Few-Shot Hyperspectral Image Classification Based on Prototype Mask Contrast and Cross-Stage Feature Refinement

Recently, cross-domain few-shot learning (FSL) has achieved remarkable performance in hyperspectral image classification (HSIC). However, current prototype-based FSL methods overlook the redundancy between prototypes, which may lead to intra-domain redundancy dependence and interfere with the effective alignment of data distributions between the source domain (SD) and target domain (TD). To address these issues, we propose a prototype mask contrast and cross-stage feature refinement (PMC-CFR) method for few-shot HSIC. Specifically, a neighbor-resistant multidimensional feature fusion network (NRMFF) is designed to fuse features inclined toward different dimensional information after random neighboring-pixel removal. Next, the parameter-free adaptive mask generator (PAMG) identifies potential redundant components based on the statistical distribution of intra-domain prototypes to form negative pairs, while treating other components as key features to form positive pairs. Contrastive pairs are further processed through graph convolution (GC) for supervised contrastive learning (SCL), which mitigates the dependence on intra-domain redundancy and enhances the learning of key features. To extract more representative features, multi-level representations are formed depending on whether the shared multilayer perceptron (MLP) is applied to focus on key components in each domain by fitting positive pairs. Meanwhile, the directional calibration (DC) separates inter-class prototypes across levels by balancing direction and distance. The synergy between MLP and DC in hierarchical feature modeling and semantic separation enables cross-stage feature refinement. Experimental results on four public HSI datasets demonstrate that the PMC-CFR outperforms many advanced contrastive methods, and feature visualizations further support its effectiveness in cross-domain feature distribution alignment.