Hyperspectral image classification using spectral-spatial hypergraph convolution neural network

Zhongtian Ma; Zhiguo Jiang; Haopeng Zhang

doi:10.1117/12.2599787

12 September 2021 Hyperspectral image classification using spectral-spatial hypergraph convolution neural network

Zhongtian Ma, Zhiguo Jiang, Haopeng Zhang

Proceedings Volume 11862, Image and Signal Processing for Remote Sensing XXVII; 118620I (2021) https://doi.org/10.1117/12.2599787
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

Deep learning methods, especially convolutional neural networks(CNN), have been widely used in hyperspectral image(HSI) classification. Recently, graph convolutional networks (GCN) have shown great potential in HSI classification problem. However, the existing GCN-based methods have several problems. First, the existing methods rely too much on the adjacency matrix, which cannot be changed during training. Furthermore, most of them can only use a single kind of feature, and fail to extract the spectral-spatial information from the HSI. Finally, for the existing GCN-based methods, it is difficult to achieve the same accuracy as the mature CNN methods. In this paper, we propose a spectral-spatial hypergraph convolutional neural network (S²HCN) for HSI classification. Compared with the existing GCN-based methods, S²HCN has the following advantages. Different from the adjacency matrix that is fixed during training of GCN, S²HCN can dynamically update the weight of the hyperedge during training, which reduces the reliance on prior information to a certain extent. In addition, S²HCN generates hyperedges from the spectral and spatial features independently, and adopts the incidence matrix composed of all hyperedges to replace the adjacency matrix in GCN. In this way, the spectral and spatial features can be better integrated. Finally, compared to a simple graph structure, the hypergraph structure can express the high-dimensional relationships in the data, which is beneficial to classification problems. Sufficient experiments on two popular HSI datasets have proved the effectiveness of S²HCN.

Conference Presentation

Citation Download Citation

Zhongtian Ma, Zhiguo Jiang, and Haopeng Zhang "Hyperspectral image classification using spectral-spatial hypergraph convolution neural network", Proc. SPIE 11862, Image and Signal Processing for Remote Sensing XXVII, 118620I (12 September 2021); https://doi.org/10.1117/12.2599787

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