Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

Abstract

The road surface roughness is an important parameter that determines the quality of a road network. It has a direct influence on the grip and skid resistance of the vehicles. For this reason, this parameter has to be periodically monitored to keep track of its changes. Nowadays, road surface roughness is measured by driving measurement vehicles equipped with laser scanners all over the country. But, this approach is very costly, labor-intensive, and time-consuming. This article is done to evaluate the potential of high-resolution airborne polarimetric synthetic aperture radar (SAR) to remotely estimate the road surface roughness on a wide scale. Different SAR backscatter-based semi-empirical models and SAR polarimetry-based models for surface roughness estimation are implemented in this article. Also, a new semi-empirical model is proposed in this article, which is trained specifically for the road surface roughness estimation. Additive noise subtraction, upper sigma nought threshold masking, and lower signal-to-noise ratio threshold masking techniques were implemented in this article to improve the reliability of road surface roughness estimation. The feasibility of this approach is tested using fully polarimetric X-band datasets acquired with DLR's airborne radar sensor F-SAR. The surface roughness results estimated using these airborne SAR datasets show good agreement with the ground truth surface roughness values and the results are discussed in this article.

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

“…The SAR polarimetry-based models [17] and the Oh models [18], [19] used in the literature for roughness estimation require the cross-polarization channels and hence cannot be used for road surface roughness estimation. The remaining models that require only the co-polarization channels are the semi-empirical Dubois model [20] and the modified Dubois model specifically developed for the road surface roughness estimation [24]. In [24], it was shown that the Dubois model has an incidence angle dependency and cannot estimate the roughness changes between concrete and asphalt surfaces at steeper incidence angles, which led to the development of the modified Dubois model for road surface roughness estimation.…”

“…Unfortunately, no GT data are available for the secondary test sites. Therefore, the surface roughness results obtained from these test sites can only be compared to the results obtained from the surface roughness estimation models investigated in previous work [5], [24], [27].…”

“…However, both of the above-mentioned studies used medium-resolution SAR datasets and did not produce a road surface roughness image that a road maintenance engineer without SAR knowledge could use to identify problematic areas on the road. One study that produced road surface roughness images can be found in [24]. In [24], the authors used high-resolution airborne Xband SAR datasets with 25 cm spatial resolution acquired by the F-SAR sensor of the German Aerospace Center (DLR).…”

“…One study that produced road surface roughness images can be found in [24]. In [24], the authors used high-resolution airborne Xband SAR datasets with 25 cm spatial resolution acquired by the F-SAR sensor of the German Aerospace Center (DLR).…”

Real-Time Road Pothole Mapping Based on Vibration Analysis in Smart City

“…To meet this challenge, it is necessary to propose an efficient and real-time observation system to discover the road surface potholes. For this purpose, several methods can be used to observe damaged road sections rapidly, including laser scanning methods [9]- [10] (3D laser scanning), machine vision (stereo machine vision) [12]- [14], airborne Synthetic Aperture Radar (SAR) [15], Ground Penetrating Radar [16], and vibration [17].…”