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This study enhances satellite radar interferometry (InSAR) for railway monitoring by improving persistent scatterer (PS) geolocation and classifying infrastructure. It accurately identifies unstable rail segments using decomposed Line of Sight (LOS) deformation data.

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Area of Science:

  • Geodesy and Remote Sensing
  • Civil Engineering
  • Infrastructure Monitoring

Background:

  • Satellite radar interferometry (InSAR) is valuable for monitoring linear infrastructure like railways.
  • Limitations in Sentinel-1 SAR imagery resolution and geolocation hinder precise scatterer identification and instability detection.
  • Existing methods struggle to accurately associate radar scatterers with specific railway components and identify unstable segments from single-geometry Line of Sight (LOS) data.

Purpose of the Study:

  • To improve the 3-D geolocation accuracy of Sentinel-1 derived persistent scatterers (PS) for railway infrastructure.
  • To classify identified PS into distinct railway components (rails, embankments, surroundings).
  • To develop a method for identifying unstable railway segments using decomposed LOS deformation, focusing on vertical rail settlement.

Main Methods:

  • A two-step method was employed to enhance the 3-D geolocation of Sentinel-1 PS, incorporating laser scanning data.
  • Classified railway infrastructure components based on improved PS geolocation.
  • Decomposed LOS deformation to derive near-vertical rail settlement for instability detection.
  • Utilized 170 Sentinel-1a/b ascending datasets from January 2017 to December 2019.

Main Results:

  • Achieved 98% association of PS with real objects at a 25% significance level after geolocation improvement.
  • PS settlement measurements showed good agreement with in-situ Rail Infrastructure aLignment Acquisition (RILA) data.
  • The average standard deviation of PS settlement measurements was 6.16 mm, indicating reliable precision.

Conclusions:

  • The enhanced PS geolocation and classification method significantly improves the reliability of InSAR for railway structural health monitoring.
  • The technique effectively identifies unstable railway segments by analyzing decomposed vertical rail settlement.
  • The findings demonstrate the potential of InSAR for precise and large-scale monitoring of railway infrastructure health.