End-member extraction for hyperspectral image analysis

被引：86

作者：

Du, Qian ^{[1
]}

Raksuntorn, Nareenart ^{[1
]}

Younan, Nicolas H. ^{[1
]}

King, Roger L. ^{[1
]}

机构：

[1] Mississippi State Univ, Dept Elect & Comp Engn, GeoResources Inst, Mississippi State, MS 39762 USA

来源：

APPLIED OPTICS | 2008年 / 47卷 / 28期

关键词：

D O I：

10.1364/AO.47.000F77

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We investigate the relationship among several popular end-member extraction algorithms, including NFINDR, the simplex growing algorithm (SGA), vertex component analysis (VCA), automatic target generation process (ATGP), and fully constrained least squares linear unmixing (FCLSLU). We analyze the fundamental equivalence in the searching criteria of the simplex volume maximization and pixel spectral signature similarity employed by these algorithms. We point out that their performance discrepancy comes mainly from the use of a dimensionality reduction process, a parallel or sequential implementation mode, or the imposition of certain constraints. Instructive recommendations in algorithm selection for practical applications are provided. (C) 2008 Optical Society of America

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收藏

页码：F77 / F84

页数：8

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