An Approach to Extract Edge in SAR Image Based on Square Successive Difference of Neighborhood Averages

An approach based on the square successive difference of neighborhood averages is proposed to extract edges in SAR images. The proposed operator partitions the neighborhood window of a pixel into many non-overlapping regions, and applies the mean square successive difference as an indicator of the edge strength. It is proved regardless of the mean intensity and is an edge detector with Constant False Alarms Rate (CFAR) theoretically. It also performs well in the estimation of edge orientations. The edge thinning algorithm and an adaptive double-thresholds processing are carried out to reduce false alarms nearby the true edges and to connect edges. The experiments on simulated and real SAR images indicate that the proposed operator achieves well performance in the edge detection and localization, and the extracted edge segments are long and continuous.