Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total
Variation and Primal-Dual Hybrid Gradient Algorithm
- Author(s): Zhu, W
- Chayes, V
- Tiard, A
- Sanchez, S
- Dahlberg, D
- Bertozzi, AL
- Osher, S
- Zosso, D
- Kuang, D
- et al.
Published Web Locationhttps://doi.org/10.1109/TGRS.2017.2654486
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.
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