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

Chang, Ling; Sakpal, Nikhil; Elberink, Sander Oude; Wang, Hao Yu

doi:10.3390/s20247108

Cited by 18 publications

(10 citation statements)

References 43 publications

(52 reference statements)

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“…A passive angle repeater (CR) was used to improve the accuracy of the X-band (TerraSAR-X) PS geolocation, which reduces the uncertainty of a geographic location to a subcentimeter level [20]. LiDAR data were successfully used to improve the Radarsat-2 (extra fine mode) and Sentinel-1A/B derived PS geolocation along a railway line [21,22].…”

Section: Introductionmentioning

confidence: 99%

Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China

Kaufmann

et al. 2021

Remote Sensing

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Spaceborne interferometric synthetic aperture radar (InSAR) methodology has been widely successfully applied to measure urban surface micro slow subsidence. However, the accuracy is still limited by the spatial resolution of currently operating SAR systems and the lacking precision of geolocation of the respective scatters. In this context, high-precision urban models, as provided by the active laser point cloud methodology through light detection and ranging (LiDAR) techniques, can assist in improving the geolocation quality of InSAR-derived permanent scatters (PS) and provide the precise contour of buildings for hazard analysis. This paper proposes to integrate InSAR and LiDAR technologies for an improved detailed analysis of subsidence levels and a hazard assessment for buildings in the urban environment. By the use of LiDAR data, most building contours in the main subsidence area were extracted and SAR positioning of buildings via PS points was refined more precisely. The workflow for the proposed method includes the monitoring of land subsidence by the TS-InSAR technique, the geolocation improvement of InSAR-derived PS, and building contour extraction by LiDAR data. Furthermore, a reasonable hazard assessment system of land subsidence was developed. Significant vertical subsidence of −40 to 12 mm per year was detected by the analysis of multisensor SAR images. The land subsidence rates in the Shenzhen District obviously follow certain spatial patterns. Most stable areas are located in the middle and northeast of Shenzhen except for some areas in Houhai, the Qianhai Bay, and the Wankeyuncheng. An additional hazard assessment of land subsidence reveals that the subsidence of buildings is mainly caused by the construction of new buildings and some by underground activities. The research results of this paper can provide a useful synoptic reference for urban planning and help reducing land subsidence in Shenzhen.

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Section: Introductionmentioning

confidence: 99%

Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China

Kaufmann

et al. 2021

Remote Sensing

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“…Mobile satellite measurement methods are also being introduced, in which (apart from GNSS receivers) inertial navigation system (INS) devices [ 12 ] are used as supporting devices, as well as optical methods such as terrestrial laser scanning (TLS) [ 13 ]. Research is being conducted on the possibility of using systems consisting of satellite receivers mounted on various types of vehicles [ 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

The Procedure of Identifying the Geometrical Layout of an Exploited Railway Route Based on the Determined Curvature of the Track Axis

Koc

2022

Sensors

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This paper presents a detailed algorithm for determining the curvature of a track axis with the use of a moving chord method, and then discusses the procedure for identifying the geometric layout of an exploited railway route on the basis of the determined curvature. In the moving chord method, the measured coordinates of the track axis allow one to directly determine the existence of the horizontal curvature without the need for additional measurements. This enables comprehensively identifying the existing geometric elements—straight lines, circular arcs, and transition curves. The conducted activities were illustrated with a calculation example, including a 5.5 km long test section with five areas of directional change. This showed a sequential procedure that led to the solution of the given problem. Based on the curvature diagram, the coordinates of the segmentation points, which define the connections of the existing geometric elements with each other, were determined.

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“…Sentinel-1 carries a C-band sensor, has a short repetition interval of 6 days and the data is free of charge, which makes it an excellent choice for In-SAR time series analysis and monitoring tasks. However, linear traffic features like highways, railways and bridges are difficult to monitor with Sentinel-1, because their spatial extend is small compared to the spatial resolution of a Sentinel-1 pixel (North et al, 2017;Chang et al, 2020). Sentinel-1 provides medium resolution images of roughly 5m×20m in range and azimuth, respectively.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Pixel Selection Methods for Traffic Infrastructure Monitoring Using Sentinel-1 Insar

Piter¹,

Haghighi²,

Motagh

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The Synthetic Aperture Radar (SAR) satellite Sentinel-1 provides excellent traffic infrastructure monitoring capabilities due to its short revisit time, wide-scale coverage and free of charge data policy. However, pixels from Sentinel-1 have a medium spatial resolution so that traffic infrastructure is only covered by a few pixels. Moreover, Interferometric Synthetic Aperture Radar (InSAR) yields deformation time series for coherent pixels only, thus limiting the number of pixels reporting the ground motion at traffic infrastructure. Although various InSAR time series methods have been successfully applied for traffic infrastructure monitoring, the selection of appropriate methods achieving a high pixel density has not yet been evaluated. In this study, we test whether we can improve the monitoring capabilities by combining different InSAR time series methods. On the one hand, we applied widely used Stanford Method for Persistent Scatterers (StaMPS) as a baseline method to retrieve the deformation time series. On the other hand, we enhanced the phase quality in a pre-processing step using the Phase-Linking (PL) approach and afterwards estimated the deformation time series also with StaMPS based on pixels selected by PL. We compared and evaluated the achieved pixel densities from both methods at main roads, highways and railways in a study area in Germany. We found that the InSAR time series methods selected complementary sets of pixels at all traffic infrastructure types. Moreover, the results indicate a great potential for railway monitoring using Sentinel-1 InSAR due to both high pixel density and homogeneous spatial distribution of pixels. Interestingly, coherent scatterers selected by StaMPS were observed to coincide with large traffic signs at highways which show a double-bounce scattering in the amplitude images. This study shows the benefits of combining PL and StaMPS for increased pixel density at traffic infrastructure and confirms Sentinel-1 data as a suitable data source for traffic infrastructure monitoring.

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

Cited by 18 publications

References 43 publications

Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China

Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China

The Procedure of Identifying the Geometrical Layout of an Exploited Railway Route Based on the Determined Curvature of the Track Axis

Evaluation of Pixel Selection Methods for Traffic Infrastructure Monitoring Using Sentinel-1 Insar

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