{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:29:08Z","timestamp":1760236148278,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs13214293","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T23:54:33Z","timestamp":1635292473000},"page":"4293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gabor Filter-Based Segmentation of Railroad Radargrams for Improved Rail Track Condition Assessment: Preliminary Studies and Future Perspectives"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0290-6888","authenticated-orcid":false,"given":"Gerald","family":"Zauner","sequence":"first","affiliation":[{"name":"School of Engineering, University of Applied Sciences Upper Austria, 4600 Wels, Austria"}]},{"given":"David","family":"Groessbacher","sequence":"additional","affiliation":[{"name":"Plasser & Theurer GmbH, 4021 Linz, Austria"}]},{"given":"Martin","family":"Buerger","sequence":"additional","affiliation":[{"name":"Plasser & Theurer GmbH, 4021 Linz, Austria"}]},{"given":"Florian","family":"Auer","sequence":"additional","affiliation":[{"name":"Plasser & Theurer GmbH, 4021 Linz, Austria"}]},{"given":"Giuseppe","family":"Staccone","sequence":"additional","affiliation":[{"name":"Ground Control Geophysik & Consulting GmbH, 82152 Planegg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,26]]},"reference":[{"key":"ref_1","first-page":"6","article-title":"Scattering analysis of ground-penetrating radar data to quantify railroad ballast contamination","volume":"41","author":"Xie","year":"2008","journal-title":"NDT E Int."},{"key":"ref_2","unstructured":"Zhang, Q., Eriksen, A., and Gascoyne, J. (2010, January 21\u201325). Rail radar\u2014A fast maturing tool for monitoring trackbed. Proceedings of the International Conference on Ground Penetrating Radar (GPR), Lecce, Italy."},{"key":"ref_3","unstructured":"Jol, H. (2008). Ground Penetrating Radar Theory and Applications, Elsevier. [1st ed.]."},{"key":"ref_4","unstructured":"Utsi, E. (2017). Ground Penetrating Radar: Theory and Practice, Butterworth-Heinemann."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, D., Hyslip, J., Sussman, T., and Chrismer, S. (2015). Railway Geotechnics, Taylor & Francis Ltd.","DOI":"10.1201\/b18982"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1016\/j.proeng.2016.06.120","article-title":"Railways Track Characterization Using Ground Penetrating Radar","volume":"143","author":"Fontul","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shao, W., Bouzerdoum, A., Phung, S.L., Su, L., Indraratna, B., and Rujikiatkamjorn, C. (2010, January 21\u201325). Automatic classification of GPR signals. Proceedings of the International Conference on Ground Penetrating Radarm (GPR), Lecce, Italy.","DOI":"10.1109\/ICGPR.2010.5550187"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fontul, S., Paix\u00e3o, A., Solla, M., and Pajewski, L. (2018). Railway Track Condition Assessment at Network Level by Frequency Domain Analysis of GPR Data. Remote Sens., 10.","DOI":"10.3390\/rs10040559"},{"key":"ref_9","first-page":"4","article-title":"Data Analysis Techniques for GPR Used for Assessing Railroad Ballast in High Radio-Frequency Environment","volume":"136","author":"Xie","year":"2010","journal-title":"J. Transp. Eng."},{"key":"ref_10","first-page":"4","article-title":"Development of a time\u2013frequency approach to quantify railroad ballast fouling condition using ultra-wide band ground-penetrating radar data","volume":"11","author":"Xie","year":"2010","journal-title":"Int. J. Pavement Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3141\/2289-13","article-title":"Ground-Penetrating Radar Data to Develop Wavelet Technique for Quantifying Railroad Ballast\u2013Fouling Conditions","volume":"2289","author":"Shangguan","year":"2012","journal-title":"Transp. Res. Rec."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.conbuildmat.2017.02.110","article-title":"Railway ballast condition assessment using ground-penetrating radar\u2014An experimental, numerical simulation and modelling development","volume":"140","author":"Benedetto","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ndteint.2017.05.005","article-title":"A spectral analysis of ground-penetrating radar data for the assessment of the railway ballast geometric properties","volume":"90","author":"Ciampoli","year":"2017","journal-title":"NDT E Int."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bianchini Ciampoli, L., Calvi, A., and D\u2019Amico, F. (2019). Railway Ballast Monitoring by GPR: A Test Site Investigation. Remote Sens., 11.","DOI":"10.20944\/preprints201909.0237.v2"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cai, J.Q., Liu, S.X., Fu, L., and Feng, Y.Q. (2016, January 13\u201316). Detection of railway subgrade moisture content by GPR. Proceedings of the International Conference on Ground Penetrating Radar (GPR), Hong Kong, China.","DOI":"10.1109\/ICGPR.2016.7572613"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"110","DOI":"10.3141\/2159-14","article-title":"Railroad Ballast Evaluation Using Ground-Penetrating Radar","volume":"2159","author":"Leng","year":"2010","journal-title":"Transp. Res. Rec."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Brancadoro, M.G., Ciampoli, L.B., Ferrante, C., Benedetto, A., Tosti, F., and Alani, A.M. (2017, January 28\u201330). An Investigation into the railway ballast grading using GPR and image analysis. Proceedings of the International Workshop on Advanced Ground Penetrating Radar (IWAGPR), Edinburgh, Scotland.","DOI":"10.1109\/IWAGPR.2017.7996043"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/TGRS.2009.2012701","article-title":"Automatic Analysis of GPR Images: A Pattern-Recognition Approach","volume":"47","author":"Pasolli","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, X., Lei, Y., and Yang, F. (2018). Railway Subgrade Defect Automatic Recognition Method Based on Improved Faster R-CNN. Sci. Program., 2018.","DOI":"10.1155\/2018\/4832972"},{"key":"ref_20","first-page":"10","article-title":"Image representation using 2D Gabor wavelets","volume":"18","author":"Lee","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, D. (2019). Fundamentals of Image Data Mining: Analysis, Features, Classification and Retrieval, Springer. [1st ed.].","DOI":"10.1007\/978-3-030-17989-2"},{"key":"ref_22","unstructured":"Gonzalez, R., and Woods, R. (2018). Digital Image Processing, Pearson. [4th ed.]."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4293\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:23:40Z","timestamp":1760167420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":22,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214293"],"URL":"https:\/\/doi.org\/10.3390\/rs13214293","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,10,26]]}}}