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Proceedings Paper

Incorporating local information in unsupervised hyperspectral unmixing
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Paper Abstract

In hyperspectral imaging, the radiation represented by a single pixel rarely comes from the interaction with a single homogeneous material. However, the high spectral resolution of imaging spectrometers enables the detection, identification, and classification of subpixel objects from their contribution to the measured spectral signal. Unmixing is a hyperspectral image processing approach where the measured spectral signature is decomposed into a collection of constituent spectra, or endmembers, and a set of corresponding fractions or abundances which correspond to the fractional area occupied by the particular endmember in that pixel. The use of a single spectrum to represent an endmember class does not take into account the variability of spectral signatures caused by natural factors. Simple spectral mixture analysis can, by itself, provide suitable accuracies in some relatively homogeneous environments, but because of the spectral complexity of many landscapes, the use of fixed endmember spectra may results in inaccurate unmixing analysis for complex regions over large landscapes. This paper addresses the question of how to perform unsupervised unmixing where local information is used to extract local endmember information and merged at a global level to extract endmembers classes for developing an accurate description of the scene under study using the nonnegative matrix factorization. Preliminary results using AVIRIS data are presented. Results show that this approach better captures local structures that are not possible with global unmixing approach. Furthermore, they show that spatial information allows the identification of more spectral endmembers than is it possible with just spectral-only methods.

Paper Details

Date Published: 24 May 2012
PDF: 12 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901N (24 May 2012); doi: 10.1117/12.920686
Show Author Affiliations
Miguel A. Goenaga-Jimenez, Univ. de Puerto Rico Mayagüez (United States)
Miguel Velez-Reyes, Univ. de Puerto Rico Mayagüez (United States)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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