Superpixel Graph Contrastive Clustering with Semantic-Invariant Augmentations for Hyperspectral Images

arXiv:2403.01799v1 Announce Type: new
Abstract: Hyperspectral images (HSI) clustering is an important but challenging task. The state-of-the-art (SOTA) methods usually rely on superpixels, however, they do not fully utilize the spatial and spectral information in HSI 3-D structure, and their optimization targets are not clustering-oriented. In this work, we first use 3-D and 2-D hybrid convolutional neural networks to extract the high-order spatial and spectral features of HSI through pre-training, and then design a superpixel graph contrastive clustering (SPGCC) model …

3-d abstract art arxiv clustering cs.cv graph images information optimization semantic sota spatial state targets type work