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Structural health monitoring of railway infrastructure with Sentinel-1 SAR data and AHN3 DSM data

Sakpal, Nikhil (2020) Structural health monitoring of railway infrastructure with Sentinel-1 SAR data and AHN3 DSM data.

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Abstract:Synthetic aperture radar interferometry (InSAR) is a precise and efficient technique to measure the Earth’s surface deformation. A recent development in the InSAR application has opened opportunities to monitor civil infrastructure precisely with millimeter precision.In this research, we monitor the structural health of railway infrastructure and develop a methodology to pinpoint the deforming location on infrastructure with Sentinel-1 SAR data and LiDAR. The stability of the railway tracks and aligned structures like powerlines, embankments, platforms, and bridges is subject to geological characteristics of the Earth’s surface on which they are built. The stability of the structure is affected by natural (environmental)or anthropological factors. The surface movements cause instability and affect the health of the infrastructure by changing the structure’s geometry. To systematically detect the railway instability, we attempt to employ time series InSAR techniques to generate temporal dynamics of all identified InSAR measurement Persistent Scatterer (PS) points. However, interpreting the deformation’s geolocation (deformed PS points) is a challenge due to poor radar estimates; the positioning is in the order of meters. As a result, associating the PS to the actual ground object is unlikely straight forward. Recently developed, an innovative approach has been presented to improve the geolocation of PS integrating airborne laser scanning data as a reference. The PS is associated with the most likely corresponding point of LiDAR point cloud. However, the results obtained in previous studies were based on high-resolution SAR data, such as obtained by the TerraSAR-X and RadarSAT-2 (XF-mode). Due to the high-resolution, the precision of the initial PS positions can still be assumed to be relatively high. In this study, we assess the feasibility of applying the technique to medium-resolution SAR data acquired by the Sentinel-1 (S-1) satellite. The study focuses on improving the 3-D positioning precision of S-1 SAR data, and its feasibility to monitor railway infrastructure at a sub-structure level. We test and evaluate the approach on two railway corridors in the Netherlands, from a single viewing orbit. The high-precision LiDAR data set of Actueel Hoogtebestand Netherland 3 (AHN3) is referred to improve the 3-D attributes of PS. The PSI measurements for deformation monitoring are obtained along the railway within a buffer of 100 m. The PS is associated with a part of infrastructure by decoding the origin of the scatterer in a pixel. By associating at a point level, the PS 3-D positioning accuracy is improved in two steps, first by correcting satellite estimates in 2-D in azimuth and range referring a high precision elevation model. In the second step, absolute height correction is applied to PS height estimates by matching the height of the local surface elevation model. The 3-D positioning estimates after improvement lie in the order of meters. The PS positions are improved by associating PS points with LiDAR point based on geometric correlation and nearest distance in the statistical istance. The methodology was tested on two study areas with different surrounding and orientation of railway features onto the radar LOS. The PS points are well associated with LiDAR points in both areas, with 98 % of a successful match. The PS associated on the railway track every 50 m, and the deformation at sub-structure is assessed in mm level. Test for macro-level detects irregularity of 3.5 mm between the neighboring tracks and test micro-level monitoring detect irregularity of -6 mm on the rails of a single track. The PS points associated with the railway track are assessed relative to determine the health of the structure at the sub-structure.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
Link to this item:https://purl.utwente.nl/essays/85194
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