Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913251871162368 |
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| author | Li, Xianju Guan, Renxiang Li, Zihao Liu, Hao Yang, Jing |
| author_facet | Li, Xianju Guan, Renxiang Li, Zihao Liu, Hao Yang, Jing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_01465 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks Li, Xianju Guan, Renxiang Li, Zihao Liu, Hao Yang, Jing Computer Vision and Pattern Recognition 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. |
| title | Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.01465 |