Pixel- And Patch-Wise Context-Aware Learning with CNN and GCN Collaboration for Hyperspectral Image Classification

Graph convolutional network (GCN) gains increasing attention in the hyperspectral image (HSI) classification by the ability to flexibly capture arbitrarily irregular objects. However, due to expensive computation, the graph construction is usually based on superpixel-wise nodes, which ignore the subtle pixel-wise features. In contrast, the convolution neural network (CNN) can mine pixel-wise spectral-spatial features but is limited to capturing local features in small square windows. In this paper, we design a new CNN and GCN collaborative network to simultaneously introduce pixel- and patch-wise contextual information. Concretely, we use the depthwise separable convolution to perform pixel-wise local feature extraction. To further mine the long-range contextual information between land covers, we concatenate a GCN. Finally, we further fuse the complementary features and decode them to obtain the classification map. Extensive experiments reveal that our method achieves competitive performance.