Hypergraph Contrastive Learning for Large-Scale Hyperspectral Image Clustering

Large-scale hyperspectral image (HSI) clustering has become an important research task owing to its promising applications in various fields. Recently, beneficial from the correlation modeling capability of graphs, graph contrastive learning methods have received increasing attention in the clustering task. However, these methods usually have limited ability to explore the high-order correlation as well as beneficial clustering information of large-scale HSI, thus limiting the clustering performance on large-scale HSI. To this end, a novel hypergraph contrastive learning network (HCL-Net) for large-scale HSI clustering is proposed in this paper. Specifically, a diffusion hypergraph-based contrastive clustering mechanism is presented, in which a diffusion hypergraph is constructed to model the high-order correlation in large-scale HSI, thus guiding contrastive learning for obtaining more discriminative representations. Besides, by mining the confident clustering information, a confidence-guided positive-negative updating strategy is designed to dynamically update positives and negatives for contrastive learning, thereby obtaining a more compact clustering structure. The proposed method is evaluated on three public large-scale HSI datasets. The experimental results have demonstrated the superior performance of the proposed HCL-Net over state-of-the-art methods.