Special Section on Sparsity-Driven High Dimensional Remote Sensing Image Processing and Analysis

Endmember initialization method for hyperspectral data unmixing

[+] Author Affiliations
Rui Wang

Southwest Jiaotong University, School of Information Science and Technology, West Hi-Tech Zone, Chengdu 611756, China

Ghent University, Department of Telecommunications and Information Processing, (TELIN), Sint-Pietersnieuwstraat 41, Ghent 9000, Belgium

Hengchao Li

Southwest Jiaotong University, School of Information Science and Technology, West Hi-Tech Zone, Chengdu 611756, China

Wenzhi Liao

Ghent University, Department of Telecommunications and Information Processing, (TELIN), Sint-Pietersnieuwstraat 41, Ghent 9000, Belgium

Xin Huang

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, No. 129 Luoyu Road, Wuhan 430079, China

J. Appl. Remote Sens. 10(4), 042009 (Aug 09, 2016). doi:10.1117/1.JRS.10.042009
History: Received February 10, 2016; Accepted July 22, 2016
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Abstract.  Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of the hyperspectral image scene. One important issue in hyperspectral data unmixing is the initialization of endmembers. Most unmixing methods initialize their endmembers by randomly selecting a specified number of pixels from the data or by vertex component analysis, which limits their performance in practice. We propose an endmember initialization method for hyperspectral data unmixing. Our initial endmembers include some of the true endmembers, which improves the accuracy of hyperpspectral unmixing effectively. The experimental results on both synthetic and real hyperspectral data illustrate the superiority of the proposed method compared with other state-of-the-art approaches.

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© 2016 Society of Photo-Optical Instrumentation Engineers

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Rui Wang ; Hengchao Li ; Wenzhi Liao and Xin Huang
"Endmember initialization method for hyperspectral data unmixing", J. Appl. Remote Sens. 10(4), 042009 (Aug 09, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.042009


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