| Full text | |
| Author(s): |
dos Santos, Vinicius Rafael N.
[1]
;
Al-Nuaimy, Waleed
[2]
;
Porsani, Jorge Luis
[1]
;
Tomita Hirata, Nina S.
[3]
;
Alzubi, Hamzah S.
[2]
Total Authors: 5
|
| Affiliation: | [1] Univ Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer IAG, Dept Geofis, BR-05508090 Sao Paulo - Brazil
[2] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside - England
[3] Univ Sao Paulo, Inst Matemat & Estat IME, Dept Ciencia Computaceo, BR-05508090 Sao Paulo - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | JOURNAL OF APPLIED GEOPHYSICS; v. 100, p. 32-43, JAN 2014. |
| Web of Science Citations: | 7 |
| Abstract | |
The accuracy of detecting buried targets using ground penetrating radar (GPR) depends mainly on features that are extracted from the data. The objective of this study is to test three spectral features and evaluate the quality to provide a good discrimination among three types of materials (concrete, metallic and plastic) using the 200 MHz GPR system. The spectral features which were selected to check the interaction of the electromagnetic wave with the type of material are: the power spectral density (PSD), short-time Fourier transform (STFT) and the Wigner-Ville distribution (WVD). The analyses were performed with simulated data varying the sizes of the targets and the electrical properties (relative dielectric permittivity and electrical conductivity) of the soil. To check if the simulated data are in accordance with the real data, the same approach was applied on the data obtained in the IAG/USP test site. A noticeable difference was found in the amplitude of the studies' features in the frequency domain and these results show the strength of the signal processing to try to differentiate buried materials using GPR, and so can be used in urban planning and geotechnical studies. (C) 2013 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 09/05882-6 - Automatic detection and classification of buried interferences with GPR using Artificial Neural Networks (ANNs): Study on IAG/USP test site. |
| Grantee: | Vinicius Rafael Neris dos Santos |
| Support Opportunities: | Scholarships in Brazil - Doctorate |