A Multi-Level Label-Aware Semi-Supervised Framework for Remote Sensing Scene Classification

Qiang Liu, Min He, Yang Kuang, Linshan Wu, Jun Yue, Leyuan Fang

Abstract: Semi-supervised learning (SSL) is a promising approach to reduce the labeling burden in remote sensing scene classification tasks. However, most semi-supervised methods typically exploit the single-level semantic information of unlabeled data, ignoring the multi-level semantic structure prevalent in remote sensing data. The multi-level semantic structure, which contains the correlation of different categories and the multi-granularity semantic information, can help the scene classification model to more accurately measure the feature distance between different categories and more effectively utilize unlabeled data. Therefore, this article proposes a multi-level label-aware (MLLA) semi-supervised scene classification framework, MLLA, which extends the semantic information captured in unlabeled data from single-level to multi-level to improve the scene classification performance. Specifically, we first propose a multi-level prototype awareness module to capture the multi-level semantic structure underlying remote sensing data. Then, based on this structure, a multi-level pseudo-label generation module is designed to assign multi-level pseudo-labels to the unlabeled data. Finally, by combining the labeled samples and the multi-level pseudo-labeled samples, the scene classification model is progressively trained. The experimental results on three benchmark datasets show that the proposed MLLA achieves excellent performance compared to other semi-supervised classification methods.