Ratio-Based Edge Detection for SAR Imagery Using MAP Estimation and GGMRF Modeling
Due to the low signal-to-noise ratios and the multiplicative nature of speckle,edge detection is particularly difficult for synthetic aperture radar (SAR) imagery.A new spatially adaptive Bayesian method based on Markov random field (MRF) is proposed to detect edges in SAR imagery.The generalized Gaussian MRF (GGMRF) is employed as a prior distribution to develop a maximum a posteriori probability estimator for the local mean power.A method of jointly and iteratively estimating the local mean power and the hyperparameters of GGMRF model is introduced.The receiver operating characteristics analysis and Chi-square test are utilized to determine optimal parameters of the edge detector.Experiments are carried out by using real SAR images,and the results show that the proposed edge detector is effective and performs favorably in comparison with the existing popular edge detectors in most cases.
Maximum a posteriori probability estimationGeneralized Gaussian-Markov random fieldEdge detectionSynthetic aperture radarRatio
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维普
