Separable Deep Graph Convolutional Network Integrated With CNN and Prototype Learning for Hyperspectral Image Classification

Graph convolutional networks (GCNs) have garnered extensive attention in the realm of hyperspectral image (HSI) classification. However, due to the problem of oversmoothing caused by deep GCN, most of the existing GCN-based methods are limited to constructing shallow networks, thus only able to extract superficial features. Moreover, when existing shallow GCNs extend to a more deeper structure, the number of learnable parameters increases linearly, thus leading to poor generalization performance under limited training samples. To address the aforementioned issues, a separable deep GCN integrated with convolutional neural network and prototype learning (SDGCP) is proposed for HSI classification, which can extract effective global structural information of HSI without increasing the number of trainable parameters. Specifically, the spectral and spatial features, adaptively selected by the attention module, are encoded into the structure of a graph by the graph encoder with the assistance of the pixel-to-region mapping obtained from the simple linear iterative clustering (SLIC). Then, a separable deep graph convolution module, composed of feature extraction and deep feature propagation, is adopted to capture the long-range contextual relationships from HSI encoded as graph data, which is combined with locally complementary information extracted by CNN after decoding. Finally, to further boost the performance of classification under limited labeled samples, prototype learning with regularization terms is utilized to enhance the intraclass compactness and interclass separability of feature representations. Extensive experiments on three standard HSI datasets demonstrate the superiority of the proposed SDGCP over the state-of-the-art (SOTA) methods.