Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networks
This work addresses the problem of improving clustering accuracy for hyperspectral images, which is important for remote sensing applications, but it is incremental as it builds on existing subspace clustering and graph convolutional network techniques.
The paper tackled the challenging problem of clustering hyperspectral images by proposing a contrastive multi-view subspace clustering method based on graph convolutional networks, achieving overall accuracies of up to 97.65% on four datasets and significantly outperforming state-of-the-art methods.
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.