Road Surface Quality Assessment Using Polarimetric Airborne SAR

Babu, Arun; Baumgartner, Stefan V.

doi:10.1109/radarconf2043947.2020.9266588

Cited by 4 publications

(3 citation statements)

References 13 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, periodic monitoring and maintenance of the road infrastructure quality is a necessity. The road surface roughness is one of the important factors which affect the road surface quality [4]- [6] .…”

Section: Introductionmentioning

confidence: 99%

Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

Babu

Baumgartner

Krieger

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

Babu

Baumgartner

Krieger

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Test Sitesmentioning

confidence: 99%

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

Rischioni

Babu

Baumgartner

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Airborne Synthetic Aperture Radar (SAR) has the potential to monitor remotely the road traffic infrastructure on a large scale. Of particular interest is the road surface roughness, which is an important road safety parameter. For this task, novel algorithms need to be developed. Machine learning approaches, such as Artificial Neural Networks (ANN) and Random Forest Regression, which can perform non-linear regression, can achieve this goal. This work considers fully polarimetric airborne radar datasets captured with DLR's airborne F-SAR radar system. Several machine learning-based approaches were tested on the datasets to estimate road surface roughness. The resulting models were then compared with ground truth surface roughness values and also with the semi-empirical surface roughness model studied in previous work.

show abstract

“…Another common method to gain information about surfaces, especially if they should be very accurate, is using a synthetic aperture radar (SAR). However, past work using SAR on road characterization concentrate on road condition, like Babu and Baumgartner (2020), who monitor mainly road damage. The recorded inhomogeneities are of higher order than for this work.…”

Section: Introductionmentioning

confidence: 99%

MmWave scattering properties of roads on rough asphalt and concrete surfaces

Kurz,

Fuenfer,

Pfeiffer

et al. 2023

Adv. Radio Sci.

View full text Add to dashboard Cite

Abstract. In this work, a synthetic aperture radar setup is used for analyzing the mmWave scattering of road surfaces in the automotive 77 GHz band in the laboratory. With this setup, samples of concrete roads in two different surface conditions are investigated, determining the variances in reflectivity depending on material composition and surface structure. Afterward, the distribution of these variations is fitted using probability density functions, namely normal and rayleigh distribution fits. Consequently, the diffuse scattering behavior of concrete roads can be described mathematically. Additionally, previously presented porous asphalt roads are compared and fitted analogously to get a summary of the scattering for all common road surfaces in Germany. Furthermore, a validation of the measurement and the processing by analyzing particularly generated reference samples is performed.

show abstract

Road Surface Quality Assessment Using Polarimetric Airborne SAR

Cited by 4 publications

References 13 publications

Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

Approaches for Road Surface Roughness Estimation Using Airborne Polarimetric SAR

Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar

MmWave scattering properties of roads on rough asphalt and concrete surfaces

Contact Info

Product

Resources

About