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Proceedings Paper

Integrating spatial information in unmixing using the nonnegative matrix factorization
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Paper Abstract

An approach to incorporate spatial information in unmixing using the nonnegative matrix factorization is presented. We call this method the spectrally adaptive constrained NMF (sacNMF). The spatial information is incorporated by partitioning hyperspectral images into spectrally homogeneous regions using quadtree region partitioning. Endmembers for each region are extracted using the nonnegative matrix factorization and then clustered in spectral endmembers classes. The endmember classes better account for the variability of spectral endmembers across the landscape. Abundances are estimated using all spectral endmembers. Experimental results using AVIRIS data from Indian Pines is used to demonstrate the potential of the proposed approach. Comparisons with other published approaches are presented.

Paper Details

Date Published: 13 June 2014
PDF: 9 pages
Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 908811 (13 June 2014); doi: 10.1117/12.2053401
Show Author Affiliations
Miguel A. Goenaga-Jimenez, Univ. de Puerto Rico Mayagüez (United States)
Miguel Vélez-Reyes, The Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 9088:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX
Miguel Velez-Reyes; Fred A. Kruse, Editor(s)

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