Feature extraction and automatic material classification of underground objects from ground penetrating radar data

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. © 2014 qingqing lu et al.