@article {128,
	title = {Evaluating SAR sea ice image segmentation using edge-preserving region-based MRFs.},
	journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)},
	volume = {5},
	year = {2012},
	pages = {1383-1393},
	abstract = {<p>This paper presents a new approach to sea ice segmentation in synthetic aperture radar (SAR) intensity images by combining an edge-preserving region (EPR)-based representation with region-level MRF models. To construct the EPR-based representation of a SAR image, edge strength is measured using instantaneous coefficient of variation (ICOV) upon which the watershed algorithm is applied to partition the image into primitive regions. In addition, two new metrics for quantitative assessment of region characteristics (region accuracy and region redundancy) are defined and used for parameter estimation in the ICOV extraction process towards desired region characteristics. In combination with a region-level MRF, the EPR-based representation facilitates the segmentation process by largely reducing the search space of optimization process and improving parameter estimation of feature model, leading to considerable computational savings and less probability of false segmentation. The proposed segmentation method has been evaluated using a synthetic sea ice image corrupted with varying levels of speckle noise as well as real SAR sea ice images. Relative to the existing region-level MRF-based methods, testing results have demonstrated that our proposed method substantially improves the segmentation accuracy at high speckle noise and achieves on average 29\% reduction of computational time.</p>
},
	keywords = {instantaneous coefficient of variation, Markov random field (MRF), sea ice classification, synthetic aperture radar (SAR), watersheds},
	author = {X Yang and D A. Clausi}
}
@article {417,
	title = {Operational map-guided classification of SAR sea ice imagery},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {43},
	year = {2005},
	pages = {2940 - 2951},
	abstract = {<p>This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual \&quot;egg code\&quot; maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71\% and 89\%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.</p>
},
	keywords = {Beaufort Sea, Canadian Ice Service, Fisher criterion, geophysical signal processing, gray-level cooccurrence matrix, Gulf of Saint Lawrence, image classification, image segmentation, image texture, Kolmogorov-Smirnov test, Mahalanobis distance, map-guided classification, Markov processes, Markov random field, oceanographic regions, oceanographic techniques, pixel-based ice maps, radar imaging, remote sensing by laser beam, remote sensing by radar, SAR sea ice imagery, sea ice, sea ice classification, segment distribution comparison, synthetic aperture radar},
	issn = {0196-2892},
	doi = {http://dx.doi.org/10.1109/TGRS.2005.857897},
	author = {P Maillard and D A. Clausi and H Deng}
}
