Attention-Based Contrastive Learning for Few-Shot Remote Sensing Image Classification

Few-shot remote sensing image classification entails identifying images using a limited set of labeled data within remote sensing scenes, holding significant theoretical and practical implications. However, owing to the intricacy and variety of remote sensing images, traditional classification methods usually struggle to extract effective features and learn robust classifiers. To address this issue, an end-to-end metric learning framework named attention-based contrastive learning network (ACL-Net) is introduced in this article. Specifically, the attention-based feature optimization (ABFO) module is employed to align and enhance target image features, highlighting the target region and strengthening the network's feature extraction capability. In addition, the dictionary-based contrastive loss (DBCL) module is assigned to optimize image feature vectors, improving category distinguishability and consequently enhancing classification accuracy. The experimental results on five publicly available few-shot remote sensing classification datasets demonstrate the high competitiveness of our proposed method. Furthermore, it illustrates superior classification accuracy compared to other pertinent few-shot learning algorithms in the five-way one-shot scenario.