Heterogeneous Prototype Distillation With Support-Query Correlative Guidance for Few-Shot Remote Sensing Scene Classification

Few-shot remote sensing scene classification (FSRSSC) aims to identify unseen classes only relying on very limited training samples. However, scarce training samples are insufficient to support a robust classwise representation, which is easily influenced by agnostic biases from diverse testing scenarios. Fortunately, there are abundant spatial contextual clues that exist in very limited training samples to have an enormous potential to establish discriminative and transferable concepts. Thus, in this article, a hybrid architecture called ProtoConViT is proposed to learn a powerful classwise representation based on spatial contextual clues for FSRSSC promotion. First, support-query correlative guidance is designed to generate more stable spatial connections among support and query data based on intermediate convolution neural network (CNN) feature maps, which not only can be embedded into each episodic training task to reduce redundant spatial contextual representation learning space of vision transformer (ViT) but also can assist it in rapidly capturing critical spatial contextual clues to classify query data into one of classes from support set. Second, followed by the designed support-query correlative guidance, a novel heterogeneous prototype distillation is proposed to integrate the advantages of CNN and ViT for heterogeneous prototype construction, which can rapidly set up discriminative and transferable concepts for FSRSSC. Third, corresponding to the proposed ProtoConViT, a joint loss is designed to make the model rapid convergence based on meta-learning. Finally, extensive experiments are carried out on three FSRSSC benchmarks, and comparative results indicate that the proposed ProtoConViT can achieve a superior FSRSSC performance.