Road Surface Roughness Estimation Using Spaceborne Synthetic Aperture Radar

Abstract

Road surface roughness is a major factor that is responsible for the skid resistance of vehicles and, thus, road safety. Therefore, it needs to be monitored regularly to ensure that the roughness values are in the optimal range and to perform maintenance actions when needed. Synthetic aperture radar (SAR) backscatter is sensitive to surface roughness and can provide large-scale estimates of road surface roughness. In this letter, a semi-empirical model for estimating road surface roughness using high-resolution spaceborne X-band SAR datasets from Germany’s TerraSAR-X satellite is proposed for the first time. The method is capable of handling the low signal-to-noise ratio (SNR) of spaceborne SAR. To enhance the reliability of the results obtained from rather low SNR datasets, techniques such as SNR thresholding, multi-dataset fusion, and automatic road extraction were implemented. The study’s results show good agreement with ground truth data.