Robust SAR Edge Detection

Edge detection is a fundamental building block in synthetic aperture radar (SAR) image analysis. Speckle phenomena present in SAR images poses a challenge for detection and localization of edges. Hence, there is a need for robust edge detection methods specifically designed for such images. In this paper, three different models for edges present in SAR images are introduced and a new robust SAR edge detection (ROSED) method is proposed. The method uses a ratio of medians (ROM) kernel for detecting clutter boundaries combined with a modified constant false alarm rate (CFAR) target detector for point targets and line-like edges. Finally, the log likelihood ratio test is applied for precise edge localization. The results show that the proposed method is robust to biasing effects and provides more complete and accurate edges compared to other methods.