@article {125,
	title = {Unsupervised operational SAR sea ice image classification},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {50},
	year = {2012},
	pages = {4397-4408},
	abstract = {<p>Thousands of spaceborne synthetic aperture radar (SAR) sea-ice images are systematically processed every year in support of operational activities such as ship navigation and environmental monitoring. An automated approach that generates a pixel-level sea-ice image classification is required since manual pixel-level classification is not feasible. Currently, using a standardized approach, trained ice analysts manually segment full SAR scenes into smaller polygons to record ice types and concentrations. Using this data, pixel-level classification can be achieved by initial unsupervised segmentation of each polygon followed by automatic sea-ice labeling of the full scene. A fully automated Markov random field model that is used to assign labels to all segmented regions in the full scene has been designed and implemented. This approach is the first known successful end-to-end process for operational SAR sea-ice image classification. In addition, a novel performance evaluation framework has been developed to validate the segmentation and labeling of SAR sea-ice images. A trained sea-ice expert has conducted an arms length evaluation using this framework to generate a set of full scene reference images used for testing. Testing demonstrates operational success of the labeling approach.</p>
},
	keywords = {image classification, Markov random field (MRF), sea-ice, Synthetic, unsupervised segmentation},
	author = {S Ochilov and D A. Clausi}
}
