Road Crack Detection and Orientation Estimation Using Airborne Synthetic Aperture Radar

Cracks on road surfaces are a significant safety hazard that can progress into larger potholes, posing risks to vehicles and passengers. Synthetic aperture radar (SAR) data acquired by high-resolution airborne SAR systems are sensitive to changes on the road surface and can be utilized for periodic road condition monitoring. This study proposes a novel method that combines an adaptive thresholding algorithm with the Radon transform for detecting road cracks and estimating both their severity and orientation. In this approach, the adaptive thresholding algorithm detects the cracks, while the Radon transform qualitatively quantifies their severity using the maximum Radon magnitude from the sinogram and estimates their orientation as bearing angles relative to true north. While the proposed method is applicable to various airborne SAR platforms, it is demonstrated in this study with X-band airborne SAR data acquired by DLR's F-SAR system with a spatial resolution of 25 cm. The detected cracks and orientations were validated against Google Earth images, showing close agreement with the locations and orientations of the actual cracks. This research underscores the potential of airborne SAR data in supporting predictive road maintenance efforts through early identification of surface defects.