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

Fengming Hu, Freek van Leijen, Ling Chang, Jicang Wu, Ramon F. Hanssen

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Abstract

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.
Original languageEnglish
Article number2298
Pages (from-to)1-19
Number of pages19
JournalRemote sensing
Volume11
Issue number19
DOIs
Publication statusPublished - 2 Oct 2019

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railway
radar
monitoring
synthetic aperture radar
laser
health monitoring
positioning
infrastructure
road
geometry
matrix

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

Hu, Fengming ; van Leijen, Freek ; Chang, Ling ; Wu, Jicang ; Hanssen, Ramon F. / Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data. In: Remote sensing. 2019 ; Vol. 11, No. 19. pp. 1-19.
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abstract = "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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Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data. / Hu, Fengming; van Leijen, Freek ; Chang, Ling ; Wu, Jicang; Hanssen, Ramon F.

In: Remote sensing, Vol. 11, No. 19, 2298, 02.10.2019, p. 1-19.

Research output: Contribution to journalArticleAcademicpeer-review

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