SCL-MLNet: Boosting Few-Shot Remote Sensing Scene Classification via Self-Supervised Contrastive Learning

Few-shot classification aims at recognizing novel categories from low data regimes based on prior knowledge. However, the existing methods for few-shot scene classification have limitations on using few annotated data and do not fully consider the intra-class samples with classification targets in different sizes, which lead to poor feature representation. To address these problems, this study introduces an end-to-end framework called self-supervised contrastive learning-based metric learning network (SCL-MLNet) for few-shot remote sensing (RS) scene classification. On one hand, we weave self-supervised contrastive learning into few-shot classification algorithms through multi-task learning, enabling feature extractors to learn representative image features from few annotated samples. Moreover, we devise a new loss function to train the proposed model end-to-end and speed up the convergence of the model. On the other hand, considering the differences between intra-class samples, we introduce a novel attention module embedded in the feature extractor to fuse multi-scale spatial features from the classification targets in different sizes. In our experiments, SCL-MLNet is evaluated on three public benchmark datasets. The results demonstrate that SCL-MLNet achieves state-of-the-art performance for few-shot remote sensing scene classification.