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.