Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads

The traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are,...

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Main Authors: Franz J. Meyer, Olaniyi A. Ajadi, Edward J. Hoppe
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Remote Sensing
Subjects:
IRI
Online Access:https://www.mdpi.com/2072-4292/12/9/1507
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spelling doaj-0b11d68079a1479384e1f6c5eb04256b2020-11-25T02:04:33ZengMDPI AGRemote Sensing2072-42922020-05-01121507150710.3390/rs12091507Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US RoadsFranz J. Meyer0Olaniyi A. Ajadi1Edward J. Hoppe2Geophysical Institute, University of Alaska Fairbanks, 2156 Koyukuk Drive, Fairbanks, AK 99775, USAGeophysical Institute, University of Alaska Fairbanks, 2156 Koyukuk Drive, Fairbanks, AK 99775, USAVirginia Transportation Research Council, 530 Edgemont Rd, Charlottesville, VA 22903, USAThe traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are, therefore, time consuming and costly when applied at the network scale. This paper studies the applicability of satellite radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band, to the network-wide mapping of pavement roughness of roads in the US. Based on a comparison of high-resolution X-band Cosmo-SkyMed images with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. Based on these findings, we developed and inverted a model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reaches an overall accuracy of 92.6%. This study illustrates the capacity of X-band SAR for pavement roughness mapping and suggests that incorporating SAR into DOT operations could be beneficial.https://www.mdpi.com/2072-4292/12/9/1507X-bandsynthetic aperture radarpavement roughnessroad surface qualityinternational roughness indexIRI
collection DOAJ
language English
format Article
sources DOAJ
author Franz J. Meyer
Olaniyi A. Ajadi
Edward J. Hoppe
spellingShingle Franz J. Meyer
Olaniyi A. Ajadi
Edward J. Hoppe
Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads
Remote Sensing
X-band
synthetic aperture radar
pavement roughness
road surface quality
international roughness index
IRI
author_facet Franz J. Meyer
Olaniyi A. Ajadi
Edward J. Hoppe
author_sort Franz J. Meyer
title Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads
title_short Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads
title_full Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads
title_fullStr Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads
title_full_unstemmed Studying the Applicability of X-Band SAR Data to the Network-Scale Mapping of Pavement Roughness on US Roads
title_sort studying the applicability of x-band sar data to the network-scale mapping of pavement roughness on us roads
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2020-05-01
description The traveling public judges the quality of a road mostly by its roughness and/or ride quality. Hence, mapping, monitoring, and maintaining adequate pavement smoothness is of high importance to State Departments of Transportation in the US. Current methods rely mostly on in situ measurements and are, therefore, time consuming and costly when applied at the network scale. This paper studies the applicability of satellite radar remote sensing data, specifically, high-resolution Synthetic Aperture Radar (SAR) data acquired at X-band, to the network-wide mapping of pavement roughness of roads in the US. Based on a comparison of high-resolution X-band Cosmo-SkyMed images with road roughness data in the form of International Roughness Index (IRI) measurements, we found that X-band radar brightness generally increases when pavement roughness worsens. Based on these findings, we developed and inverted a model to distinguish well maintained road segments from segments in need of repair. Over test sites in Augusta County, VA, we found that our classification scheme reaches an overall accuracy of 92.6%. This study illustrates the capacity of X-band SAR for pavement roughness mapping and suggests that incorporating SAR into DOT operations could be beneficial.
topic X-band
synthetic aperture radar
pavement roughness
road surface quality
international roughness index
IRI
url https://www.mdpi.com/2072-4292/12/9/1507
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