Mdl-Based Band Selection and Adaptive Penalties for Hyperspectral Image Segmentation

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Kerfoot, Ian B.

Contributors dc:contributor

• Bresler, Yoram

• Engineering, Electronics and Electrical

Language dc:language

eng

Identifier

(MiAaPQ)AAI9737156

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81187