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Proceedings Paper

Classification of hyperspectral spatial/spectral patterns using Gauss-Markov random fields
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Paper Abstract

Hyperspectral imaging sensors capture digital images in hundreds of contiguous spectral bands, allowing remote material identification. Most algorithms for identifying materials characterize the materials according to spectral information only, ignoring potentially valuable spatial relationships. This paper investigates the use of integrated spatial and spectral information for characterizing materials. It examines the specific situation where a set of pixels has resolution such that it contains spatial patterns of mixed pixels. An autoregressive Gauss-Markov random field (GMRF) is used to model the predictability of a target pixel from neighboring pixels. At the resolution of interest, the GMRF model can successfully classify spatial patterns of aircraft and a residential area from the HYDICE airborne sensor Desert Radiance field collection at Davis Monthan Air Force Base, Arizona.

Paper Details

Date Published: 4 May 2006
PDF: 10 pages
Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 62330I (4 May 2006); doi: 10.1117/12.666041
Show Author Affiliations
Heidi A. Smartt, Sandia National Labs. (United States)
Univ. of New Mexico (United States)
J. Scott Tyo, Univ. of New Mexico (United States)


Published in SPIE Proceedings Vol. 6233:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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