Contrastive Graph Convolution Network With Skip Connections for Few-Shot Hyperspectral Image Classification

Graph convolution networks (GCNs) have achieved impressive results for few-shot hyperspectral image classification. However, current methods focus on migrating labels from support patches to query patches through graph convolution. Less attention has been paid to the patch features generated by graph convolution. This leads to that the methods can’t perceive the abnormal situation that the labels of two patches from different classes are correctly predicted but their features are also similar with each other. Moreover, current methods usually adopt multiple layers of graph convolution for label migration and have not distinguished the impact of different layers. This may result in that important information are submerged in the iterative convolution process. To address these issues, this paper proposes a Contrastive Graph Convolution Network with Skip Connection for few-shot hyperspectral image classification (CGCN-SC). By following the supervised contrastive learning, the contrastive loss modules are proposed to constrain the relationship between the generated features of the patches. It is expected that while migrating labels from labeled patches to unlabeled ones, the generated features of patches from the same classes are close with each other and the features from different classes are far away. Moreover, the skip connection is proposed to be incorporated into the graph convolution network to allow high-level abstract information to be combined with low-level features that may be lost during graph convolution. Through extensive experiments, the proposed method has shown better performance when compared with a set of related methods. All the codes are available at github https://github.com/dongao11/CGCN.