Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total   Variation and Primal-Dual Hybrid Gradient Algorithm
  In this paper, a graph-based nonlocal total variation method (NLTV) is proposed for unsupervised classification of hyperspectral images (HSI). The variational problem is solved by the primal-dual hybrid gradient (PDHG) algorithm. By squaring the labeling function and using a stable simplex clustering routine, an unsupervised clustering method with random initialization can be implemented. The effectiveness of this proposed algorithm is illustrated on both synthetic and real-world HSI, and numerical results show that the proposed algorithm outperforms other standard unsupervised clustering methods such as spherical K-means, nonnegative matrix factorization (NMF), and the graph-based Merriman-Bence-Osher (MBO) scheme. 
Wei Zhu, Victoria Chayes, Alexandre Tiard, Stephanie Sanchez, Devin   Dahlberg, Andrea L. Bertozzi, Stanley Osher, Dominique...
cs.CV