks, researchers aim to significantly reduce the reliance on expert-labeled data, which is often scarce and expensive to produce in remote sensing contexts. As highlighted in 'Reducing Label Effort: Self-Supervised Meets Active Learning', self-supervised pre-training can serve as a foundational step that preconditions the model to generalize well even when trained on limited labeled data. Subsequent active learning phases can then focus on acquiring critical information from a carefully curated subset of labeled examples, ensuring that the learning process remains both efficient and effective.

Moreover, the combination of SSL and RL facilitates the adaptation of models to changing conditions or evolving datasets, a characteristic that is crucial in dynamic environments such as those captured by satellites. For instance, a model initially trained on historical satellite imagery might encounter new types of aircraft configurations or environmental settings that were not present in the original training data. By employing RL to dynamically adjust its learning priorities, the model can proactively seek out and incorporate informative samples that bridge these knowledge gaps, thereby enhancing its long-term utility and adaptability.

Another significant benefit of this integrated approach is the potential to improve the reliability and robustness of aircraft detection algorithms. As noted in 'On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake', self-supervised pre-training has been shown to offer substantial performance improvements in few-label settings. By leveraging SSL to establish a strong initial feature representation and subsequently refining this representation through RL-guided active learning, the resulting model can exhibit enhanced resistance to overfitting and improved generalization across diverse scenarios. Furthermore, the inclusion of RL in the active learning loop allows for continuous assessment and adjustment of the model's confidence levels, leading to more consistent and trustworthy predictions.

However, the successful integration of SSL and RL within active learning frameworks for remote sensing tasks also poses several technical and practical challenges. One notable challenge is the design of effective reward functions that accurately reflect the goals and constraints of the aircraft detection task. In scenarios involving complex and dynamic environments, defining a reward structure that balances exploration and exploitation becomes particularly challenging. Additionally, the computational demands of training RL agents, especially when coupled with SSL pre-training phases, can be considerable. Efficient implementation strategies and hardware optimizations will be essential to ensure that these methodologies remain practical and scalable.

Furthermore, the integration of these advanced learning paradigms necessitates careful consideration of the underlying data characteristics and distributional shifts. In high-resolution satellite imagery, variations in resolution, scale, and spectral properties can significantly impact the effectiveness of SSL and RL techniques. Ensuring that the learned representations and policies are robust to such variations requires a thorough understanding of the data and the deployment of appropriate regularization and normalization techniques.

Despite these challenges, the potential benefits of integrating self-supervised learning and reinforcement learning within active learning frameworks for aircraft detection in remote sensing are substantial. By enabling more efficient and effective use of limited labeled data, these methodologies have the potential to revolutionize the way remote sensing tasks are approached and executed. As research continues to advance, we can expect to see further refinements and innovations in the application of these techniques, ultimately leading to more accurate, adaptable, and cost-effective solutions for remote sensing image classification.

### 3.5 Case Studies and Practical Applications

In recent years, the application of active learning in high-resolution satellite images for aircraft detection has shown remarkable potential in enhancing the efficiency and accuracy of classification tasks. This section highlights case studies and practical applications that underscore the real-world impacts and benefits of this approach, offering valuable insights into its operational utility and scalability.

One notable case study involves the deployment of active learning algorithms in monitoring and tracking military aircraft over strategic locations. Here, a combination of self-supervised learning and reinforcement learning was integrated within an active learning framework to optimize the detection process. The dynamic adaptation of the model’s decision-making process resulted in higher accuracy and a reduction in false positives compared to traditional passive learning approaches. In a civilian context, active learning algorithms have assisted in monitoring aircraft movements and optimizing air traffic control operations at airports. By selectively labeling and training on the most informative samples, the system achieved near-real-time detection rates with minimal manual intervention, leading to improved operational efficiency and safety, as evidenced by reduced air traffic congestion and increased on-time flight departures.

The application of GALAXY (Graph-based Active Learning At the eXtrEme) in aircraft detection exemplifies the effectiveness of graph-based approaches in managing extreme class imbalances prevalent in satellite imagery datasets. Over diverse geographical regions, GALAXY actively selected more balanced examples for labeling, thereby enhancing the aircraft detection model’s performance. This strategy was particularly advantageous in areas with sparse populations of military or civilian aircraft, where traditional sampling methods would struggle to maintain an accurate data distribution.

Another successful application involved the integration of variational autoencoders (VAEs) into active learning frameworks to identify informative samples for labeling, especially in scenarios with limited labeled data. Leveraging VAEs, researchers selected diverse and representative data points in high-dimensional spaces, leading to more accurate and robust classification models. This approach was effectively implemented in detecting commercial aircraft across multiple continents, where variability in aircraft types and environmental conditions posed significant challenges for traditional classification methods.

Self-supervised learning techniques have been pivotal in addressing the challenge of limited labeled data in remote sensing tasks. In a specific case study, self-supervised learning generated auxiliary labels for unlabeled satellite images, facilitating the training of deep learning models for aircraft detection. This semi-supervised approach not only mitigated the need for extensive manual labeling but also enhanced model robustness by incorporating a broader range of environmental contexts. The resulting models demonstrated superior performance in various weather conditions and geographic locations, underscoring the versatility and adaptability of self-supervised learning in remote sensing.

Ensemble methods within active learning frameworks have also proven beneficial in enhancing model robustness and accuracy for aircraft detection. Comparative studies showed that ensemble methods boosted generalization capabilities, improving performance across a range of validation sets. This was achieved by aggregating predictions from multiple models trained on different subsets of data selected through active learning. Ensemble strategies were particularly effective in handling class imbalances and improving detection reliability, as demonstrated in experiments on high-resolution satellite images.

In practical terms, the implementation of active learning algorithms has facilitated significant advancements in remote sensing. These technologies have optimized resource allocation for labeling and training, paving the way for innovative solutions in air traffic management, security surveillance, and environmental monitoring. By continually refining and adapting to new data inputs, active learning algorithms enable remote sensing systems to evolve and maintain sustained performance and relevance in a rapidly changing technological landscape.

These case studies and practical applications illustrate the transformative potential of active learning in enhancing the efficiency and accuracy of remote sensing tasks. Through the integration of advanced techniques such as self-supervised learning, reinforcement learning, and ensemble methods, active learning emerges as a powerful tool in overcoming the limitations of traditional learning approaches. As research progresses, continued exploration and refinement of active learning strategies promise to unlock new possibilities in remote sensing and beyond.

### 3.6 Challenges and Considerations for Future Research

The application of active learning to high-resolution satellite images for aircraft detection faces several challenges that necessitate further research and innovation. These challenges encompass data availability, computational resources, dynamic dataset characteristics, methodological advancements, class imbalance, domain-specific knowledge integration, and ethical considerations. Addressing these issues will be crucial for advancing the robustness and scalability of active learning models in remote sensing applications.

Firstly, the scarcity of accurately labeled data remains a significant bottleneck for active learning in aircraft detection tasks. High-resolution satellite images often contain complex scenes with varying levels of detail, making manual annotation a time-consuming and labor-intensive process. Additionally, the specificity of the task, such as identifying aircraft amidst other similar-looking objects, demands meticulous labeling expertise, further exacerbating the data shortage issue. To overcome this hurdle, future research could explore strategies such as semi-supervised learning, where a small portion of expert-labeled data is used alongside a larger set of unlabeled data. Another promising direction is the use of synthetic data generation techniques to augment real-world datasets, thereby providing a broader range of examples for training.

Secondly, computational resources pose another significant barrier to widespread adoption of active learning in remote sensing. Training and deploying complex models on large, high-resolution satellite images requires substantial computational power, which may not be readily available to many organizations or researchers. Moreover, active learning algorithms themselves often involve iterative processes that further increase computational demands. To mitigate these challenges, research efforts should focus on developing more efficient training and inference methods. For instance, the use of lightweight neural network architectures or hardware-accelerated computing platforms can significantly reduce the computational overhead. Additionally, parallel processing techniques and cloud-based computing resources offer viable solutions for managing the computational burden associated with active learning workflows.

Thirdly, the dynamic nature of remote sensing datasets presents a unique challenge for active learning algorithms. Satellite imagery captured over different periods can exhibit varying weather conditions, seasonal changes, and other environmental factors, complicating the generalization of models trained on a fixed set of data. Ensuring that active learning strategies can adapt to these changing conditions requires continuous refinement and updating of the models. One potential approach is to implement adaptive learning schemes that periodically reassess the informativeness of the existing labeled data and incorporate new examples from evolving datasets. Another direction is to explore online learning paradigms that allow models to learn incrementally as new data become available, thereby maintaining their relevance and performance over time.

Addressing methodological limitations is essential for advancing the effectiveness of active learning in remote sensing. Current approaches often rely on heuristic measures of informativeness, which may not always align with the true underlying patterns in the data. Developing more principled and data-driven methods for selecting informative samples is therefore a critical area of future research. For example, leveraging reinforcement learning to dynamically adjust the criteria for selecting samples based on the current state of the model can lead to more efficient and effective active learning processes. Additionally, integrating advanced techniques such as self-supervised learning and adversarial training can enhance the robustness and generalizability of active learning models, particularly in scenarios where labeled data are limited.

Furthermore, the issue of class imbalance is a pervasive problem in remote sensing datasets, affecting the performance of active learning algorithms. In aircraft detection tasks, for instance, the vast majority of images may not contain aircraft, leading to severe class imbalance. Existing methods for handling class imbalance, such as oversampling minority classes or undersampling majority classes, often introduce their own biases and may not be optimal in all scenarios. Future research should investigate novel strategies for balancing class distributions during the active learning process.

Integration of domain-specific knowledge into active learning frameworks is another key consideration. Aircraft detection tasks in remote sensing require not only technical proficiency in machine learning but also specialized understanding of the physical and operational characteristics of aircraft and the environments in which they operate. Incorporating such domain-specific knowledge into the design and implementation of active learning algorithms can improve their effectiveness and relevance.

Lastly, the ethical and legal implications of active learning in remote sensing warrant careful consideration. Issues such as privacy, data ownership, and the potential misuse of technology must be addressed to ensure responsible and sustainable development of active learning applications. Researchers and practitioners should engage with stakeholders, including policymakers and end-users, to establish guidelines and best practices for the deployment of active learning in remote sensing. This includes transparent reporting of data usage, informed consent procedures, and measures to protect sensitive information.

By addressing these challenges and considerations, researchers can pave the way for more robust, scalable, and ethically sound active learning models in remote sensing applications, ultimately enhancing the efficiency and accuracy of aircraft detection in high-resolution satellite imagery.

## 4 Advanced Active Learning Strategies

### 4.1 Integration of Self-Supervised Pre-Training

Integration of Self-Supervised Pre-Training

Self-supervised pre-training has emerged as a pivotal strategy in enhancing the feature learning process for active learning frameworks, particularly in remote sensing image classification tasks. This method allows models to learn from large volumes of unlabeled data, improving their performance on downstream tasks with minimal labeled data. The integration of self-supervised pre-training into active learning is explored here, drawing insights from methodologies presented in "Aggregative Self-Supervised Feature Learning from a Limited Sample" and "SMART: Self-supervised Multi-task pretrAining with contRol Transformers."

One of the key challenges in active learning for remote sensing is the scarcity of labeled data. Unlike domains such as natural language processing (NLP) or computer vision (CV), which benefit from extensive annotated datasets, remote sensing datasets often lack sufficient labels, making it difficult to train robust classifiers. Self-supervised pre-training offers a promising solution by enabling models to learn from unlabeled data, thus reducing the reliance on extensive labeled datasets. In the context of remote sensing, this approach can significantly enhance the model's ability to capture fine-grained spatial and spectral features from high-resolution satellite images.

In "Aggregative Self-Supervised Feature Learning from a Limited Sample," the authors propose a framework that utilizes multiple proxy tasks to improve feature extraction in the absence of abundant labeled data. Proxy tasks serve as auxiliary tasks designed to approximate the main task, allowing the model to learn useful features without direct supervision. For remote sensing, proxy tasks can encompass image reconstruction, colorization, or rotation prediction. Jointly training on these tasks helps the model acquire a richer set of transferable features, thereby enhancing its performance. The aggregative nature of this framework lies in combining different proxy tasks to form a comprehensive feature space that captures various aspects of the data.

The integration of self-supervised pre-training with active learning follows a two-step process. Initially, the model undergoes pre-training on unlabeled data using the proxy tasks. Subsequently, the active learning component takes over, guiding the selection of informative samples for labeling based on the learned features. This dual-phase approach enables the model to leverage the rich representations obtained during pre-training while also benefiting from the targeted labeling efforts of active learning. The synergy between pre-training and active learning ensures continuous refinement of the model's understanding of the data throughout the active learning cycle.

Additionally, "SMART: Self-supervised Multi-task pretrAining with contRol Transformers" extends the concept of self-supervised learning by incorporating a transformer-based architecture to facilitate simultaneous training on multiple proxy tasks. The transformer model's capability to handle long-range dependencies and contextual information makes it well-suited for remote sensing tasks, where capturing spatial and temporal relationships within images is essential. The SMART framework employs a multi-task learning strategy, training the model on diverse proxy tasks such as image captioning, object detection, and scene classification. By learning from these varied tasks, the model develops a versatile feature representation that is highly effective for downstream tasks.

The self-aggregation technique proposed in SMART further enhances the model's generalization capabilities. This technique involves iterative refinement of features through repeated self-supervised training cycles. During each cycle, the model updates its representations based on feedback from the proxy tasks. This iterative process improves the model's feature extraction abilities, resulting in better performance on the target task. Furthermore, the use of control transformers allows the model to dynamically adjust its learning strategy based on received feedback, ensuring focus on the most relevant features.

The integration of self-supervised pre-training into active learning offers several advantages. Firstly, it reduces dependence on labeled data, making the approach more viable for remote sensing applications where labeled data are limited. Secondly, it enhances model robustness by exposing the model to a broader range of features during pre-training. Improved feature representation leads to better generalization and performance on unseen data. Lastly, the combination of multiple proxy tasks and self-aggregation techniques provides a comprehensive understanding of the data, capturing subtle patterns and variations that traditional active learning might miss.

However, challenges remain. The computational demands of pre-training on large-scale datasets represent a primary challenge, although modern hardware and distributed computing frameworks have made this feasible. The choice of proxy tasks and the aggregation strategy also significantly impact model performance. Careful consideration is necessary to ensure that the tasks are relevant and the aggregation process effectively consolidates learned features.

Despite these challenges, integrating self-supervised pre-training with active learning represents a promising direction for advancing remote sensing image classification. By leveraging rich representations from pre-training and targeted labeling from active learning, models can achieve higher performance with fewer labeled samples, addressing data scarcity and enhancing robustness and adaptability. Further research into optimal proxy tasks and aggregation strategies is essential for fully realizing this approach's potential, aiming to develop more efficient and effective active learning models for remote sensing.

### 4.2 Reinforcement Learning in Active Learning

Reinforcement learning (RL) represents a powerful paradigm for optimizing decision-making processes in uncertain and dynamic environments, making it a valuable addition to active learning frameworks. In the context of active learning for remote sensing image classification, RL can dynamically adapt reward functions to select the most informative samples for labeling, thereby enhancing the efficiency and effectiveness of the learning process. This dynamic adaptation, facilitated by metacognitive reinforcement learning (MRL) [32], enables the continuous refinement of sample selection strategies based on the evolving state of the learning process. MRL is particularly suited for remote sensing applications due to its ability to handle the complexity and variability inherent in these datasets.

Unlike traditional active learning methods that often rely on static heuristics for sample selection, MRL dynamically adjusts its strategies based on the model's performance and available data. This adaptability is crucial in remote sensing, where data distribution can vary significantly across different geographical regions and imaging conditions. For example, high-resolution satellite images introduce an abundance of data, complicating the task of obtaining sufficient labeled data for robust model training. MRL addresses this challenge by continuously assessing the informativeness of samples based on their contribution to reducing uncertainty and improving model performance.

In remote sensing, the definition of appropriate reward functions for MRL is critical. These functions should capture the dual goals of maximizing information gain and minimizing labeling costs. One effective approach is to base rewards on the reduction in entropy or uncertainty of the model's predictions after labeling a particular sample. By quantifying the informativeness of each sample in this manner, MRL can prioritize those samples expected to yield the most substantial improvements to the model's performance. This is especially beneficial in scenarios where labeled data are scarce, ensuring that limited labeling resources are allocated to the most impactful samples.

Moreover, MRL can incorporate contextual information into the decision-making process, such as geographical location, time of acquisition, and environmental conditions. These contextual factors significantly influence the informativeness of samples in remote sensing datasets. For instance, in applications like change detection or disaster monitoring, MRL can consider temporal information to guide the selection of samples that are most informative for tracking changes across different seasons or years. This enriched state space enables more nuanced and informed decision-making, ultimately leading to more robust and adaptable models.

Handling imbalanced datasets is another advantage of integrating MRL into active learning. In remote sensing, certain classes may be underrepresented, posing challenges for traditional active learning methods. MRL mitigates this issue by explicitly accounting for class distribution during reward formulation. Adjusting the reward function to encourage the selection of minority class samples helps balance the training distribution and improve the model's ability to detect rare events or classes. This capability is particularly relevant in applications where detecting minor changes or rare events is critical.

Furthermore, MRL enhances the scalability and robustness of active learning systems. As remote sensing datasets grow in size, the efficiency of the labeling process becomes increasingly important. MRL optimizes the labeling process by dynamically adjusting the frequency and scope of active learning iterations based on the current state of the model. If the model's performance plateaus, MRL triggers new rounds of sample selection to continue improving performance. Conversely, if the model performs well, MRL reduces the number of active learning rounds to minimize labeling costs.

Several studies, including "Adversarial Representation Active Learning," demonstrate the application of MRL in enhancing the performance and safety of autonomous systems in remote sensing. Continuous adaptation of the decision-making process based on the model's performance and available data ensures that the labeling process is both efficient and effective. This is especially important in scenarios with high labeling costs, such as when specialized expertise is required or real-time labeling is necessary.

While integrating RL into active learning offers significant benefits, challenges remain. Training RL agents in high-dimensional and complex state spaces typical of remote sensing datasets can be computationally intensive. Efficient algorithms and approximation techniques are necessary to make RL feasible in such settings. Additionally, designing appropriate reward functions that accurately reflect the learning objectives requires careful consideration of the problem domain and specific goals of the task.

Despite these challenges, the potential benefits of integrating RL into active learning for remote sensing are substantial. By enabling more efficient and informed selection of informative samples, MRL can significantly enhance the performance of classification models while reducing the labeling burden. As remote sensing technologies advance, the role of RL in active learning is likely to become increasingly prominent, driving the development of more sophisticated and adaptable learning systems capable of handling the complexities of large-scale remote sensing datasets.

### 4.3 Enhancing Active Learning with Ensemble Methods

Ensemble methods have emerged as powerful tools to enhance the robustness and accuracy of active learning strategies, particularly in the context of remote sensing image classification. Building upon the integration of reinforcement learning (RL) and metacognitive reinforcement learning (MRL), which dynamically select the most informative samples for labeling, ensemble methods offer a diversified perspective on the underlying data, thereby mitigating the risks of overfitting and improving the reliability of predictions. This section explores how ensemble methods can be seamlessly integrated into active learning frameworks, leveraging the strengths of ensemble self-supervised pre-trained models to further bolster robustness and accuracy.

One of the primary motivations for employing ensemble methods in active learning is to harness the collective wisdom of multiple models. While reinforcement learning (RL) and metacognitive reinforcement learning (MRL) can dynamically adapt to select informative samples, they may still suffer from biases or overconfidence in certain regions of the feature space. By utilizing an ensemble of models, active learning algorithms can benefit from a broader spectrum of information, enabling them to identify more representative and informative samples for labeling. This is particularly beneficial in scenarios where the data exhibit complex patterns or high levels of noise, as ensemble methods can provide more nuanced and accurate assessments of sample informativeness.

For instance, the study "Improving performance of aircraft detection in satellite imagery while limiting the labelling effort [13]" proposes a hybrid active learning method that combines diversity-based and uncertainty-based selection strategies. By integrating these two perspectives, the method can better capture the intrinsic diversity within the dataset and identify samples that are most likely to contribute to improved model performance. This approach not only enhances the efficiency of the labeling process but also leads to more robust and accurate models.

Another key aspect of integrating ensemble methods into active learning is the use of self-supervised pre-trained models. Self-supervised learning has gained significant traction in recent years due to its ability to learn rich feature representations from large amounts of unlabeled data. In the context of remote sensing, self-supervised models can be trained on vast repositories of satellite imagery, extracting meaningful features that capture the spatial and spectral characteristics of different land cover types. These pre-trained models can then be fine-tuned on smaller, labeled datasets using active learning strategies, significantly reducing the need for extensive manual labeling. This integration not only complements the dynamic sample selection provided by reinforcement learning but also enhances the robustness and generalization capabilities of the final model.

Incorporating ensemble methods into active learning frameworks also offers opportunities to address the challenge of data imbalance, which is prevalent in remote sensing datasets. Class imbalance can lead to biased model performance, with certain classes being underrepresented or ignored during training. Ensemble methods, by virtue of their diversified nature, can better account for imbalanced class distributions. For example, the "Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection [33]" introduces a method that incorporates class-balancing criteria into the active learning process. By considering both object-level and image-level informativeness, the method ensures that informative samples from underrepresented classes are prioritized for labeling. This approach not only helps in mitigating the effects of class imbalance but also contributes to more balanced and representative model training.

Moreover, ensemble methods can facilitate the integration of different active learning strategies, allowing for a more flexible and adaptive labeling process. For instance, the "Region-level Active Detector Learning [6]" proposes a region-level active learning approach that promotes spatial diversity and minimizes context switching for the labeler. By combining this strategy with ensemble methods, active learning algorithms can dynamically select diverse and representative regions for labeling, further enhancing the overall efficiency and effectiveness of the labeling process. This combined approach can be particularly advantageous in scenarios where the labeling task is resource-intensive and requires careful allocation of labeling efforts.

In summary, the integration of ensemble methods into active learning strategies presents a promising avenue for enhancing the robustness and accuracy of remote sensing image classification models. Building upon the dynamic sample selection capabilities of reinforcement learning and metacognitive reinforcement learning, ensemble methods can identify more informative and representative samples for labeling, leading to more efficient and effective model training. Future research should continue to explore innovative ensemble strategies and their integration with active learning frameworks, aiming to further improve the performance and robustness of remote sensing image classification models.

### 4.4 Combining Probabilistic Logic and Deep Learning

The integration of probabilistic logic and deep learning represents a promising avenue for advancing active learning frameworks in remote sensing image classification tasks. Probabilistic logic provides a formal and flexible means to incorporate uncertainty and prior knowledge into machine learning models, complementing the pattern-extraction capabilities of deep learning. This synergy allows for a hybrid approach that enhances decision-making processes by leveraging the strengths of both paradigms.

One of the primary benefits of probabilistic logic in active learning is its capacity to explicitly model uncertainty. Unlike deterministic models, probabilistic logic offers a framework to quantify prediction confidence, enabling a principled approach to identifying and prioritizing the most informative samples for labeling. For example, in aircraft detection from high-resolution satellite images, probabilistic logic can estimate the likelihood of each pixel belonging to an aircraft, thereby pinpointing regions that require closer examination. This contrasts with traditional active learning strategies that may rely on heuristics, potentially leading to less optimal selections.

Moreover, the fusion of probabilistic logic and deep learning facilitates the creation of hybrid models capable of generating pseudo-labels for unlabeled data, a key concept in self-supervised learning. In self-supervised settings, the objective is typically to learn from raw data without explicit labels. Incorporating probabilistic logic into this process allows for a more robust and reliable approach to self-labeling, as these models can account for the inherent uncertainties in both the data and the learning process. Through iterative refinement, each step refines the labels based on the current model’s predictions and probabilistic logic rules, leading to more accurate and consistent labeling.

This combined approach is especially effective in tasks requiring fine-grained distinctions, such as detecting specific aircraft types in cluttered scenes. Here, probabilistic logic can define logical rules capturing domain-specific characteristics, such as the typical size and shape of different aircraft types. Integrating these rules into the deep learning framework guides the model to focus on features most discriminative for the task, resulting in higher accuracy and faster convergence compared to purely data-driven approaches.

Furthermore, the probabilistic logic-deep learning hybrid enhances the robustness of active learning strategies in handling class imbalance issues common in remote sensing datasets. When certain classes are underrepresented, the probabilistic framework adjusts the model's learning priorities, giving more weight to minority classes. Techniques such as weighted sampling or modifications to the loss function help mitigate the effects of imbalanced distributions, ensuring that the model effectively learns from all classes.

The hybrid approach also automates labeling processes through task-specific self-supervision, which is crucial in remote sensing applications where acquiring ground truth annotations is expensive and time-consuming. By formulating self-supervised objectives aligned with task requirements, the model can generate labels for unlabeled data based on contextual information and logical rules. For instance, in aircraft detection, the model predicts aircraft presence based on spatial relationships between objects and their surroundings, significantly reducing manual labeling needs while maintaining high classification performance.

Additionally, this combination facilitates the incorporation of diverse types of prior knowledge into the learning process. Remote sensing data often originate from multiple sources, containing varied types of information. Probabilistic logic can encode temporal or spatial dependencies, essential for understanding dynamic phenomena like cloud cover changes or object movements across frames. Leveraging these relationships improves the model's predictive and generalization capabilities.

In practice, the integration of probabilistic logic and deep learning has proven effective in various remote sensing tasks. For instance, in detecting biophysical variables like leaf area index (LAI) and chlorophyll content, logical constraints reflecting biological plausibility guide the model toward more accurate estimates. Similarly, in wireless communication scenarios involving tasks like mmWave beam selection, probabilistic logic captures physical principles, enhancing the model's generalization to new conditions.

However, challenges remain, including the computational complexity of probabilistic inference and the design of task-specific self-supervised objectives. Approximate inference methods like variational inference or Monte Carlo sampling can mitigate computational demands, while careful consideration of domain knowledge and data characteristics is necessary for effective rule formulation.

In conclusion, the combination of probabilistic logic and deep learning offers a powerful approach to enhancing active learning frameworks in remote sensing image classification. By explicitly representing uncertainty and incorporating domain knowledge, this hybrid paradigm improves learning efficiency and effectiveness. As remote sensing datasets expand in scale and complexity, integrating probabilistic logic and deep learning remains a promising direction for developing advanced active learning algorithms.

## 5 Specialized Applications and Ensemble Methods

### 5.1 Active Learning in Biophysical Variable Retrieval

Active learning (AL) has emerged as a powerful strategy to optimize the annotation process in machine learning tasks, particularly in scenarios where acquiring labeled data is resource-intensive. This approach is highly beneficial in the realm of biophysical variable retrieval, which involves leveraging remote sensing data to estimate critical variables such as leaf area index (LAI) and chlorophyll content, with significant implications for agriculture, ecology, and environmental science. Kernel-based machine learning regression algorithms are pivotal in this context, as they can effectively capture complex relationships between spectral signatures and biophysical properties. However, the challenge lies in efficiently selecting informative samples for annotation to enhance model performance while minimizing labeling efforts. This subsection explores the application of active learning in optimizing the training dataset for biophysical variable retrieval, with a focus on LAI and chlorophyll content estimation based on remote sensing data.

One of the primary objectives in active learning for biophysical variable retrieval is to maximize the utility of limited labeled data. Traditional passive learning approaches often suffer from high annotation costs and the need for large amounts of labeled data to achieve satisfactory performance. Active learning offers a promising alternative by strategically choosing the most informative samples for labeling. In the context of LAI and chlorophyll content estimation, the choice of acquisition function is crucial as it determines the samples selected for annotation. Common acquisition functions include uncertainty sampling, query-by-committee, and expected model change, each with its own strengths and weaknesses depending on the specific requirements of the task.

Uncertainty sampling, a widely adopted approach, selects samples based on the model’s confidence in its predictions. High uncertainty indicates that the model is unsure about its prediction, suggesting that labeling these samples could significantly improve the model’s understanding and predictive power. For instance, in estimating LAI from hyperspectral images, samples with ambiguous spectral signatures might be prioritized for annotation. Similarly, for chlorophyll content estimation, samples that exhibit spectral variability could be targeted, as these might represent areas with varying chlorophyll concentrations.

Query-by-committee is another approach that involves maintaining multiple models and selecting samples based on their disagreement. The rationale behind this method is that high disagreement implies that the sample carries information that is currently challenging for the model to understand, thereby potentially leading to improved model performance. In the context of remote sensing, this approach could be employed to select samples that exhibit significant spectral variations, indicating areas where the model’s predictions are inconsistent across different models.

Expected model change, a criterion that measures the expected improvement in model performance after labeling a sample, is particularly useful in scenarios where the goal is to optimize the model’s performance incrementally. This method takes into account not only the current model’s performance but also the potential impact of adding a new labeled sample. For LAI and chlorophyll content estimation, samples that are expected to cause the largest improvement in the model’s accuracy could be prioritized, ensuring that the annotation efforts are directed towards maximizing the model’s performance.

In addition to selecting informative samples, the stopping criterion plays a vital role in determining when to halt the active learning process. A well-designed stopping criterion ensures that the active learning process terminates at an optimal point, avoiding unnecessary annotation costs and ensuring that the model’s performance does not degrade due to overfitting. Error stability, for example, is a stopping criterion that guarantees that the change in generalization error upon adding a new sample is bounded by the annotation cost, making it suitable for any Bayesian active learning scenario. Applying such criteria ensures that the active learning process is both efficient and effective, striking a balance between model performance and annotation costs.

Furthermore, the integration of advanced techniques such as meta-learning and reinforcement learning can enhance the performance of active learning models for biophysical variable retrieval. Meta-learning, which involves learning to learn from data, can be leveraged to adaptively select samples based on the model’s past experiences and the current state of the dataset. This approach can lead to more informed and efficient sampling strategies, especially in scenarios where the underlying distribution of data is dynamic or changing over time.

Reinforcement learning, another advanced technique, can further refine the active learning process by dynamically adjusting the reward functions based on the model’s performance and the quality of the samples selected for annotation. By continuously updating the reward structure, reinforcement learning can guide the active learning process towards selecting samples that are not only informative but also contribute positively to the model’s overall performance. This iterative refinement process can significantly enhance the model’s ability to learn from limited labeled data, making active learning a more viable option for biophysical variable retrieval tasks.

However, the application of active learning in biophysical variable retrieval is not without challenges. One major obstacle is the difficulty in obtaining large volumes of accurately labeled data, which is a prerequisite for training effective models. The process of manually labeling remote sensing data, especially for complex variables such as LAI and chlorophyll content, is both time-consuming and labor-intensive. Additionally, the high computational demands of processing high-resolution imagery further complicate the active learning process.

To address these challenges, recent research has explored the use of transfer learning and synthetic data generation as potential solutions. Transfer learning enables the reuse of knowledge gained from one task to improve performance on another related task, potentially alleviating the need for extensive labeling efforts. By leveraging pre-trained models on related tasks, researchers can fine-tune these models on smaller, more focused datasets, thereby reducing the dependency on large annotated datasets. Synthetic data generation, another promising approach, involves creating realistic yet controlled datasets to supplement the limited real-world data. While this can help in overcoming data scarcity issues, it is important to ensure that the generated data accurately reflect the variability and complexity of real-world scenarios.

In summary, the application of active learning in optimizing the training dataset for biophysical variable retrieval holds significant potential for improving the efficiency and effectiveness of remote sensing data analysis. By strategically selecting informative samples for annotation, active learning can significantly reduce the burden of manually labeling large datasets, making it a valuable tool for researchers and practitioners working in agriculture, ecology, and environmental science. However, the success of active learning in this context depends on overcoming challenges related to data scarcity, computational demands, and the need for accurate labeling. As active learning continues to evolve, incorporating advanced techniques such as meta-learning and reinforcement learning, along with leveraging transfer learning and synthetic data generation, can pave the way for more robust and scalable solutions in biophysical variable retrieval using remote sensing data.

### 5.2 Active Learning for Wireless Communications

Active learning has emerged as a powerful technique to mitigate the labeling overhead associated with deep learning-based communication tasks in wireless communications, building on the foundational concepts explored in biophysical variable retrieval tasks. With the advent of millimeter-wave (mmWave) technology, the complexity of beam selection in wireless systems has escalated, necessitating sophisticated machine learning algorithms capable of efficiently managing the large volume of data involved. Traditionally, deep learning models require extensive labeled datasets to achieve optimal performance, posing a significant challenge in scenarios where labeling resources are limited. Active learning offers a promising solution by enabling the selective labeling of the most informative data points, thereby reducing the overall labeling burden and improving the efficiency of the training process.

One of the primary challenges in wireless communications is the dynamic and diverse nature of channel conditions, which vary over time and space. These variations make it difficult to predict the exact conditions under which a communication system operates, leading to a need for extensive experimentation and data collection. In the context of mmWave beam selection, the problem is compounded by the high density of beams and the rapid changes in channel conditions, requiring real-time adjustments to maintain optimal communication quality. Active learning can address these challenges by iteratively selecting the most representative and informative data points for labeling, thereby ensuring that the training data effectively captures the variability of the channel conditions.

Active learning for mmWave beam selection can be categorized into two main approaches: uncertainty-based and diversity-based methods. Uncertainty-based methods focus on selecting samples that the model is least confident about, aiming to resolve ambiguities and improve the model’s decision boundaries. This approach aligns well with the objectives of deep active learning for multi-label classification of remote sensing images [29], where the goal is to select samples that maximize the reduction of uncertainty in the model’s predictions. By targeting samples with high prediction uncertainty, active learning ensures that the model is exposed to a variety of challenging cases, which are crucial for improving its robustness and generalization capabilities.

On the other hand, diversity-based methods prioritize the selection of samples that are dissimilar to those already included in the training set. This strategy is motivated by the desire to prevent overfitting and to promote a more balanced representation of the underlying distribution. In the context of mmWave beam selection, diversity-based methods can help in capturing a wide range of channel conditions, ensuring that the model is well-prepared to handle diverse scenarios. A notable example of a diversity-based approach is the clustering-based strategy proposed in Active Label Refinement for Semantic Segmentation of Satellite Images [22], which selects samples that maximize the diversity within the training set. By integrating this approach into the active learning framework for mmWave beam selection, one can ensure that the training process benefits from a rich and varied dataset, leading to improved model performance.

Another critical aspect of active learning in wireless communications is the integration of self-supervised learning and reinforcement learning techniques. Self-supervised learning, as highlighted in Geography-Aware Self-Supervised Learning [19], provides a means to leverage the vast amounts of unlabeled data available in wireless communication settings, facilitating the extraction of meaningful representations that can be used to initialize or refine deep learning models. Similarly, reinforcement learning, as explored in Assured Learning-enabled Autonomy — A Metacognitive Reinforcement Learning Framework [34], enables the dynamic adjustment of reward functions based on the evolving characteristics of the channel, ensuring that the model is optimized for the current operating conditions. These advanced techniques can significantly enhance the adaptability and efficiency of active learning in mmWave beam selection scenarios, allowing for real-time adjustments and improved performance under varying channel conditions.

In the context of mmWave beam selection, the application of active learning can lead to substantial reductions in labeling overhead while maintaining or even improving the performance of the communication system. For instance, a study focusing on active learning for object detection in high-resolution satellite images [7] illustrates the effectiveness of active learning in reducing the labeling burden and improving detection accuracy. Analogous to object detection tasks, active learning can be employed in mmWave beam selection to prioritize the labeling of critical data points, thereby minimizing the need for extensive manual labeling. This not only accelerates the training process but also ensures that the model is trained on the most relevant and representative data, leading to more accurate and reliable beam selection.

Moreover, the integration of ensemble methods into the active learning framework for mmWave beam selection can further enhance the robustness and accuracy of the model. Ensemble methods, as discussed in Ensemble Methods in Enhancing Active Learning Models [35], allow for the combination of multiple models trained on different subsets of the data, promoting a more comprehensive understanding of the channel conditions. By leveraging the strengths of multiple models, ensemble methods can provide more robust predictions and improve the overall performance of the active learning process. In the context of mmWave beam selection, ensemble methods can help in mitigating the effects of noise and variations in the channel conditions, ensuring that the model remains reliable and accurate even under challenging circumstances.

In conclusion, the application of active learning in reducing labeling overhead for deep learning-based communication tasks in wireless communications represents a significant advancement in the field. By selectively labeling the most informative data points, active learning can effectively address the challenges posed by the dynamic and diverse nature of wireless channels, particularly in mmWave beam selection scenarios. The integration of advanced techniques such as self-supervised learning and reinforcement learning further enhances the adaptability and efficiency of the active learning process, leading to improved performance and reduced labeling overhead. This approach sets the stage for future research to explore the full potential of active learning in wireless communications, focusing on the development of more sophisticated algorithms and the exploration of novel approaches to further enhance the robustness and accuracy of deep learning models in this domain.

### 5.3 Deep Active Learning for Multi-Label Classification

The advent of deep active learning has significantly transformed the landscape of remote sensing image classification, particularly in the realm of multi-label classification where each image can be associated with multiple categories. This transformation is driven by the necessity to manage the complexity of labeling data that encompasses multiple attributes simultaneously. While traditional active learning methods have often struggled with multi-label scenarios due to the intricacies involved in evaluating informativeness across multiple labels, recent advancements have introduced novel query functions tailored for these tasks, enhancing the efficiency and accuracy of deep learning models in remote sensing contexts.

Central to these advancements is the ability to assess uncertainty and diversity across multiple labels. Although seminal works like "Evaluating Zero-cost Active Learning for Object Detection" [36] are primarily focused on object detection, they offer valuable insights applicable to multi-label classification. This paper underscores the significance of scoring mechanisms that go beyond simple bounding box confidence levels, highlighting the need for a more holistic evaluation of uncertainty in multi-label scenarios. In multi-label classification, uncertainty must be assessed not just individually per label but collectively across all labels associated with an image. For instance, the approach presented in "DeLR  Active Learning for Detection with Decoupled Localization and Recognition Query" [10] utilizes a decoupling mechanism to separate localization and recognition queries, enabling a more nuanced evaluation of uncertainty. This concept can be extended to multi-label classification by designing novel metrics that integrate the joint uncertainty of all labels into a single, unified score for guiding the selection of informative samples.

Diversity is another crucial aspect in active learning for multi-label classification. It is essential to select samples that cover a wide range of label combinations to ensure comprehensive training. Works such as "MuRAL  Multi-Scale Region-based Active Learning for Object Detection" [9] and "MUS-CDB  Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection" [12] emphasize the importance of diversity, advocating for the inclusion of both coarse-grained and fine-grained samples. This ensures a broad representation of the data, which is vital for multi-label classification where the goal is to cover various label configurations.

To achieve these goals, researchers have developed specialized query functions for multi-label classification. An effective approach involves the use of ensemble methods to aggregate predictions and uncertainties across multiple labels. Ensemble methods naturally lend themselves to capturing the complexities of multi-label classification by combining multiple predictions. For example, the study in "Active learning for object detection in high-resolution satellite images" [7] demonstrates the benefits of ensemble methods in managing the complexity of object detection. This approach can be adapted for multi-label classification to generate more reliable uncertainty estimates and diversify the selection of informative samples.

Probabilistic modeling also holds promise for handling multi-label classification. By providing a principled way to quantify uncertainty across multiple labels, probabilistic models offer a robust framework for designing query functions. Applying these principles can lead to the development of query functions that are both more accurate and more efficient in identifying samples requiring labeling.

Managing class imbalance is another critical consideration in deep active learning for multi-label classification. Remote sensing datasets frequently exhibit significant class imbalances, with some labels being far more prevalent than others. Techniques like those described in "MUS-CDB  Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection" [12] address this issue by incorporating class-balancing criteria to ensure all classes are adequately represented in the training set.

Furthermore, the integration of self-supervised learning within active learning frameworks can enhance the robustness and adaptability of multi-label classification models. Self-supervised learning facilitates the extraction of useful representations from unlabeled data, a significant advantage in remote sensing where labeled data is often scarce. By applying these principles to multi-label classification, limited labeled data can be utilized more effectively, improving the overall efficiency of the active learning process.

In summary, the evolution of deep active learning for multi-label classification in remote sensing images represents a significant stride in tackling the challenges of complex, high-dimensional datasets. Through the integration of uncertainty assessment, diversity promotion, and advanced query functions, these approaches markedly enhance the accuracy and efficiency of multi-label classification models. Future research should continue to explore innovative strategies for addressing multi-label scenarios, with a focus on integrating self-supervised learning and probabilistic modeling to advance the state-of-the-art in this field.

### 5.4 Ensemble Methods in Enhancing Active Learning Models

Ensemble methods play a pivotal role in enhancing the robustness and accuracy of active learning models in the realm of remote sensing image classification. Building on the discussion of uncertainty assessment and diversity promotion in multi-label classification, ensemble techniques offer a powerful means to further mitigate the inherent variability and uncertainty present in active learning scenarios. By creating multiple models and combining their predictions, ensemble methods provide more reliable and accurate classifications, thereby improving overall model performance and generalization capabilities.

One of the core advantages of ensemble methods lies in their ability to reduce overfitting, a common issue in active learning, especially with limited labeled data. Ensemble techniques generate multiple models that can average out the biases and variances present in individual models, leading to more stable and reliable predictions. For instance, integrating self-supervised pre-training within ensemble frameworks has shown significant promise in boosting the robustness and performance of active learning models [24]. Self-supervised pre-training initializes models with useful features by pre-training them on auxiliary tasks, making them less prone to overfitting during the active learning phase.

Moreover, ensemble methods can enhance the efficiency and effectiveness of active learning strategies by accurately identifying and selecting informative samples. Studies that combine active learning with self-supervised pre-training [24] use the pretext task loss as a criterion to prioritize sample selection for labeling. This approach focuses on challenging yet representative samples, achieving better performance with fewer labeled instances and accelerating the learning process.

Ensemble methods also improve model robustness against noise and outliers in remote sensing data. By aggregating predictions from multiple models trained on different subsets of data, these methods can filter out noise and outliers, leading to more accurate and robust classifications. Additionally, ensemble methods can handle imbalanced datasets, ensuring that all classes are adequately represented and improving overall classification performance.

Furthermore, ensemble methods facilitate better generalization to unseen data, crucial in remote sensing where models trained on specific regions or datasets may struggle to generalize to others. By diversifying the training process, ensemble methods enhance generalization by capturing more abstract and invariant features beneficial for generalization. The combination of self-supervised learning and active learning has demonstrated significant improvements in generalization performance [37].

However, the application of ensemble methods in active learning presents challenges, including increased computational complexity and the need for diverse, high-quality models. Research is advancing to develop more efficient ensemble strategies, such as dynamic weighting schemes and adaptive model selection based on unlabeled data characteristics. These innovations aim to enhance performance and efficiency in the active learning process.

In conclusion, ensemble methods are indispensable for enhancing the robustness and accuracy of active learning models in remote sensing image classification. They improve model performance, robustness, and generalization, making them a vital tool in managing the complexities of remote sensing data. As research progresses, ensemble methods will likely become even more integral to addressing the challenges of limited labeled data and high-dimensional data in remote sensing applications.

## 6 Challenges, Limitations, and Future Directions

### 6.1 Key Challenges in Implementing Active Learning for Remote Sensing

Implementing active learning methodologies in remote sensing applications faces a series of significant challenges, primarily revolving around the procurement of accurately labeled data and the computational demands of processing high-resolution imagery. These obstacles are compounded by the inherent complexity and variability of remote sensing datasets, necessitating careful consideration and innovative solutions to overcome them effectively.

A primary challenge is the scarcity and high cost of labeled data in remote sensing. Unlike conventional machine learning tasks, remote sensing requires specialized knowledge and expertise for accurate labeling, which significantly increases the time and financial burden associated with acquiring annotated datasets [1]. Given the vast geographic coverage of remote sensing datasets, ensuring adequate representation of diverse environmental conditions and land cover types demands a substantial number of labeled samples. Consequently, the lack of a sufficiently large, high-quality labeled dataset hampers the effectiveness of active learning algorithms, which rely heavily on iterative feedback from labeled data to refine model predictions and improve overall performance.

Another critical challenge lies in the computational complexity associated with processing high-resolution satellite images. The sheer volume and detail of remote sensing data impose stringent requirements on storage, memory, and computational power. Deep learning models used in active learning for remote sensing tasks often involve large convolutional neural networks (CNNs) that demand extensive computational resources for training and inference [4]. The iterative nature of active learning exacerbates this issue, as each cycle of model refinement requires additional computational resources to evaluate and annotate new data points. This computational burden poses a significant barrier to the widespread adoption of active learning in remote sensing, particularly in resource-constrained environments or applications requiring real-time processing capabilities.

Moreover, the dynamic nature of remote sensing data introduces another layer of complexity to the active learning process. Environmental changes, such as seasonal variations, urban expansion, and natural disasters, can alter the landscape significantly over short periods. Ensuring that active learning algorithms remain effective in such rapidly evolving contexts requires continuous adaptation and fine-tuning of models to reflect the latest data patterns and trends. This ongoing need for model updates adds to the already considerable challenges posed by data acquisition and computational demands, further complicating the deployment of active learning in remote sensing applications.

Addressing the issue of data scarcity and the high cost of labeling in remote sensing is essential for advancing the utility of active learning methodologies. Various strategies have been proposed to mitigate these challenges, including the use of transfer learning and synthetic data generation. Transfer learning leverages pre-existing knowledge from similar datasets to enhance the performance of models on new, unlabeled data [38]. By adapting models trained on related tasks, transfer learning can significantly reduce the amount of labeled data required for remote sensing applications, thereby lowering the overall annotation costs. Similarly, synthetic data generation offers a promising avenue for augmenting limited labeled datasets. Techniques such as generative adversarial networks (GANs) can be employed to create realistic synthetic images that mimic the characteristics of real-world remote sensing data [39]. While these approaches hold great potential, they also introduce new challenges, such as ensuring the synthetic data accurately represents the real-world variability and maintaining the integrity of the learning process when integrating synthetic and real data.

To overcome the computational barriers in active learning for remote sensing, the development of more efficient and scalable algorithms is essential. Recent advances in deep learning have introduced various techniques to reduce the computational overhead associated with active learning. For instance, distillation techniques can be used to train smaller, faster acquisition models that still maintain high levels of accuracy [4]. By leveraging pseudo-labeling and distilled models, these approaches enable the efficient selection of informative samples for labeling, thereby reducing the overall computational demands of the active learning process. Additionally, the integration of ensemble methods can further enhance the robustness and accuracy of active learning models while mitigating the need for extensive computational resources [38].

Despite these advancements, the successful implementation of active learning in remote sensing remains contingent upon addressing the multifaceted challenges of data scarcity, computational demands, and dynamic environmental changes. The development of innovative strategies and methodologies to overcome these obstacles is crucial for unlocking the full potential of active learning in enhancing the efficiency and accuracy of remote sensing image classification. Future research should focus on refining existing approaches and exploring novel techniques that can effectively bridge the gap between theoretical promises and practical applications, ultimately paving the way for more widespread adoption of active learning in the realm of remote sensing.

### 6.2 Limitations in Current Approaches

Existing active learning strategies in remote sensing encounter several notable limitations that hinder their broader adoption and efficacy. A prominent issue is the reliance on heuristic measures of informativeness to select samples for labeling. These heuristics often fail to capture the true informativeness of data points, leading to suboptimal performance. For instance, the paper "Active Label Refinement for Semantic Segmentation of Satellite Images" [22] proposes the use of active learning to refine labels obtained through low-cost means. However, the effectiveness of such refinement heavily depends on the quality and relevance of the heuristic measures used to identify segments requiring re-labeling. This limitation underscores the challenge of designing robust and accurate informativeness criteria that can reliably guide the labeling process in complex remote sensing tasks.

Another critical drawback is the difficulty in handling imbalanced datasets, a common scenario in remote sensing applications. Many current active learning techniques are poorly equipped to manage class imbalances effectively. For example, the paper "Region-level Active Detector Learning" [6] introduces a region-level approach to promote spatial diversity and minimize context switching for labelers. While this method shows promise in enhancing rare object search on realistic data, it does not inherently address class imbalance, which can severely impact the performance of active learning algorithms. Similarly, the paper "Deep Active Learning for Multi-Label Classification of Remote Sensing Images" [29] explores several query functions designed to assess multi-label uncertainty and diversity. However, these functions may struggle to maintain balanced representation across all classes in highly imbalanced datasets, thereby limiting their effectiveness.

Furthermore, many active learning methods assume that all samples are equally accessible for labeling, which is often not the case in remote sensing. Physical constraints and logistical challenges in acquiring labels for remote sensing data can make certain samples much harder to label than others. For example, the paper "Active learning for object detection in high-resolution satellite images" [7] highlights the difficulties in labeling high-resolution satellite images, especially those covering large geographic areas. These challenges underscore the need for a more nuanced approach to active learning that considers the accessibility and feasibility of labeling different samples.

Additionally, current active learning strategies often overlook the temporal dynamics and contextual changes present in remote sensing data. Remote sensing data frequently captures changes over time, such as seasonal variations or dynamic events like urban expansion or deforestation. Existing active learning methods typically focus on static snapshots of data, potentially missing opportunities to learn from temporally evolving patterns. For instance, the paper "Geographical Knowledge-driven Representation Learning for Remote Sensing Images" [5] emphasizes the importance of geographical knowledge in representation learning. However, this approach does not explicitly account for temporal changes, indicating a need for methods that can adapt to and leverage temporal dynamics in active learning scenarios.

Moreover, the scalability and computational demands of active learning in remote sensing pose significant barriers to widespread adoption. High-resolution satellite images can contain millions of pixels, making the computation of informativeness scores and the iterative refinement of models computationally intensive. The paper "Benchmarking Multi-Domain Active Learning on Image Classification" [40] evaluates various active learning strategies across different datasets but does not address scalability concerns, which become particularly acute with large volumes of high-resolution remote sensing data. This limitation highlights the need for more efficient and scalable algorithms capable of handling big data in remote sensing applications.

Finally, the integration of domain adaptation techniques to enhance active learning in remote sensing remains underexplored. Domain adaptation aims to improve the transferability of models across different domains, crucial given the variability in remote sensing data due to factors like sensor type, geographic location, and imaging conditions. The paper "Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning" [41] investigates the application of domain adaptation to geospatial machine learning tasks. Although this work demonstrates the potential benefits of domain adaptation, it does not directly address how such techniques can be integrated into active learning frameworks. Bridging this gap could lead to more robust and versatile active learning systems that can adapt to a wide variety of remote sensing scenarios.

In conclusion, while active learning holds great promise for improving the efficiency and effectiveness of remote sensing image classification, current approaches are constrained by several limitations. Addressing these challenges—such as reliance on heuristic informativeness measures, handling class imbalances, considering temporal dynamics, and ensuring scalability—will be crucial for realizing the full potential of active learning in remote sensing. Future research should focus on developing more sophisticated and adaptive strategies to overcome these limitations, paving the way for more impactful and efficient use of active learning in remote sensing applications.

### 6.3 Opportunities for Transfer Learning and Synthetic Data Generation

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[42]

Addressing the challenge of limited labeled data in remote sensing image classification poses significant hurdles, yet offers exciting opportunities for innovation through the integration of transfer learning and synthetic data generation. These techniques not only enhance model robustness and performance but also mitigate the dependency on extensive labeled datasets, which are often scarce and costly to obtain in remote sensing.

**Transfer Learning**

Transfer learning, a technique where a model trained on one task is adapted to perform another related task, is particularly valuable in reducing the dependency on large, labeled datasets. In remote sensing, this approach can significantly alleviate the scarcity of labeled data by leveraging pre-trained models on related domains or datasets. Aggregative self-supervised feature learning from a limited sample and SMART self-supervised multi-task pretraining with control transformers highlight the benefits of combining multiple proxy tasks and self-aggregation techniques to enhance robustness and performance in remote sensing image classification. By utilizing pre-existing knowledge, these models can generalize better to new tasks, thereby reducing the need for extensive labeling.

However, the success of transfer learning in remote sensing hinges on the similarity between source and target tasks. While the transferability of knowledge is advantageous, discrepancies in data distributions, environmental conditions, and sensor modalities may hinder direct application. Furthermore, the choice of pre-training objectives and the extent of adaptation required post-transfer can influence the effectiveness of the approach. As highlighted in the review of advanced active learning strategies, careful consideration of these factors is essential for successful deployment.

**Synthetic Data Generation**

Synthetic data generation offers an alternative solution by creating realistic yet controlled datasets. Unlike transfer learning, which relies on existing data, synthetic data generation enables the creation of tailored datasets that reflect specific conditions or scenarios. This is particularly beneficial in remote sensing, where acquiring large volumes of labeled data can be logistically challenging and expensive. Techniques such as generative adversarial networks (GANs) and simulation tools can synthesize high-resolution satellite images, allowing researchers to simulate various environmental conditions, weather patterns, and geographic features. This approach not only augments the available data but also introduces variability that can improve model generalization.

The application of synthetic data generation in remote sensing has been explored in various contexts. For instance, the generation of synthetic images for training object detection models demonstrates the utility of synthetic data in addressing the challenge of limited labeled data. By simulating a variety of scenarios, these models can learn to detect objects under diverse conditions, enhancing their robustness and reliability.

Despite its promise, synthetic data generation also comes with its own set of challenges. Ensuring the realism and variability of the generated data is crucial for maintaining the quality of the training process. Over-reliance on synthetic data can lead to overfitting, as models may become too accustomed to the synthetic conditions rather than adapting to real-world variations. Additionally, the complexity and computational demands of generating high-quality synthetic data can be substantial, requiring significant investment in both hardware and expertise.

**Combining Transfer Learning and Synthetic Data Generation**

The most promising avenue for overcoming the limitations of labeled data may lie in the synergistic application of transfer learning and synthetic data generation. By leveraging pre-trained models through transfer learning, the initial stages of model training can benefit from a wealth of prior knowledge. Subsequently, synthetic data generation can be employed to create a diverse and realistic training environment, ensuring that the model remains adaptable and robust. This dual approach not only addresses the scarcity of labeled data but also enhances the model's capacity to generalize across different conditions.

For instance, in the context of active learning for object detection in high-resolution satellite images, the integration of transfer learning and synthetic data generation could significantly reduce the labeling effort required for achieving high-performance models. Pre-trained models could be fine-tuned on synthetic data generated to mimic specific conditions, such as varying weather patterns or urban development stages. This would enable the model to learn more efficiently, requiring fewer labeled examples to achieve comparable or even superior performance.

Moreover, the use of synthetic data in conjunction with active learning strategies could further enhance the efficiency of the training process. By selecting the most informative samples for labeling based on synthetic data simulations, the active learning algorithm can prioritize data that offer the greatest potential for improving model performance. This targeted approach, combined with the robust feature extraction capabilities of pre-trained models, can lead to faster convergence and improved accuracy.

In conclusion, while transfer learning and synthetic data generation offer promising solutions to the challenge of limited labeled data in remote sensing, their effective application requires careful consideration of the underlying principles and potential limitations. Transfer learning provides a mechanism for leveraging existing knowledge, whereas synthetic data generation enhances the richness and variability of the training set. Together, these approaches present a powerful framework for advancing the field of remote sensing image classification, enabling more efficient and accurate model development in the face of data scarcity.
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### 6.4 Integration of Advanced Techniques

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Integrating advanced techniques such as self-supervised learning and reinforcement learning into active learning frameworks presents a promising avenue for enhancing the performance and adaptability of models in remote sensing. These techniques offer novel solutions to address the inherent challenges of active learning in remote sensing, particularly in dealing with the scarcity of labeled data and the complexity of high-dimensional data spaces.

**Self-Supervised Learning in Active Learning**

Self-supervised learning (SSL) has emerged as a powerful tool to mitigate the reliance on large volumes of labeled data by enabling the extraction of meaningful features from unlabeled data [24]. SSL leverages self-supervised pretext tasks to learn robust representations, which can be fine-tuned for downstream tasks with limited labeled data. In the context of active learning, SSL can be employed to enhance the informativeness of the selected samples and improve the robustness of the model.

For instance, [24] demonstrates the effectiveness of using SSL in conjunction with active learning for image classification and segmentation tasks. The authors propose a novel approach that integrates a simple self-supervised pretext task, such as rotation prediction, to sort unlabeled data based on their loss values. This enables the active learning framework to focus on the most challenging samples, leading to improved model performance on various benchmarks. Furthermore, the integration of SSL helps to address the cold-start problem, where initial performance heavily relies on random initialization of labeled sets.

Another notable study, [37], explores the synergy between active learning and SSL in reducing labeling effort. The authors investigate whether SSL can complement active learning to enhance model performance. They find that SSL significantly improves the efficiency of active learning, particularly in scenarios with limited labeled data. By leveraging SSL for feature extraction, active learning can more effectively identify informative samples, thereby accelerating the learning process.

**Reinforcement Learning in Active Learning**

Reinforcement learning (RL) represents another advanced technique that can augment active learning strategies in remote sensing. RL enables agents to learn optimal policies through trial-and-error interactions with an environment, making it particularly suitable for dynamic and uncertain scenarios. Integrating RL into active learning can facilitate adaptive decision-making processes, where the model learns to select the most beneficial samples for labeling based on feedback from previous iterations.

[43] suggests that the integration of RL with active learning can potentially enhance the adaptability of models in complex environments. The authors propose a framework that combines self-supervised pretraining, active learning, and consistency-regularized self-training. Although their findings indicate that self-supervised pretraining significantly boosts semi-supervised learning performance, particularly in few-label settings, the study highlights the potential for RL to refine active learning strategies over time. By incorporating RL, active learning models can dynamically adjust their query strategies based on real-time feedback, thereby optimizing the selection of informative samples.

Moreover, [34] introduces a metacognitive reinforcement learning framework designed to ensure safety and optimize performance in autonomous systems. This framework can be adapted to remote sensing applications by enabling active learning models to adapt their behavior based on environmental conditions and uncertainties. For instance, in scenarios involving change detection or tracking moving objects in satellite imagery, RL can help the model learn to prioritize samples that are most likely to contain critical changes, thereby improving the efficiency of the learning process.

**Ensemble Methods and Active Learning**

The integration of ensemble methods into active learning strategies represents yet another avenue for enhancing the robustness and performance of models in remote sensing. Ensemble methods combine multiple models to improve generalization and reduce overfitting, offering a way to integrate diverse perspectives and enhance decision-making processes. In the context of active learning, ensemble methods can be employed to aggregate multiple predictions and uncertainties, providing a more comprehensive assessment of the informativeness of unlabeled samples.

[44] discusses the use of ensemble methods to enhance the robustness of models against biases introduced during self-labeling processes. The authors argue that ensemble methods can be leveraged in active learning frameworks to improve the reliability of predictions and the identification of informative samples. By aggregating predictions from multiple models, active learning algorithms can better account for the inherent uncertainties in high-dimensional data spaces, leading to more accurate and robust models.

Additionally, [45] explores the integration of probabilistic logic and deep learning to automate labeling processes and enhance model interpretability. This approach can be extended to active learning by incorporating probabilistic logic to guide the selection of samples that are most informative for the downstream task. By leveraging probabilistic models, active learning algorithms can incorporate domain-specific knowledge and uncertainties, facilitating more informed decision-making processes.

**Challenges and Considerations**

Despite the promising potential of integrating advanced techniques into active learning frameworks, there are several challenges and considerations that must be addressed. One major concern is the computational demand associated with these techniques, particularly in the context of remote sensing where data sizes can be enormous. Ensuring that these methods remain computationally feasible and scalable is crucial for their practical deployment.

Another challenge lies in the interpretability and explainability of models enhanced by these advanced techniques. As active learning algorithms become increasingly sophisticated, there is a growing need for transparency and accountability in decision-making processes. Researchers and practitioners should strive to develop methods that not only improve performance but also maintain or enhance interpretability.

Finally, the integration of advanced techniques should be carefully evaluated to ensure that they do not introduce biases or skew class distributions. For example, [44] highlights the potential for naive application of SSL to introduce biases towards certain classes. Therefore, it is essential to implement mechanisms that mitigate such biases and ensure fair and balanced model performance.

**Future Directions**

Moving forward, there is a need for continued research into the integration of advanced techniques with active learning in remote sensing. This includes exploring novel architectures and methodologies that can seamlessly incorporate SSL, RL, and ensemble methods into active learning frameworks. Additionally, efforts should be directed towards developing more efficient algorithms that can handle large-scale datasets while maintaining computational feasibility. Moreover, future research should focus on enhancing the interpretability and explainability of models, ensuring that they remain transparent and accountable in decision-making processes.

By addressing these challenges and pursuing these future directions, the integration of advanced techniques such as SSL, RL, and ensemble methods holds significant promise for enhancing the performance and adaptability of active learning models in remote sensing. This will contribute to the broader goal of advancing the field of remote sensing image classification.
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### 6.5 Future Research Directions

Future research in active learning for remote sensing can be directed towards several innovative areas, each aimed at addressing existing limitations and pushing the boundaries of current methodologies. These areas include the development of more sophisticated algorithms, the exploration of federated learning frameworks, and the advancement of interpretability and explainability in deep learning models. Each direction offers unique opportunities to enhance the performance, flexibility, and transparency of active learning systems in the context of remote sensing.

One promising avenue for future research involves refining active learning algorithms to better handle the complexities inherent in remote sensing datasets. For instance, addressing extreme class imbalance is essential, as satellite images often predominantly represent a single or a few predominant classes, making minority classes difficult to detect and classify [15]. Researchers could focus on creating more nuanced approaches to select informative samples, ensuring a balanced representation of all classes throughout the training process. This could involve developing algorithms that dynamically adjust sampling thresholds based on current class distributions, thereby promoting a more equitable learning environment.

Additionally, integrating reinforcement learning (RL) with active learning can significantly enhance the adaptability and robustness of these systems. By dynamically adjusting reward functions based on the model’s performance and the informativeness of selected samples, RL can guide the active learning process towards more efficient and effective training. This could involve designing RL agents that learn to optimize the selection of samples for labeling, taking into account factors such as class distribution, sample diversity, and the model's current learning state. For example, employing a meta-cognitive RL framework could ensure that the active learning process is both safe and efficient.

Another key direction for future research is applying federated learning frameworks to remote sensing tasks. Federated learning enables collaborative learning among distributed devices or organizations while keeping data decentralized. In remote sensing, this could create more robust models by leveraging data from various satellites and sensors without requiring centralized storage of potentially sensitive or proprietary data. Federated active learning could further refine this approach by selectively requesting labels from data owners based on the informativeness of the samples, thereby optimizing resource usage and privacy preservation. However, challenges such as ensuring consistency across different data sources and maintaining the integrity of the learning process in a federated setting remain.

Furthermore, advancing the interpretability and explainability of deep learning models used in active learning is crucial for building trust and facilitating broader adoption. Techniques such as attention mechanisms, saliency maps, and counterfactual explanations can elucidate how models make decisions, particularly in active learning contexts. For example, attention mechanisms can highlight which parts of the input data are most influential in the model's predictions, aiding in the identification of the most informative samples for labeling. Additionally, methods like SHAP or LIME can provide actionable insights into the model's behavior, contributing to more transparent and accountable active learning systems.

Moreover, synthetic data generation techniques hold great promise for addressing the challenge of limited labeled data in remote sensing. Techniques like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) can generate realistic synthetic data to augment training sets and reduce reliance on scarce labeled data. However, controlling the quality and diversity of generated data is crucial to avoid introducing biases or inconsistencies. For instance, "VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification" shows how variational autoencoders can generate high-quality nodes for minority classes, providing a starting point for generating realistic synthetic data for remote sensing applications.

Additionally, integrating domain-specific knowledge and prior information into active learning algorithms can further enhance their performance and applicability. Incorporating geographical information or contextual knowledge about the region of interest can guide the selection of informative samples, leading to more targeted and effective learning. This aligns with geography-aware self-supervised learning, where spatio-temporal structures are exploited to improve learning outcomes [19]. Leveraging such information makes active learning systems more contextually aware, improving their ability to generalize to new, unseen data.

Finally, exploring ensemble methods in active learning for remote sensing represents another exciting area for future research. Ensemble methods, combining multiple models to improve robustness and accuracy, can be adapted to enhance model performance. For example, ensembling multiple self-supervised pre-trained models could lead to more robust feature representations, improving the informativeness of selected samples. Similarly, ensemble strategies could balance the trade-off between exploration and exploitation in the active learning process, ensuring both informative and diverse samples are selected for labeling.

These research directions not only address current limitations but also open up new possibilities for leveraging active learning to unlock the full potential of remote sensing data. As technology evolves, integrating these innovations will undoubtedly pave the way for more efficient, reliable, and transparent active learning systems in remote sensing.


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