You are here

Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model

TitleUnsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model
Publication TypeJournal Article
Year of Publication2005
AuthorsDeng, H., and D. A. Clausi
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume43
Issue12
Start Page2940
Pagination528 - 538
KeywordsClassification, coocurrence, expectation-maximization (EM), Gamma distribution, intensity, K-means clustering, Markov random field (MRF), mixture model, Pattern Recognition, probabilities, sea ice, Segmentation, synthetic aperture radar (SAR), texture, unsupervised
Abstract

—Environmental and sensor challenges pose difficulties for the development of computer-assisted algorithms to segment synthetic aperture radar (SAR) sea ice imagery. In this research, in support of operational activities at the Canadian Ice Service, images containing visually separable classes of either ice and water or multiple ice classes are segmented. This paper uses image intensity to discriminate ice from water and uses texture features to identify distinct ice types. In order to seamlessly combine image spatial relationships with various image features, a novel Bayesian segmentation approach is developed and applied. This new approach uses a function-based parameter to weight the two components in a Markov random field (MRF) model. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to successfully segment various SAR sea ice images and achieve improvement over existing published methods including the standard MRF-based method, finite Gamma mixture model, and K-means clustering.