PURE ENDMEMBER EXTRACTION USING SSR FOR HYPERSPECTRAL IMAGERY

被引：0

作者：

Sun, Weiwei ^{[1
,2
]}

Jiang, Man ^{[1
]}

Zhang, Liangpei ^{[2
]}

机构：

[1] Ningbo Univ, Fac Architectural Engn Civil Engn & Environm, Ningbo 315211, Zhejiang, Peoples R China

[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China

来源：

2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年

关键词：

Endmember extraction; symmetric sparse representation; hyperspectral imagery; spectral unmixing;

D O I：

10.1109/IGARSS.2016.7730721

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This manuscript proposes a symmetric sparse representation (SSR) method to extract pure endmembers from Hyperspectral imagery (HSI). The SSR assumes that the desired endmembers and all the HSI pixels can be sparsely represented by each other and it formulates the endmember extraction problem into finding archetypes in the minimal convex hull of the HSI data. The optimization program of SSR is solved by a simple projected gradient algorithm and the endmembers are initialized with the vector quantization scheme. Preliminary results on the popular Urban HSI data infer that the SSR performs better than several state-of-the-art methods (VCA, NFINDER, AVMAX, SVMAX, XRAY, OSP and H2NMF).

引用

页码：6589 / 6592

页数：4

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