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Proceedings Paper

A comparison of unmixing algorithms for hyperspectral imagery
Author(s): Andrea Santos-García; Miguel Vélez-Reyes; Samuel Rosario-Torres; Jesus D. Chinea
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Paper Abstract

In this paper, we present an experimental comparison of unmixing using the constrained positive matrix factorization (cPMF) with SMACC and MaxD unmixing algorithms that retrieve endmembers from the image pixels. The comparison was made using hyperspectral images collected over Vieques Island in Puerto Rico using the AISA sensor. Based on field work, six information classes were identified in the area of interest and the algorithms are evaluated in their capability to retrieve information about the classes of interest. The cPMF was the only approach capable of identifying all six informational classes with one or more spectral classes assigned to them. SMACC and MaxD were unable to extract one of the classes. The abundance maps from cPMF describe the spatial distribution of the information classes.

Paper Details

Date Published: 11 May 2009
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341N (11 May 2009); doi: 10.1117/12.819486
Show Author Affiliations
Andrea Santos-García, Univ. de Puerto Rico Mayagüez (United States)
Miguel Vélez-Reyes, Univ. de Puerto Rico Mayagüez (United States)
Samuel Rosario-Torres, Univ. de Puerto Rico Mayagüez (United States)
Jesus D. Chinea, Univ. de Puerto Rico Mayagüez (United States)


Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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