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Open AccessArticle

Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

1
School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 2897; https://doi.org/10.3390/rs11242897
Received: 14 October 2019 / Revised: 22 November 2019 / Accepted: 3 December 2019 / Published: 4 December 2019
(This article belongs to the Section Remote Sensing Image Processing)
Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis. To further improve the unmixing performance, in this paper, a nonlocal low-rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. The proposed method is based on a fact that hyperspectral images have self-similarity in nonlocal sense and smoothness in local sense. To explore the spatial self-similarity, nonlocal cubic patches are grouped together to compose a low-rank matrix. Then, based on the linear mixed model framework, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively. Finally, the results of the experiments on two simulated data sets and two real data sets show that the proposed algorithm produces better performance than other state-of-the-art algorithms.
Keywords: hyperspectral images; sparse unmixing; nonlocal self-similarity; low-rank hyperspectral images; sparse unmixing; nonlocal self-similarity; low-rank
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Zheng, Y.; Wu, F.; Shim, H.J.; Sun, L. Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior. Remote Sens. 2019, 11, 2897.

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