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Remote Sens. 2017, 9(11), 1166;

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

1,* , 1,* , 1
Electronic Information School, Wuhan University, Wuhan 430072, China
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
Authors to whom correspondence should be addressed.
Received: 5 October 2017 / Revised: 4 November 2017 / Accepted: 10 November 2017 / Published: 13 November 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different noise levels. To overcome this problem, the noise levels at different bands are assumed to be different in this paper, and a general sparse unmixing method based on noise level estimation (SU-NLE) under the sparse regression framework is proposed. First, the noise in each band is estimated on the basis of the multiple regression theory in hyperspectral applications, given that neighboring spectral bands are usually highly correlated. Second, the noise weighting matrix can be obtained from the estimated noise. Third, the noise weighting matrix is integrated into the sparse regression unmixing framework, which can alleviate the impact of different noise levels at different bands. Finally, the proposed SU-NLE is solved by the alternative direction method of multipliers. Experiments on synthetic datasets show that the signal-to-reconstruction error of the proposed SU-NLE is considerably higher than those of the corresponding traditional sparse regression unmixing methods without noise level estimation, which demonstrates the efficiency of integrating noise level estimation into the sparse regression unmixing framework. The proposed SU-NLE also shows promising results in real HSIs. View Full-Text
Keywords: alternative direction method of multipliers (ADMM); hyperspectral image (HSI); sparse unmixing method based on noise level estimation (SU-NLE) alternative direction method of multipliers (ADMM); hyperspectral image (HSI); sparse unmixing method based on noise level estimation (SU-NLE)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Li, C.; Ma, Y.; Mei, X.; Fan, F.; Huang, J.; Ma, J. Sparse Unmixing of Hyperspectral Data with Noise Level Estimation. Remote Sens. 2017, 9, 1166.

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