Paper Info
Title
Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data.
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.
Year
DOI
Venue
2019
10.3390/rs11192298
REMOTE SENSING
Keywords
Field
DocType
railway,multi-temporal InSAR,point cloud,geo-location,deformation monitoring
Radar,Decorrelation,Interferometric synthetic aperture radar,Laser scanning,Deformation monitoring,Remote sensing,Interferometry,Lidar,Point cloud,Geology
Journal
Volume
Issue
Citations 
11
19
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Fengming Hu171.22
Freek J. van Leijen200.34
Ling Chang300.34
Jicang Wu4213.32
Ramon F. Hanssen510728.92