Block-Wise Contrastive Few-Shot Learning With Multiview Mixing for Hyperspectral Image Cross-Scene Classification

The cross-scene classification of hyperspectral images (HSI) faces challenges posed by different sensors and various land cover categories, and methods based on the cross-domain few-shot learning (FSL) framework are commonly used to address the challenge. However, current FSL methods struggle with the issue of intradomain inductive bias, where models tend to learn simplistic and potentially erroneous correlations within samples (classes with similar backgrounds are always confused due to simple correlations with the background), leading to poor generalization ability to target domains (TDs). Furthermore, most approaches employ a patch-wise representation learning mechanism, which provides the model with all information within a sample, but inherently limits its representation capacity when dealing with the limited prior information in FSL tasks. To address these issues, we propose a block-wise contrastive FSL (BCFSL) framework. First, a multiview cross-domain mixing strategy enhances spatial diversity and constructs harder samples to mitigate inductive bias. Second, a novel block-wise representation mechanism performs fine-grained feature extraction from local regions, improving generalization. Finally, a multiview spectral reconstruction module preserves essential high-dimensional spectral information during training and assists the block-wise representation in fully leveraging prior knowledge. Extensive experiments on three benchmark HSI datasets validate the effectiveness and superiority of the proposed method.