Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering

arXiv:2404.05211v1 Announce Type: new
Abstract: 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 …

abstract arxiv clustering complexity cs.cv data global graph image performance shows study type