|Title||SAR sea ice image segmentation using an edge-preserving region-based MRF|
|Publication Type||Conference Paper|
|Year of Publication||2009|
|Authors||Yang, X., and D. A. Clausi|
|Conference Name||16th IEEE International Conference on Image Processing (ICIP)|
|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|
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.