A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging

• Other Authors: Neher, Robert E. (authoraut)
• Format: Others
• Language: English; English
• Published: Florida State University
• Subjects: Statistics; Probabilities
• Online Access: http://purl.flvc.org/fsu/fd/FSU_migr_etd-2691

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. === A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. === Fall Semester, 2004. === August 27, 2004. === Hyperspectral, Bayesian, Labeling, Gibbs Random Fields, Markov Random Fields === Includes bibliographical references. === Anuj Srivastava, Professor Directing Dissertation; Xiuwen Liu, Outside Committee Member; Fred Huffer, Committee Member; Marten Wegkamp, Committee Member.