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

Wenbin Shao, University of Wollongong
Abdesselam Bouzerdoum, University of Wollongong
Son Lam Phung, University of Wollongong
Lijun Su, University of Wollongong - Dubai Campus
Buddhima Indraratna, University of Wollongong
Cholachat Rujikiatkamjorn, University of Wollongong

Article comments

W. Shao, A. Bouzerdoum, S. L. Phung et al., “Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 10, 2011, pp. 3961-3972. Copyright IEEE 2011. Original item available here

Abstract

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.

Suggested Citation

Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung, Lijun Su, Buddhima Indraratna, and Cholachat Rujikiatkamjorn. "Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment" IEEE Transactions on Geoscience and Remote Sensing 49.10 (2011): 3961-3972.
Available at: http://works.bepress.com/son_lam_phung/2