Boosting Few-Shot Hyperspectral Image Classification Through Dynamic Fusion and Hierarchical Enhancement

Ying Guo, Bin Fan, Yuchao Dai, Yan Feng, Mingyi He

Abstract: Few-shot learning has garnered increasing attention in hyperspectral image classification (HSIC) due to its potential to reduce dependency on labor-intensive and costly labeled data. However, most existing methods are constrained to feature extraction using a single image patch of fixed size, and typically neglect the pivotal role of the central pixel in feature fusion, leading to inefficient information utilization. In addition, the correlations among sample features have not been fully explored, thereby weakening feature expressiveness and hindering cross-domain knowledge transfer. To address these issues, we propose a novel few-shot HSIC framework incorporating dynamic fusion and hierarchical enhancement. Specifically, we first introduce a robust feature extraction module, which effectively combines the content concentration of small patches with the noise robustness of large patches, and further captures local spatial correlations through a central-pixel-guided dynamic pooling strategy. Such patch-to-pixel dynamic fusion enables a more comprehensive and robust extraction of ground object information. Then, we develop a support–query hierarchical enhancement module that integrates intraclass self-attention and interclass cross-attention mechanisms. This process not only enhances support-level and query-level feature representation but also facilitates the learning of more informative prior knowledge from the abundantly labeled source domain. Moreover, to further increase feature discriminability, we design an intraclass consistency loss and an interclass orthogonality loss, which collaboratively encourage intraclass samples to be closer together and interclass samples to be more separable in the metric space. Experimental results on four benchmark datasets demonstrate that our method substantially improves classification accuracy and consistently outperforms competing approaches. Code is available at https://github.com/guoying918/DFHE2025

External IDs:doi:10.1109/tnnls.2025.3615950