| Citation: | DING Y，ZHANG Z L，ZHAO X F，et al. Semi-supervised locality preserving dense graph convolution for hyperspectral image classification[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（12）：3409-3418 （in Chinese）
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The application of graph convolutional network (GCN) to hyperspectral image (HSI) classification is the hotspot and frontier of current research. Nevertheless, the over-smoothing, feature adaptive selection, and calculation complexity issues still exist for the graph convolution network approaches that are now accessible. To circumvent these problems, a superpixel segmentation method to reduce the spatial dimension of the HSI is proposal, which reduces the amount of calculation while preserving the spectral characteristics of the nodes. In addition, the dense structure is adopted to retain the features of the convolution in process, and the problem of excessive smoothing of the graph convolution is settled. Finally, a mechanism for extracting the practical local knowledge produced by each layer of the dense GCN is created using a layer-wise context-aware learning approach. The network realizes end-to-end semi-supervised classification. The experimental results on three real datasets show that the proposed algorithm outperforms the compared state-of-the-art methods on all indices and improves the classification accuracy of HSI.
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