Type of Document	Dissertation
Author	Neher, Robert Earl
Author's Email Address	debrob2@earthlink.net
URN	etd-08312004-164956
Title	A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Anuj Srivastava Committee Chair Marten Wegkamp Committee Member Xiuwen Liu Committee Member
Keywords	Hyperspectral Bayesian Labeling Gibbs Random Fields Markov Random Fields
Date of Defense	2004-08-27
Availability	unrestricted
Abstract We explore the non-Gaussianity of hyperspectral data and present probability models that capture variability of hyperspectral images. In particular, we present a nonparametric probability distribution that models the distribution of the hyperspectral data after reducing the dimension of the data via either principal components or Fisher's discriminant analysis. We also explore the directional differences in observed images and present two parametric distributions, the generalized Laplacian and the Bessel K form, that well model the non-Gaussian behavior of the directional differences. We then propose a model that labels each spatial site, using Bayesian inference and Markov random fields, that incorporates the information of the non-parametric distribution of the data, and the parametric distributions of the directional differences, along with a prior distribution that favors smooth labeling. We then test our model on actual hyperspectral data and present the results of our model, using the Washington D.C. Mall and Indian Springs rural area data sets.
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