Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment

The ground-penetrating radar (GPR) has been widely used in many applications. However, the processing and interpretation of the acquired signals remain challenging tasks since an experienced user is required to manage the entire operation. In this paper, we present an automatic classification system...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2011-10, Vol.49 (10), p.3961-3972
Main Authors: Wenbin Shao, Bouzerdoum, Abdesselam, Son Lam Phung, Lijun Su, Indraratna, B., Rujikiatkamjorn, C.
Format: Article
Language:English
Subjects:
Applied geophysics
Discrete Fourier transforms
Earth sciences
Earth, ocean, space
Electronic ballasts
Exact sciences and technology
Feature extraction
Ground penetrating radar
Ground-penetrating radar (GPR) processing
Internal geophysics
Kernel
Rail transportation
railway-ballast assessment
support vector machine (SVM)
Time frequency analysis
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Summary:The ground-penetrating radar (GPR) has been widely used in many applications. However, the processing and interpretation of the acquired signals remain challenging tasks since an experienced user is required to manage the entire operation. In this paper, we present an automatic classification system to assess railway-ballast conditions. It is based on the extraction of magnitude spectra at salient frequencies and their classification using support vector machines. The system is evaluated on real-world railway GPR data. The experimental results show that the proposed method efficiently represents the GPR signal using a small number of coefficients and achieves a high classification rate when distinguishing GPR signals reflected by ballasts of different conditions.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2011.2128328