Automatic railway instability detection using satellite SAR interferometry

Poster (2015)

Authors

R.F. Hanssen Mathematical Geodesy and Positioning - CEG
L. Chang Mathematical Geodesy and Positioning - CEG
R.P.B.J. Dollevoet Railway Engineering - CEG
Research Group
Mathematical Geodesy and Positioning (CEG) (TU Delft)
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Published Date

2015

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Mathematical Geodesy and Positioning

Abstract

Irregular settlement of railways, either due to the loading of the trains or local ground deformation, impacts its structural stability and the safety of passengers on board. Conventional methods for structural monitoring of railway use in-situ measurements, from GPS, leveling or special survey trains. These methods are expensive and can only be applied on a limited scale, either in space or time. Moreover, they are usually only used at locations where structural deformation is suspected, requiring a-priori knowledge which may not be available everywhere. Using satellite InSAR, we are able to complement these conventional methods and monitor the kinematic behavior (deformation) of railways with millimetric precision, to detect irregular settlement. Here we use a probabilistic method for InSAR time series post-processing for the automatic detection of anomalies (e.g. railway irregular settlements). It is based on statistical hypothesis testing and the B-method of testing. In this method, we first (1) build a library of canonical kinematic functions, based on physically realistic behavior, such as linear, seasonal, temperature-related, step-wise discontinuities and exponential behavior. Then, (2) we find the best model per InSAR measurement point using multiple hypotheses testing. Particularly to detect irregular settlement of railways, the localized differential deformation between two nearby points (i.e., over ‘short arcs’) is more important for railway stability than the large deformation of certain point with respect to a far-away reference point. Therefore, we apply the testing methodology on short arcs. Finally, (3) we evaluate the quality of the estimated parameters, and classify the InSAR measurement points along the railway in terms of their temporal behavior. We conclude that irregular settlement of railways can be recognized. Since there are more than 100,000 InSAR measurement points for testing, we use the B-method of testing to increase the computational efficiency and define the optimal testing settings such as the level of significance and the power of the test. Our method is applied to all railways in the Netherlands. The kinematic time series of InSAR measurement points are derived from 73 Radarsat-2 acquisitions between June 2010 and August 2015.