@inproceedings {238,
	title = {Automated classification of operational SAR sea ice images},
	booktitle = {7th Canadian Conference on Computer and Robot Vision},
	year = {2010},
	month = {06/2010},
	address = {Ottawa, Ontario, Canada},
	abstract = {<p>The automated classification of operational sea ice satellite imagery is important for ship navigation and environmental monitoring. Annually, thousands of large synthetic aperture radar (SAR) scenes are manually processed by the Canadian Ice Service (CIS) and pixel-level interpretation is not feasible. Trained ice analysts divide SAR images into $\#$x201D;polygon $\#$x201D; areas and then identify the number and type of ice classes per polygon. Full scene unsupervised classification can be performed by first segmenting each polygon into distinct regions algorithmically. Since there is insufficient information to assign a sea ice label for each region within an individual polygon, a Markov random field formulation using joint information to label each region in a full SAR scene has been developed. This approach has been successfully applied to operational CIS data to produce pixel-level classified images and is the first known successful end-to-end process for automatically classifying operational SAR sea ice images.</p>
},
	keywords = {automated classification, Canadian Ice Service, environmental monitoring, geophysical image processing, image segmentation, Markov processes, Markov random field, navigation, operational SAR sea ice images, pattern classification, pixel level interpretation, sea ice, sea ice satellite imagery, ship navigation, synthetic aperture radar, synthetic aperture radar scenes, unsupervised classification},
	doi = {http://dx.doi.org/10.1109/CRV.2010.59},
	author = {S Ochilov and D A. Clausi}
}
