Unsupervised Segmentation of SAR Images Based Markov Random Field
Aiming at the problem of robustness in characteristics of synthetic aperture radar( SAR) sea ice image segmentation,Proposed a new Markov random field( MRF) segmentation method,The traditional for two-component MRF model segmentation,require training data to estimate the model parameters required,therefore not suitable for unsupervised segmentation. New approach uses a function-based parameter to weight the two components in a MRF model. the contribution of each component to the whole system assigns a weight parameter,Considering the spatial relation information using a simulated annealing scheme,thereby improving parameters. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that this method is more accurate edge localization ability and faster segmentation speed than RKmeans and C-MLL segmentation method.