2024/03/03 by Xianju Li, Li, Xianju, Renxiang Guan +7
Computer Science · Earth and Planetary Sciences · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Remote Sensing and Land Use #Remote-Sensing Image Classification
paper · pdf · doi:10.48550/arxiv.2403.01465
openalex publication_date 2024/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.