Cross-Domain Hyperspectral Image Classification Based on Graph Convolutional Networks

Abstract

A major challenge in hyperspectral image (HSI) classification is the small-sample-size problem. Cross-domain information can help solve the problem. In cross-domain HSI classification, the source domain has many samples, while the target domain has fewer samples. Transfer learning can transfer knowledge from the source domain to the target domain. The source and target domains are mostly captured by different sensors and thus come from different feature spaces. Heterogeneous transfer learning can solve this problem. This paper proposes a transfer learning method based on a crossdomain graph convolutional network (CD-GCN). A class co-occurrence semantic graph is built between heterogeneous spaces of source and target domains. Then graph convolutional network (GCN) is adopted to learn the features of graphs. To handle the different feature dimensions, a feature alignment subnet is proposed. By combining a feature alignment subnet and a GCN feature extraction subnet, the proposed model CD-GCN transfers knowledge between heterogeneous domains. Experiments on two cross-domain HSI datasets prove that CD-GCN overperforms many transfer learning methods.