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Using a column subset selection method for endmember extraction in hyperspectral unmixing
Author(s): Miguel Velez-Reyes; Maher Aldeghlawi
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Paper Abstract

This paper explores the use of SVD-based column subset selection (SVDSS) algorithms for endmember extraction in hyperspectral unmixing. We are using a version of the column subset selection problem that selects the subset of columns that better predicts the remaining columns in the matrix. It has been shown that this problem is related to the problem of selecting a maximum volume sub-matrix of a matrix. This is related to the problem of finding the spectral signatures from the image that provide the simplex of maximum volume that can be inscribed within the hyperspectral data set, which is the base of some endmember extraction algorithms like N-FINDR. In this paper, we present a numerical comparison between SVDSS and several endmember extraction algorithms such as N-FINDR, PPI and VCA. Simulated and real hyperspectral imagery from the HYDICE Urban Scene are used in numerical experiments to determine which algorithm extracts the endmembers with the highest simplex volume. Results show that SVDSS outperforms other approaches for endmember extraction.

Paper Details

Date Published: 16 May 2018
PDF: 7 pages
Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440A (16 May 2018); doi: 10.1117/12.2309867
Show Author Affiliations
Miguel Velez-Reyes, The Univ. of Texas at El Paso (United States)
Maher Aldeghlawi, The Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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