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Journal of Electrical and Computer Engineering
Volume 2014, Article ID 347307, 10 pages
http://dx.doi.org/10.1155/2014/347307
Research Article

Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data

Information Engineering College, Henan University of Science and Technology, Luoyang, Henan 471003, China

Received 8 September 2014; Revised 1 November 2014; Accepted 2 November 2014; Published 20 November 2014

Academic Editor: Ping Feng Pai

Copyright © 2014 Qingqing Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ground penetrating radar (GPR) is a powerful tool for detecting objects buried underground. However, the interpretation of the acquired signals remains a challenging task since an experienced user is required to manage the entire operation. Particularly difficult is the classification of the material type of underground objects in noisy environment. This paper proposes a new feature extraction method. First, discrete wavelet transform (DWT) transforms A-Scan data and approximation coefficients are extracted. Then, fractional Fourier transform (FRFT) is used to transform approximation coefficients into fractional domain and we extract features. The features are supplied to the support vector machine (SVM) classifiers to automatically identify underground objects material. Experiment results show that the proposed feature-based SVM system has good performances in classification accuracy compared to statistical and frequency domain feature-based SVM system in noisy environment and the classification accuracy of features proposed in this paper has little relationship with the SVM models.