Mdl-Based Band Selection and Adaptive Penalties for Hyperspectral Image Segmentation
Kerfoot, Ian B.
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/81187
Description
Title
Mdl-Based Band Selection and Adaptive Penalties for Hyperspectral Image Segmentation
Author(s)
Kerfoot, Ian B.
Issue Date
1997
Doctoral Committee Chair(s)
Bresler, Yoram
Department of Study
Electrical Engineering
Discipline
Electrical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
All MRF image-segmentation criteria as in Step 3 have spatial-penalty parameters that must be chosen. An adaptive algorithm that chooses the penalty parameters to maximize the pseudo-likelihood (PL) of the current image was developed by Lakshmanan and Derin, but it uses a costly simulated-annealing algorithm. We use a decoupling argument to find simple, closed-form solutions for the PL penalty parameters of a globally adaptive (GA) MRF criterion with boundary and region penalties. A theoretical analysis shows that GA penalties only minimize the error rate if the scene has certain weak symmetry properties. For example, all boundaries must be equally rough. This is not always satisfied in practice, so we also introduce an MRF with class-pair-conditional (CP) boundary penalties. We segment both synthetic and real images to validate the theoretical analysis and illustrate the capabilities and limitations inherent to the PL approximation.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
