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Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery

TitleComparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery
Publication TypeJournal Article
Year of Publication2004
AuthorsClausi, D. A., and B. Yue
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume42
Pagination215 - 228
ISSN0196-2892
KeywordsBrodatz, discrimination ability, Feature Extraction, GLCP, gray-level cooccurrence probabilities, image segmentation, image texture, Markov processes, Markov Random Fields, MRF, Remote Sensing, remote sensing by radar, SAR sea ice imagery, sea ice, synthetic aperture radar, texture analysis, texture boundary, texture feature consistency, texture feature separability, texture methods, window size
Abstract

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.

DOI10.1109/TGRS.2003.817218