  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.
