Road Surface Roughness Estimation With Spaceborne Synthetic Aperture Radar (SAR)

Abstract

Road surface roughness is a crucial factor in road traffic safety. It has a direct connection to the grip and skid resistance of vehicles, which influences their acceleration, maneuverability and braking behavior. Therefore, the quality of road roughness must be checked regularly to ensure that the values are within the acceptable range. Until now, road monitoring in Germany has been carried out by measuring vehicles that drive over the highways approximately once in every 4 years. However, this procedure is costly and requires a lot of time and personnel. The German Aerospace Center (DLR) has proved that high resolution airborne X-band synthetic aperture radar (SAR) datasets can be used to estimate the road surface roughness over a wide scale. But, the area that can be covered by an airborne SAR system is still limited. This limitation can be solved by the use of spaceborne SAR. In the scope of this master thesis, the potential to estimate the road surface roughness using the high-resolution X-band SAR datasets acquired by the TerraSAR X radar satellite of Germany was evaluated. The semi-empirical surface roughness estimation model developed at DLR for the airborne SAR was used as a foundation for this study. However, compared to the airborne SAR, the distances to the ground are tremendous in the spaceborne SAR case and thus the model was adapted. In addition to modifying the surface roughness model, this study also tested whether the results are credible, as the spaceborne SAR data has more noise, while only a low signal is received from the roads. The surface roughness results estimated using the spaceborne SAR datasets showed a good agreement with the airborne SAR based results and also with the ground truth surface roughness data. For the end user, KML files were generated to visualize the surface roughness images in Google Earth. The results may serve for future research in spaceborne SAR based surface roughness estimation with different settings and from different platforms.