PGSMC: Prototype-Guided Supervised Momentum Contrastive Learning for Hyperspectral Cross-Domain Few-Shot Classification

Contrastive learning has recently demonstrated great potential in hyperspectral image few-shot classification. However, conventional methods mainly emphasize instance-level similarity while neglecting class structure, often leading to the separation of intra-class samples. Moreover, the lack of explicit class prototype modeling often leads to ambiguous decision boundaries. To address these issues, this paper proposes a prototype-guided supervised momentum contrastive learning (PGSMC) for hyperspectral cross-domain few-shot classification. PGSMC first designs an asymmetric augmentation module to enhance sample diversity by applying distinct augmentation strategies and encoders to the query and key views. Subsequently, a momentum queue is employed to store historical key features and their corresponding labels. This mechanism enables smooth updates of class-level momentum prototypes and mitigates the limitations imposed by mini-batch training. Finally, a momentum prototype contrastive loss is formulated to guide the model toward class-level feature representations, thereby promoting more discriminative decision boundaries. Overall, PGSMC enables query samples to contrast with more representative momentum prototypes, enhancing inter-class separability and promoting well-defined class boundaries. Extensive experiments on five hyperspectral image datasets demonstrate that PGSMC significantly outperforms existing few-shot learning methods.