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Open AccessArticle

Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning

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Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an 710071, China
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Computer Science Department, Xi’an Jiaotong University, Xi’an 710049, China
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Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 339; https://doi.org/10.3390/rs10020339
Received: 27 December 2017 / Revised: 29 January 2018 / Accepted: 1 February 2018 / Published: 23 February 2018
(This article belongs to the Section Remote Sensing Image Processing)
Spectral unmixing is a popular technique for hyperspectral data interpretation. It focuses on estimating the abundance of pure spectral signature (called as endmembers) in each observed image signature. However, the identification of the endmembers in the original hyperspectral data becomes a challenge due to the lack of pure pixels in the scenes and the difficulty in estimating the number of endmembers in a given scene. To deal with these problems, the sparsity-based unmixing algorithms, which regard a large standard spectral library as endmembers, have recently been proposed. However, the high mutual coherence of spectral libraries always affects the performance of sparse unmixing. In addition, the hyperspectral image has the special characteristics of space. In this paper, a new unmixing algorithm via low-rank representation (LRR) based on space consistency constraint and spectral library pruning is proposed. The algorithm includes the spatial information on the LRR model by means of the spatial consistency regularizer which is based on the assumption that: it is very likely that two neighbouring pixels have similar fractional abundances for the same endmembers. The pruning strategy is based on the assumption that, if the abundance map of one material does not contain any large values, it is not a real endmember and will be removed from the spectral library. The algorithm not only can better capture the spatial structure of data but also can identify a subset of the spectral library. Thus, the algorithm can achieve a better unmixing result and improve the spectral unmixing accuracy significantly. Experimental results on both simulated and real hyperspectral datasets demonstrate the effectiveness of the proposed algorithm. View Full-Text
Keywords: hyperspectral remote sensing; spectral unmixing; space consistency constraint; dictionary pruning hyperspectral remote sensing; spectral unmixing; space consistency constraint; dictionary pruning
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Zhang, X.; Li, C.; Zhang, J.; Chen, Q.; Feng, J.; Jiao, L.; Zhou, H. Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning. Remote Sens. 2018, 10, 339.

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