@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}
}
@inproceedings {405,
	title = {Pixel-based sea ice classification using the MAGSIC system},
	booktitle = {International Society for Photogrammetry and Remote Sensing},
	year = {2006},
	address = {Enschede, The Netherlands},
	abstract = {<p>MAGSIC is an operation-oriented system in development dedicated to map-guided classification of sea ice for navigation route planning and meteorological modeling. It has already produced promising results in difficult situations such as the Gulf of Saint-Lawrence in late winter. The Canadian Ice Service (CIS) produces ice maps made of large regions with relatively homogeneous concentrations of different ice types. MAGSIC uses the information of these maps to produce a pixel-based (rather than region-based) ice map by labeling a Markov random field (MRF) segmentation of RADARSAT-1 data along with its derived texture features. The system uses a novel implementation of MRF segmentation in combination with a unique labeling approach based on \&ldquo;cognitive reasoning\&rdquo;. Although reasonably successful, the system often had difficulties identifying ice type that required cues based on the shape recognition of large ice floes or leads. This article aims at thoroughly testing the MAGSIC system using validation data acquired during the \&ldquo;2003 Gulf of Saint-Lawrence SAR Validation Field Program\&rdquo; performed by CIS. Some new features were also added to MAGSIC and were evaluated. Results suggest a reasonable success and that the errors can be partially attributed to the generalized nature of the analysts ́interpretation and to the difficulties of obtaining concurring ground and image data. They suggest that classification metrics that can compare sample distributions were slightly superior for labeling purposes but this could not be confirmed statistically.</p>
},
	keywords = {Classification, navigation, Radarsat, Reasoning, Segmentation, Snow Ice, texture},
	author = {P Maillard and D A. Clausi}
}
