Bayesian edge detector for SAR imagery using discontinuity-adaptive Markov random field modeling

Synthetic aperture radar (SAR) image is severely affected by multiplicative speckle noise, which greatly complicates the edge detection. In this paper, by incorporating the discontinuity-adaptive Markov random field (DAMRF) and maximum a posteriori (MAP) estimation criterion into edge detection, a Bayesian edge detector for SAR imagery is accordingly developed. In the proposed detector, the DAMRF is used as the a priori distribution of the local mean reflectivity, and a maximum a posteriori estimation of it is thus obtained by maximizing the posteriori energy using gradient–descent method. Four normalized ratios constructed in different directions are computed, based on which two edge strength maps (ESMs) are formed. The final edge detection result is achieved by fusing the results of two thresholded ESMs. The experimental results with synthetic and real SAR images show that the proposed detector could efficiently detect edges in SAR images, and achieve better performance than two popular detectors in terms of Pratt’s figure of merit and visual evaluation in most cases.