Massive Linking of Ps-Insar Deformations to a National Airborne Laser Point Cloud

Natijne, Adriaan van; Lindenbergh, Roderik; Hanssen, Ramon F.

doi:10.5194/isprs-archives-xlii-2-1137-2018

Cited by 8 publications

(10 citation statements)

References 9 publications

(11 reference statements)

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“…Other points are located in the vicinity of the structure, within a distance of 10 m. The interpretation of such points is dual: they can be associated with the structure, since the distance is within the range of 3D geolocation accuracy of the technique, or they can be related to a coherent pixel which is part of the surrounding service area. A more precise geolocation would be ensured if a local scale data correction is performed, based on sub-pixel position analysis and with the support of external positioning data, e.g., ground control points or LiDAR point cloud [54,55].…”

Section: Product 3d Locationmentioning

confidence: 99%

The Evolution of Wide-Area DInSAR: From Regional and National Services to the European Ground Motion Service

et al. 2020

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This study is focused on wide-area deformation monitoring initiatives based on the differential interferometric SAR technique (DInSAR). In particular, it addresses the use of advanced DInSAR (A-DInSAR) techniques, which are based on large sets of synthetic aperture radar (SAR) and Copernicus Sentinel-1 images. Such techniques have undergone a dramatic development in the last twenty years: they are now capable to process big sets of SAR images and can be exploited to realize a wide-area A-DInSAR monitoring. The study describes several initiatives to establish wide-area ground motion services (GMS), both at county- and region-level. In the second part of the study, some of the key technical aspects related to wide-area A-DInSAR monitoring are discussed. Finally, the last part of the study is devoted to the European ground motion service (EGMS), which is part of the Copernicus land monitoring service. It represents the most important wide-area A-DInSAR deformation monitoring system ever developed. The study describes its main characteristics and its main products. The end of the production of the first EGMS baseline product is foreseen for the last quarter of 2021.

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Section: Product 3d Locationmentioning

confidence: 99%

The Evolution of Wide-Area DInSAR: From Regional and National Services to the European Ground Motion Service

et al. 2020

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“…A nearest neighbor search process with respect to the radar geometry estimation is used to snap the scatterers to their most likely point in the point cloud [20,21]. Considering the full covariance matrix of each radar scatterer, a Whitening transform is adopted to decorrelate the dimensions of the data coordinates.…”

Section: Snapping To the Point Cloudmentioning

confidence: 99%

“…However, often such a device is not available. Airborne LiDAR provides 3D point clouds with very high spatial density, thus LiDAR points can be found close to all radar scatterers, which makes it attractive to estimate the systematic MT-InSAR height offset based on the full CS dataset [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

Leijen

Chang

et al. 2019

Remote Sensing

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Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers.

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“…In [ 22 ], a novel method was developed to improve the PS geopositioning precision in three dimensions by decoding the PS position in a 3-D voxel and associating the PS to a physical object with known 3-D geolocation. The association of the PS interprets deformation behavior at the sub-infrastructure level [ 22 , 23 ]. In recent years studies were conducted to determine the PS geopositioning precision for high resolution SAR data [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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

Chang

Sakpal

Elberink

et al. 2020

Sensors

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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.

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Massive Linking of Ps-Insar Deformations to a National Airborne Laser Point Cloud

Cited by 8 publications

References 9 publications

The Evolution of Wide-Area DInSAR: From Regional and National Services to the European Ground Motion Service

The Evolution of Wide-Area DInSAR: From Regional and National Services to the European Ground Motion Service

Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

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

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