Image and Signal Processing Methods

Extracting pure endmembers using symmetric sparse representation for hyperspectral imagery

[+] Author Affiliations
Weiwei Sun, Yanwei Sun, Jialin Li

Ningbo University, Department of Geography and Spatial Information Techniques, 818 Fenghua Road, Ningbo, Zhejiang 315211, China

Chun Liu

Tongji University, College of Surveying and Geoinformatics, 1239 Siping Road, Shanghai 200092, China

Key Laboratory of Advanced Engineering Surveying of NASMG, 1239 Siping Road, Shanghai 200092, China

Weiyue Li

Shanghai Normal University, Institute of Urban Studies, 100 Guilin Road, Shanghai 200234, China

J. Appl. Remote Sens. 10(4), 045023 (Dec 26, 2016). doi:10.1117/1.JRS.10.045023
History: Received April 11, 2016; Accepted December 6, 2016
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Abstract.  This article proposes a symmetric sparse representation (SSR) method to extract pure endmembers from hyperspectral imagery (HSI). The SSR combines the features of the linear unmixing model and the sparse subspace clustering model of endmembers, and it assumes that the desired endmembers and all the HSI pixel points can be sparsely represented by each other. It formulates the endmember extraction problem into a famous program of archetypal analysis, and accordingly, extracting pure endmembers can be transformed as finding the archetypes in the minimal convex hull containing all the HSI pixel points. The vector quantization scheme is adopted to help in carefully choosing the initial pure endmembers, and the archetypal analysis program is solved using the simple projected gradient algorithm. Seven state-of-the-art methods are implemented to make comparisons with the SSR on both synthetic and real hyperspectral images. Experimental results show that the SSR outperforms all the seven methods in spectral angle distance and root-mean-square error, and it can be a good alternative choice for extracting pure endmembers from HSI data.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Weiwei Sun ; Chun Liu ; Yanwei Sun ; Weiyue Li and Jialin Li
"Extracting pure endmembers using symmetric sparse representation for hyperspectral imagery", J. Appl. Remote Sens. 10(4), 045023 (Dec 26, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.045023


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