Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks

• URL: http://arxiv.org/abs/2312.06068v1
• Date: Mon, 11 Dec 2023 02:22:10 GMT
• Title: Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
• Authors: Renxiang Guan and Zihao Li and Xianju Li and Chang Tang and Ruyi Feng
• Abstract summary: Subspace clustering is an effective approach for clustering hyperspectral images. In this study, contrastive multi-view subspace clustering of HSI was proposed based on graph convolutional networks. The proposed model effectively improves the clustering accuracy of HSI.
• Score: 14.978666092012856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-dimensional and complex spectral structures make the clustering of hyperspectral images (HSI) a challenging task. Subspace clustering is an effective approach for addressing this problem. However, current subspace clustering algorithms are primarily designed for a single view and do not fully exploit the spatial or textural feature information in HSI. In this study, contrastive multi-view subspace clustering of HSI was proposed based on graph convolutional networks. Pixel neighbor textural and spatial-spectral information were sent to construct two graph convolutional subspaces to learn their affinity matrices. To maximize the interaction between different views, a contrastive learning algorithm was introduced to promote the consistency of positive samples and assist the model in extracting robust features. An attention-based fusion module was used to adaptively integrate these affinity matrices, constructing a more discriminative affinity matrix. The model was evaluated using four popular HSI datasets: Indian Pines, Pavia University, Houston, and Xu Zhou. It achieved overall accuracies of 97.61%, 96.69%, 87.21%, and 97.65%, respectively, and significantly outperformed state-of-the-art clustering methods. In conclusion, the proposed model effectively improves the clustering accuracy of HSI.

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