Road Surface Roughness Estimation Using Polarimetric SAR Data

Babu, Arun; Baumgartner, Stefan V.

doi:10.23919/irs48640.2020.9253934

Cited by 3 publications

(4 citation statements)

References 12 publications

(14 reference statements)

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“…T HE road infrastructure has a major role in the economic growth and development of a country. The quality of the road network directly influences the safety, health, driving comfort, and seamless transport of goods and services [1]- [3]. Therefore, periodic monitoring and maintenance of the road infrastructure quality is a necessity.…”

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

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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.

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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

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“…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%

“…Therefore, monitoring the quality of road infrastructure and carrying out regular maintenance are equally important for a country's economy and also for the safety of road users. There are several factors that affect the road surface quality, of which one important is the road surface roughness [5]. This is because the road surface roughness is responsible for the friction between the road surface and the tires of the vehicles [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Because the road surfaces have completely different characteristics from the conditions assumed in the development of these models. Road surfaces are smoother and roughness is expected to be only in the millimeter range (1 to 3 mm) [5] and the signalto-noise ratio (SNR) expected from a smooth road surface is much lower compared to the rough agricultural fields. Also, asphalt and concrete, which are commonly used for road construction, have a completely different dielectric constant than the agricultural fields [14].…”

Section: Introductionmentioning

confidence: 99%

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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

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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.

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A Comparative Study of AI-Based International Roughness Index (IRI) Prediction Models for Jointed Plain Concrete Pavement (JPCP)

et al. 2022

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The international roughness index (IRI) can be employed to evaluate the smoothness of pavement. The previously proposed mechanical-empirical pavement design guide (MEPDG), which is used to model the IRI of joint plain concrete pavement (JPCP), has been modified in this study considering its disadvantage of low prediction accuracy. To improve the reliability of the prediction effect of the IRI for JPCP, this study compares the prediction accuracy of the IRI of JPCP by using the machine-learning methods of support vector machine (SVM), decision tree (DT), and random forest (RF), optimized by the hyperparameter of the beetle antennae search (BAS) algorithm. The results from the machine-learning process show that the BAS algorithm can effectively improve the effectiveness of hyperparameter tuning, and then improve the speed and accuracy of optimization. The RF model proved to be the one with the highest prediction accuracy among the above three models. Finally, this study analyzes the importance score of input variables to the IRI, and the results show that the IRI was proportional to all the input variables in this study, and the importance score of initial smoothness (IRII) and total joint faulting cumulated per km (TFAULT) were the highest for the IRI of JPCP.

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Road Surface Roughness Estimation Using Polarimetric SAR Data

Cited by 3 publications

References 12 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

A Comparative Study of AI-Based International Roughness Index (IRI) Prediction Models for Jointed Plain Concrete Pavement (JPCP)

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