Foreground–Background Contrastive Learning for Few-Shot Remote Sensing Image Scene Classification

Few-shot learning (FSL) aims to train a model with limited samples for identifying novel category samples. As for remote sensing images, complex backgrounds may lead to large intra-class differences, and the number of labeled samples is quite smaller than that of large datasets, which both influence the classification performance. To solve these issues, a foreground-background contrastive learning (FBCL) is proposed for few-shot remote sensing image scene classification. Specifically, a foreground-background separation module is proposed to separate features between objects and background with supervised contrastive learning, which aims to improve the ability to distinguish foreground and background regions of remote sensing images. Moreover, a channel weight allocator is proposed to balance features of different dimensions, which can take full advantage of remote sensing image information. Experiments on three remote sensing datasets prove that the proposed few-shot method is able to produce superior classification results than other related approaches.