@inproceedings {283,
	title = {SAR sea ice image segmentation using an edge-preserving region-based MRF},
	booktitle = {16th IEEE International Conference on Image Processing (ICIP)},
	year = {2009},
	month = {11/2009},
	abstract = {<p>In this paper, we propose a novel edge-preserving region (EPR)-based representation for synthetic aperture radar (SAR) images, which is incorporated with a region-level Markov random field (MRF) model to offer an efficient approach to the segmentation of SAR sea ice images. The EPR-based representations of SAR images are constructed by applying the speckle reduction anisotropic diffusion (SRAD) algorithm and the watershed transform, which aims at suppressing oversegmentation within objects while accurately locating object edges at region boundaries in the presence of speckle noise. In combination with a region-level MRF, the EPR-based representation largely reduces the search space of optimization process and improves parameter estimation of feature model, leading to considerable computational savings and less probability of false segmentation. Relative to the existing region-level MRF-based methods, testing results have demonstrated that the proposed method achieves more than 50\% reduction of computational time and improves the segmentation accuracy especially at high speckle noise.</p>
},
	keywords = {edge-preserving region, image segmentation, Markov processes, MRF model, parameter estimation, region-level Markov random field, SAR sea ice image segmentation, sea ice, speckle noise, speckle reduction anisotropic diffusion, SRAD algorithm, synthetic aperture radar, synthetic aperture radar images, watershed transform},
	doi = {http://dx.doi.org/10.1109/ICIP.2009.5414606},
	author = {X Yang and D A. Clausi}
}
@inproceedings {362,
	title = {SAR sea ice image segmentation based on edge-preserving watersheds},
	booktitle = {4th Annual Canadian Conference on Computer and Robot Vision},
	year = {2007},
	month = {05/2007},
	address = {Montreal, Quebec, Canada},
	abstract = {<p>This paper presents a hybrid method for the segmentation of SAR sea ice images, which consists of an initial watershed segmentation followed by a region merging. Iterative bilateral filtering is used to reduce speckle noise and suppress irrelevant image details, which can significantly alleviate oversegmentation of watersheds. Since edges are well preserved by bilateral filtering, the watershed algorithm is capable of precisely locating object boundaries. Final segmentation is accomplished by applying an iterative region merging on the watershed regions by taking into account local boundary strengths and regional statistics. The efficiency of the proposed method has been demonstrated on the segmentation of SAR sea ice images. In comparison with traditional watershed algorithm, our method achieves better performance in identifying filament structures such as leads.</p>
},
	keywords = {edge-preserving watersheds, filtering theory, image segmentation, iterative bilateral filtering, iterative methods, local boundary strengths, radar imaging, region merging, regional statistics, SAR sea ice image segmentation, speckle noise, statistical analysis, synthetic aperture radar},
	doi = {http://dx.doi.org/10.1109/CRV.2007.58},
	author = {X Yang and D A. Clausi}
}
