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

1
School of Engineering, University of Applied Sciences Upper Austria, 4600 Wels, Austria
2
Plasser & Theurer GmbH, 4021 Linz, Austria
3
Ground Control Geophysik & Consulting GmbH, 82152 Planegg, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Tarek Zayed, Thikra Dawood, Mona Abouhamad, Mohammed Alsharqawi and Fabio Tosti
Remote Sens. 2021, 13(21), 4293; https://doi.org/10.3390/rs13214293
Received: 30 August 2021 / Revised: 22 October 2021 / Accepted: 22 October 2021 / Published: 26 October 2021
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. View Full-Text
Keywords: ground penetrating radar (GPR); railway; track condition assessment; image processing; 2D Gabor filter; image segmentation ground penetrating radar (GPR); railway; track condition assessment; image processing; 2D Gabor filter; image segmentation
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MDPI and ACS Style

Zauner, G.; Groessbacher, D.; Buerger, M.; Auer, F.; Staccone, G. Gabor Filter-Based Segmentation of Railroad Radargrams for Improved Rail Track Condition Assessment: Preliminary Studies and Future Perspectives. Remote Sens. 2021, 13, 4293. https://doi.org/10.3390/rs13214293

AMA Style

Zauner G, Groessbacher D, Buerger M, Auer F, Staccone G. Gabor Filter-Based Segmentation of Railroad Radargrams for Improved Rail Track Condition Assessment: Preliminary Studies and Future Perspectives. Remote Sensing. 2021; 13(21):4293. https://doi.org/10.3390/rs13214293

Chicago/Turabian Style

Zauner, Gerald, David Groessbacher, Martin Buerger, Florian Auer, and Giuseppe Staccone. 2021. "Gabor Filter-Based Segmentation of Railroad Radargrams for Improved Rail Track Condition Assessment: Preliminary Studies and Future Perspectives" Remote Sensing 13, no. 21: 4293. https://doi.org/10.3390/rs13214293

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