A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery

被引:39
作者
Geng, Xiurui [1 ]
Xiao, Zhengqing [2 ]
Ji, Luyan [1 ]
Zhao, Yongchao [1 ]
Wang, Fuxiang [3 ]
机构
[1] Chinese Acad Sci, Inst Elect, Key Lab Technol Geospatial Informat Proc & Applic, Beijing 100080, Peoples R China
[2] Beijing Normal Univ, Coll Resources Sci & Technol, Beijing 100086, Peoples R China
[3] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
关键词
Hyperspectral data; Endmember; Gaussian elimination; Simplex; IMAGING SPECTROSCOPY;
D O I
10.1016/j.isprsjprs.2013.02.020
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
A fast endmember-extraction algorithm based on Gaussian Elimination Method (GEM) is proposed in this paper under the fact that a pixel is an endmember if it has the maximum value in any spectral band of a hyperspectral image when based on linear mixing model. Applying Gaussian elimination is much like performing a lower triangular matrix to transform the hyperspectral image. As more endmembers have been extracted, fewer bands are needed to be involved in the Gaussian elimination process, thus greatly reducing the computing time. The experimental results with both simulated and real hyperspectral images indicate that the method proposed here is much faster than the vertex component analysis (VCA) method, and can provide a similar performance with VCA. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:211 / 218
页数:8
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