Cross-Domain Few-Shot Hyperspectral Classification with Feature Enhancement and Loss Optimization

Cross-domain few-shot learning has attracted increasing attention in hyperspectral image classification. However, in scenarios with scarce annotations and significant domain shifts, existing methods struggle to simultaneously adapt to the inherent spectral-spatial coupling of hyperspectral data while maintaining high-dimensional data-processing efficiency and balancing the learning of few-shot and cross-domain hard samples. This study proposes a collaborative optimization scheme based on improvements to the cross-domain few-shot learning with cross-modal alignment and supervised contrastive learning method. Specifically, an enhanced SimAM module dynamically weights spectral-spatial features to strengthen discriminative information of ground objects and mitigate domain shifts. A Channel Relation Attention module is introduced to capture channel-spatial dependencies and adaptively select key channels, improving high-dimensional feature processing efficiency. Moreover, a weighted joint loss function combining cross-entropy and focal loss is constructed, with class weights adjusted according to the joint distribution of source and target domains to balance sample learning priorities. Experiments on three benchmark hyperspectral data sets demonstrate that the proposed method achieves significant improvements in classification accuracy, robustness, and cross-domain adaptability.