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Research on Edge Detection for SAR Images

Common edge detections based on gradient exhibit poor performance with SAR images because of the presence of multiplicative noise. Many edge detectors for extracting linear features from SAR imagery are derived from the contrast ratio edge detector with a constant probability of false alarm. However, most of them need one or more thresholds, which are generally selected by experience or by probability of false alarm, to determine that the pixel belongs to an edge or not. After analyzing the existing ratio-based detectors, we propose a method to automatically determine the threshold according to the image feature based on ratio edge detector instead of predefining it as before. It introduces the principle of optimal entropy into the edge detection of SAR image, which classifies the intermediate result image of ratio-based edge detector into two classes: edge and non-edge. Post-processing is then applied in order to enhance the edge location precision. The presented method is compared to other edge detection methods and applied results are in good agreement with the theoretical study, which shows its effectiveness and applicability.