Feature Fusion via Deep Residual Graph Convolutional Network for Hyperspectral Image Classification

Recently, graph convolutional network (GCN) has been applied for hyperspectral image (HSI) classification and obtained better performance. The main issue in HSI classification is that the high-resolution HSI contains more complex spectral-spatial structure information. However, the previous GCN-based methods applied in HSI classification only adopted a shallow GCN layer and they cannot extract the deeper discriminative features. In addition, these methods ignored the complementary and correlated information among multiorder neighboring information extracted by multiple GCN layers. In this letter, a novel feature fusion via deep residual GCN is proposed to explore the internal relationship among HSI data. On the one hand, benefiting from residual learning to alleviate the over-smoothing problem, we can construct deep GCN layers to excavate deeper abstract features of HSI. On the other hand, we fuse the outputs of different GCN layers, and thus, the local structural information within multiorder neighborhood nodes can be fully utilized. Extensive experiments on four real HSI datasets, including Indian Pines, Pavia University, Salinas, and Houston University, demonstrate the superiority of the proposed method compared with other state-of-the-art methods in various evaluation criteria.