Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks

• URL: http://arxiv.org/abs/2403.01465v1
• Date: Sun, 3 Mar 2024 10:19:18 GMT
• Title: Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
• Authors: Xianju Li and Renxiang Guan and Zihao Li and Hao Liu and Jing Yang
• Abstract summary: This study proposes a multiview subspace clustering of hy-perspectral images (HSI) based on graph convolutional networks. The model was evaluated on three popular HSI datasets, including Indian Pines, Pavia University, and Houston. It achieved overall accuracies of 92.38%, 93.43%, and 83.82%, respectively, and significantly outperformed the state-of-the-art clustering methods.
• Score: 12.275530282665578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-dimensional and complex spectral structures make clustering of hy-perspectral images (HSI) a challenging task. Subspace clustering has been shown to be an effective approach for addressing this problem. However, current subspace clustering algorithms are mainly designed for a single view and do not fully exploit spatial or texture feature information in HSI. This study proposed a multiview subspace clustering of HSI based on graph convolutional networks. (1) This paper uses the powerful classification ability of graph convolutional network and the learning ability of topologi-cal relationships between nodes to analyze and express the spatial relation-ship of HSI. (2) Pixel texture and pixel neighbor spatial-spectral infor-mation were sent to construct two graph convolutional subspaces. (3) An attention-based fusion module was used to adaptively construct a more discriminative feature map. The model was evaluated on three popular HSI datasets, including Indian Pines, Pavia University, and Houston. It achieved overall accuracies of 92.38%, 93.43%, and 83.82%, respectively and significantly outperformed the state-of-the-art clustering methods. In conclusion, the proposed model can effectively improve the clustering ac-curacy of HSI.

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