Abstract

In this paper, a graph-based nonlocal total variation method is proposed for unsupervised classification of hyperspectral images. The variational problem is solved by the primal-dual hybrid gradient 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, and the graph-based Merriman-Bence-Osher scheme.