@inproceedings{300ba76f4fcc4072ad88dadcb1a58bf5,
title = "Classification of hyperspectral spatial/spectral patterns using Gauss-Markov random fields",
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.",
keywords = "Gauss-Markov random fields, Hyperspectral classification, Hyperspectral texture",
author = "Smartt, {Heidi A.} and Tyo, {J. Scott}",
year = "2006",
doi = "10.1117/12.666041",
language = "English (US)",
isbn = "0819462896",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII",
note = "Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII ; Conference date: 17-04-2006 Through 20-04-2006",
}