Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 55, Issue 5, Pages 2786-2798Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2017.2654486
Keywords
Hyperspectral images (HSI); nonlocal total variation (NLTV); primal-dual hybrid gradient (PDHG) algorithm; stable simplex clustering; unsupervised classification
Categories
Funding
- National Science Foundation [DMS-1118971, DMS-1045536, DMS-1417674]
- Office of Naval Research [N00014-16-1-2119]
- Direct For Mathematical & Physical Scien [1118971, 1417674] Funding Source: National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [GRANTS:13968317, 1440415] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [1417674, 1118971] Funding Source: National Science Foundation
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1045536, GRANTS:13853325] Funding Source: National Science Foundation
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In this paper, a graph-based nonlocal total variation method is proposed for unsupervised classification of hyper-spectral images (HSI). 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.
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