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TitleComparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery
AuthorClausi, D A; Yu, B
SourceIEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers) vol. 42, no. 1, 2004 p. 215-228, Open Access logo Open Access
Alt SeriesNatural Resources Canada, Contribution Series 20181382
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing
ProgramCanada Centre for Remote Sensing Divsion
Released2004 01 01
AbstractThis 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.

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