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.

翻译：跨域小样本高光谱图像分类旨在从源域中大量标注样本中学习先验知识，并将其迁移至目标域中仅包含少量标注样本的任务中。基于度量学习的方法中，当前许多方法首先提取查询样本和支持样本的特征，然后根据查询样本与支持样本或原型之间的距离直接预测其类别。样本间的关系尚未得到充分挖掘与利用。与现有工作不同，本文提出从不同视角学习样本关系并将其融入模型学习过程，以提升跨域小样本高光谱图像分类性能。在现有采用域判别器处理域级分布差异的DCFSL方法基础上，本文方法应用对比学习来学习类级样本关系，从而获得更具判别性的样本特征。此外，采用基于Transformer的交叉注意力学习模块来学习集合级样本关系，并获取查询样本对支持样本的注意力权重。实验结果表明，与最先进方法相比，多视角关系学习机制对小样本高光谱图像分类具有显著贡献。