Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domain and then transferring the knowledge to the tasks which contain only few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations from different views and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current DCFSL method which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applys contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attentions from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-view relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods.

本文提出了一种基于多视角关系学习的跨域少样本高光谱图像分类方法，通过学习样本之间的关系来改善跨域少样本高光谱图像分类，并结合对比学习和基于Transformer的跨注意力学习模块来获得更可区分的样本特征。实验证明，与现有方法相比，多视角关系学习机制对于少样本高光谱图像分类的贡献显著。

跨领域少样本高光谱图像分类的多视图关系学习