Brownian Distance Covariance Prototype Refinement for Few-Shot Multitemporal Coastal Wetland Hyperspectral Image Classification

Coastal wetlands are ecologically vital ecosystems that demand accurate and long-term land cover monitoring. The integration of deep learning with hyperspectral imaging enables detailed classification of coastal wetlands by extracting subtle spectral differences among land cover types. However, newly collected images often cannot be fully annotated in time, and they usually exhibit domain shifts from historical data. This impairs the transferability of models to new data. To address this challenge, we propose a novel framework named Brownian distance covariance prototype refinement based cross-domain few-shot learning (BDCPR-FSL) to transfer knowledge from historical hyperspectral data to newly acquired images with limited annotations. Specifically, a spectral-aware Transformer based on spectral correlation coefficient is introduced to capture subtle spectral differences, particularly in coastal wetland scenes with low inter-class separability. Moreover, Brownian distance covariance (BDC) is employed to capture nonlinear and joint dependencies among features, which are often overlooked by conventional methods relying on marginal statistics. To mitigate prototype deviation caused by sample variability in coastal wetlands, a prototype refinement mechanism is applied to emphasize more representative samples. Furthermore, a domain alignment loss based on Wasserstein distance is incorporated to reduce distribution shifts between domains, while supervised contrastive learning is introduced to enhance class separability. Extensive experiments on multi-temporal satellite hyperspectral images of coastal wetlands show that BDCPR-FSL achieves superior performance compared to existing methods.