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

Authors

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

RIS ID

37296

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.

ANZSRC / FoR Code

080109 Pattern Recognition and Data Mining, 090609 Signal Processing

Grant Number

ARC/DP0773879

Additional Grant Number

http://purl.org/au-research/grants/ARC/DP0773879