Hypergraph-Regularized Low-Rank Subspace Clustering Using Superpixels for Unsupervised Spatial-Spectral Hyperspectral Classification

Low-rank subspace representations have been observed to be well-suited to hyperspectral imagery, which tends to have a global structure composed of a small number of ground-cover signatures, and additional graph-based regularization can further incorporate local information. However, in the context of unsupervised classification, existing approaches typically limit consideration to simple graphs b...