End-member extraction for hyperspectral image analysis
Applied Optics, Vol. 47, Issue 28, pp. F77-F84 (2008)
http://dx.doi.org/10.1364/AO.47.000F77
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Abstract
We investigate the relationship among several popular end-member extraction algorithms, including N-FINDR, 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.
© 2008 Optical Society of America
OCIS Codes
(100.0100) Image processing : Image processing
(100.2960) Image processing : Image analysis
(100.4145) Image processing : Motion, hyperspectral image processing
ToC Category:
Hyperspectral Processing and Analysis
History
Original Manuscript: March 3, 2008
Revised Manuscript: June 24, 2008
Manuscript Accepted: July 2, 2008
Published: July 25, 2008
Citation
Qian Du, Nareenart Raksuntorn, Nicolas H. Younan, and Roger L. King, "End-member extraction for hyperspectral image analysis," Appl. Opt. 47, F77-F84 (2008)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-47-28-F77
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