Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering
<|reference_start|>Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering: Hyperspectral image (HSI) clustering is a challenging task due to its high complexity. Despite subspace clustering shows impressive performance for HSI, traditional methods tend to ignore the global-local interaction in HSI data. In this study, we proposed a multi-level graph subspace contrastive learning (MLGSC) for HSI clustering. The model is divided into the following main parts. Graph convolution subspace construction: utilizing spectral and texture feautures to construct two graph convolution views. Local-global graph representation: local graph representations were obtained by step-by-step convolutions and a more representative global graph representation was obtained using an attention-based pooling strategy. Multi-level graph subspace contrastive learning: multi-level contrastive learning was conducted to obtain local-global joint graph representations, to improve the consistency of the positive samples between views, and to obtain more robust graph embeddings. Specifically, graph-level contrastive learning is used to better learn global representations of HSI data. Node-level intra-view and inter-view contrastive learning is designed to learn joint representations of local regions of HSI. The proposed model is evaluated on four popular HSI datasets: Indian Pines, Pavia University, Houston, and Xu Zhou. The overall accuracies are 97.75%, 99.96%, 92.28%, and 95.73%, which significantly outperforms the current state-of-the-art clustering methods.<|reference_end|>
@article{wang2024multi-level,
    title={Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image
  Clustering},
    author={Jingxin Wang, Renxiang Guan, Kainan Gao, Zihao Li, Hao Li, Xianju Li,
  Chang Tang},
    journal={arXiv preprint arXiv:2404.05211},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05211},
    primaryClass={cs.CV}
}