Hardness-Aware Prototypical Contrastive Learning for Hyperspectral Coastal Wetlands Classification

Self-supervised contrastive learning performs well in representing hyperspectral data under limited labels. However, the heterogeneity poses challenges in balancing a fixed temperature to prevent model collapse with accurately identifying hard negatives. In this paper, we propose a hardness-aware prototypical contrastive learning method (HAPC) for hyperspectral coastal wetlands classification. Firstly, we propose a spatial-spectral integration data augmentation method to enhance the construction of positive counterparts. Additionally, we introduce a hardness-aware temperature reweighting strategy to effectively identify challenging negatives and preserve manifold integrity of data. Experiments on two hyperspectral datasets captured by Zhuhai-1 satellite indicate that HAPC can improve feature representation of hyperspectral coastal wetlands. Our code and datasets is available at https://github.com/sakurashine/HAPC.