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Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data
Joint Authors
Lu, Qingqing
Pu, Jiexin
Liu, Zhonghua
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-11-20
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Lu, Qingqing& Pu, Jiexin& Liu, Zhonghua. 2014. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data. Journal of Electrical and Computer Engineering،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1040476
Modern Language Association (MLA)
Lu, Qingqing…[et al.]. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data. Journal of Electrical and Computer Engineering No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1040476
American Medical Association (AMA)
Lu, Qingqing& Pu, Jiexin& Liu, Zhonghua. Feature Extraction and Automatic Material Classification of Underground Objects from Ground Penetrating Radar Data. Journal of Electrical and Computer Engineering. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1040476
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1040476