Gabor Filter-Based Segmentation of Railroad Radargrams for Improved Rail Track Condition Assessment: Preliminary Studies and Future Perspectives

Gerald Zauner, David Größbacher, Martin Bürger, Florian Auer, Giuseppe Staccone

Research output: Contribution to journalArticlepeer-review

Abstract

Ground penetrating radar (GPR) has been used for several years as a non-contact and non-destructive measurement method for rail track analysis with the aim of recording the condition of ballast and substructures. As the recorded data sets typically cover a distance of many kilometers, the evaluation of these data involves considerable effort and costs. For this reason, there is an increasing need for automated support in the evaluation of GPR measurement data. This paper presents an image segmentation pipeline based on 2D Gabor filter texture analysis, which can assist users in GPR data-based track condition assessment. Gabor filtering is used to transform a radargram image (or B-scan) into a high-dimensional, multi-resolution representation. Principal component analysis (PCA) is then applied to reduce the data content to three characteristic dimensions (namely amplitude, frequency, and local scattering) to finally obtain a segmented radargram image representing different classes of relevant image structures. From these results, quantitative measures can be derived that allow experts an improved condition assessment of the rail track.
Original languageEnglish
Article number4293
JournalRemote Sensing
Volume13
Issue number21
DOIs
Publication statusPublished - 26 Oct 2021

Keywords

  • ground penetrating radar (GPR)
  • railway
  • track condition monitoring
  • image processing
  • segmentation
  • Image segmentation
  • Ground penetrating radar (GPR)
  • Railway
  • Image processing
  • 2D Gabor filter
  • Track condition assessment

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