Classification via Structure-Preserved Hypergraph Convolution Network for Hyperspectral Image

Graph convolutional network (GCN) as a combination of deep learning (DL) and graph learning has gained increasing attention in hyperspectral image (HSI) classification. However, most GCN methods consider the simple point-to-point structure between two pixels rather than the high-order structure of multiple pixels, which is contradict with the real feature distribution of ground object. And the nonlinear property of HSI also brings challenge for precise structural representation in GCN. To tackle these problems, this work proposes a structure-preserved hyper GCN (SPHGCN). It first builds a multiple neighborhood reconstruction (MNR) model to reveal the essential resemblance of multiple pixels in nonlinear spectral feature space. With the high-order structure, SPHGCN designs the hypergraph convolution operation for irregular feature aggregation among similar pixels from different regions, which achieves more discriminative features from multiple pixel nodes. Meanwhile, a structure preservation layer (SPL) is built to optimize the distribution of convolutional features under the guidance of high-order structure. Moreover, SPHGCN integrates local regular convolution and irregular hypergraph convolution to learn the structured semantic feature of HSI. This strategy breaks the boundary restriction in traditional convolution and aggregates semantic feature across different image patches. Experiments on three HSI datasets indicate that SPHGCN outperforms a few state-of-the-art methods for HSI classification.