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Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 47, Iss. 28 — Oct. 1, 2008
  • pp: F77–F84

End-member extraction for hyperspectral image analysis

Qian Du, Nareenart Raksuntorn, Nicolas H. Younan, and Roger L. King  »View Author Affiliations

Applied Optics, Vol. 47, Issue 28, pp. F77-F84 (2008)

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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

Original Manuscript: March 3, 2008
Revised Manuscript: June 24, 2008
Manuscript Accepted: July 2, 2008
Published: July 25, 2008

Qian Du, Nareenart Raksuntorn, Nicolas H. Younan, and Roger L. King, "End-member extraction for hyperspectral image analysis," Appl. Opt. 47, F77-F84 (2008)

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  1. M. E. Winter, “N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data,” Proc. SPIE 3753, 266-275 (1999). [CrossRef]
  2. H. Ren and C.-I. Chang, “Automatic spectral target recognition in hyperspectral imagery,” IEEE Trans. Aerosp. Electron. Syst. 39, 1232-1249 (2003). [CrossRef]
  3. J. C. Harsanyi and C.-I. Chang, “Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection,” IEEE Trans. Geosci. Remote Sens. 32, 779-785(1994). [CrossRef]
  4. J. M. P. Nascimento and J. M. Bioucas Dias, “Vertex component analysis: a fast algorithm to unmix hyperspectral data,” IEEE Trans. Geosci. Remote Sens. 43, 898-910 (2005). [CrossRef]
  5. D. Heinz and C.-I. Chang, “Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens. 39, 529-545(2001). [CrossRef]
  6. C.-I. Chang, C.-C. Wu, W.-M. Liu, and Y.-C. Ouyang, “A new growing method for simplex-based endmember extraction algorithm,” IEEE Trans. Geosci. Remote Sens. 44, 2804-2819(2006). [CrossRef]
  7. R. E. Roger, “A faster way to compute the noise-adjusted principal components transform matrix,” IEEE Trans. Geosci. Remote Sens. 32, 1194-1196 (1994). [CrossRef]
  8. X. Tao, B. Wang, L. Zhang, and J. Zhang, “A new endmember extraction algorithm based on orthogonal bases of subspace formed by endmembers,” IEEE International Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007 (IEEE, 2007), pp. 2006-2009.
  9. D. M. Rogge, B. Rivard, J. Zhang, and J. Feng, “Iterative spectral unmixing for optimizing per-pixel endmembers sets,” IEEE Trans. Geosci. Remote Sens. 44, 3725-3736 (2006). [CrossRef]

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