Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar
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 ap...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10073636/ |
| _version_ | 1827973138030264320 |
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| author | Lucas Germano Rischioni Arun Babu Stefan V. Baumgartner Gerhard Krieger |
| author_facet | Lucas Germano Rischioni Arun Babu Stefan V. Baumgartner Gerhard Krieger |
| author_sort | Lucas Germano Rischioni |
| collection | DOAJ |
| description | 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 and random forest regression, which can perform nonlinear regression, can achieve this goal. This work considers fully polarimetric airborne radar datasets captured with German Aerospace Center's (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 semiempirical surface roughness model studied in the previous work. |
| first_indexed | 2024-04-09T19:31:05Z |
| format | Article |
| id | doaj.art-7ed13463e2e54b90b72c324d9c2b4e87 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-09T19:31:05Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-7ed13463e2e54b90b72c324d9c2b4e872023-04-04T23:00:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-01163070308210.1109/JSTARS.2023.325805910073636Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture RadarLucas Germano Rischioni0https://orcid.org/0009-0007-0216-3267Arun Babu1https://orcid.org/0000-0002-3973-1666Stefan V. Baumgartner2https://orcid.org/0000-0002-8337-6825Gerhard Krieger3https://orcid.org/0000-0002-4548-0285Microwaves and Radar Institute, German Aerospace Center (DLR), Weßling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), Weßling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), Weßling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), Weßling, GermanyAirborne 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 and random forest regression, which can perform nonlinear regression, can achieve this goal. This work considers fully polarimetric airborne radar datasets captured with German Aerospace Center's (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 semiempirical surface roughness model studied in the previous work.https://ieeexplore.ieee.org/document/10073636/Additive noisemachine learningsurface roughnesssynthetic aperture radar (SAR)vehicle safety |
| spellingShingle | Lucas Germano Rischioni Arun Babu Stefan V. Baumgartner Gerhard Krieger Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Additive noise machine learning surface roughness synthetic aperture radar (SAR) vehicle safety |
| title | Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar |
| title_full | Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar |
| title_fullStr | Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar |
| title_full_unstemmed | Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar |
| title_short | Machine Learning Approaches for Road Condition Monitoring Using Synthetic Aperture Radar |
| title_sort | machine learning approaches for road condition monitoring using synthetic aperture radar |
| topic | Additive noise machine learning surface roughness synthetic aperture radar (SAR) vehicle safety |
| url | https://ieeexplore.ieee.org/document/10073636/ |
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