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Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data

1
Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7514 AE Enschede, The Netherlands
2
Fugro B.V., 3515 ET Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7108; https://doi.org/10.3390/s20247108
Received: 8 November 2020 / Revised: 3 December 2020 / Accepted: 8 December 2020 / Published: 11 December 2020
Satellite radar interferometry (InSAR) techniques have been successfully applied for structural health monitoring of line-infrastructure such as railway. Limited by meter-level spatial resolution of Sentinel-1 satellite radar (SAR) imagery and meter-level geolocation precision, it is still challenging to (1) categorize radar scatterers (e.g., persistent scatterers (PS)) and associate radar scatterers with actual objects along railways, and (2) identify unstable railway segments using InSAR Line of Sight (LOS) deformation time series from a single viewing geometry. In response to this, (1) we assess and improve the 3-D geolocation quality of Sentinel-1 derived PS using a 2-step method for PS 3-D geolocation improvement aided by laser scanning data; after geolocation improvement, we step-wisely classify railway infrastructure into rails, embankments and surroundings; (2) we recognize unstable rail segments by utilizing the (localized) differential settlement of rails in the normal direction (near vertical) which is yielded from the LOS deformation decomposition. We tested and evaluated the methods using 170 Sentinel-1a/b ascending data acquired between January 2017 and December 2019, over the Betuwe freight train track, in the Netherlands. The results show that 98% PS were associated with real objects with a significance level of 25%, the PS settlement measurements were generally in line with the in-situ track survey Rail Infrastructure aLignment Acquisition (RILA) measurements, and the standard deviations of the PS settlement measurements varied slightly with an average value of 6.16 mm. View Full-Text
Keywords: railway infrastructure; Sentinel-1; SAR; structural health; settlement railway infrastructure; Sentinel-1; SAR; structural health; settlement
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MDPI and ACS Style

Chang, L.; Sakpal, N.P.; Elberink, S.O.; Wang, H. Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data. Sensors 2020, 20, 7108. https://doi.org/10.3390/s20247108

AMA Style

Chang L, Sakpal NP, Elberink SO, Wang H. Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data. Sensors. 2020; 20(24):7108. https://doi.org/10.3390/s20247108

Chicago/Turabian Style

Chang, Ling; Sakpal, Nikhil P.; Elberink, Sander O.; Wang, Haoyu. 2020. "Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data" Sensors 20, no. 24: 7108. https://doi.org/10.3390/s20247108

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