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Article
Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment
IEEE Transactions on Geoscience and Remote Sensing
  • 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
RIS ID
37296
Publication Date
1-1-2011
Publication Details

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.

Grant Number
ARC/DP0773879
Citation Information
Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung, Lijun Su, et al.. "Automatic Classification of Ground-Penetrating-Radar Signals for Railway-Ballast Assessment" IEEE Transactions on Geoscience and Remote Sensing Vol. 49 Iss. 10 (2011) p. 3961 - 3972
Available at: http://works.bepress.com/son_lam_phung/2/