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Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging

Title: A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging.
Name(s): Neher, Robert E., Jr., author
Srivastava, Anuj, professor directing dissertation
Liu, Xiuwen, outside committee member
Huffer, Fred, committee member
Wegkamp, Marten, committee member
Department of Statistics, degree granting department
Florida State University, degree granting institution
Type of Resource: text
Genre: Text
Issuance: monographic
Date Issued: 2004
Publisher: Florida State University
Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: 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.
Identifier: FSU_migr_etd-2691 (IID)
Submitted Note: A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Fall Semester, 2004.
Date of Defense: August 27, 2004.
Keywords: Hyperspectral, Bayesian, Labeling, Gibbs Random Fields, Markov Random Fields
Bibliography Note: Includes bibliographical references.
Advisory Committee: Anuj Srivastava, Professor Directing Dissertation; Xiuwen Liu, Outside Committee Member; Fred Huffer, Committee Member; Marten Wegkamp, Committee Member.
Subject(s): Statistics
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Host Institution: FSU

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Neher, R. E. (2004). A Bayesian MRF Framework for Labeling Terrain Using Hyperspectral Imaging. Retrieved from