Ground penetrating radar has been widely used in many areas. However, the processing and interpretation of acquired signals remains a challenging task since it requires experienced users to manage the whole operations. In this paper, we propose an automatic classification system to categorise GPR signals based on magnitude spectrum amplitudes and support vector machines. The system is tested on a real-world GPR data set. The experimental results show that our system can correctly distinguish ground penetrating radar signals reflected by different materials.
Описание
IEEE Xplore - Automatic classification of GPR signals
%0 Conference Paper
%1 5550187
%A Shao, W.
%A Bouzerdoum, A.
%A Phung, S.L.
%A Su, L.
%A Indraratna, B.
%A Rujikiatkamjorn, C.
%B Ground Penetrating Radar (GPR), 2010 13th International Conference on
%D 2010
%K GPR SVM
%P 1 -6
%R 10.1109/ICGPR.2010.5550187
%T Automatic classification of GPR signals
%U http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5550187&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5550187
%X Ground penetrating radar has been widely used in many areas. However, the processing and interpretation of acquired signals remains a challenging task since it requires experienced users to manage the whole operations. In this paper, we propose an automatic classification system to categorise GPR signals based on magnitude spectrum amplitudes and support vector machines. The system is tested on a real-world GPR data set. The experimental results show that our system can correctly distinguish ground penetrating radar signals reflected by different materials.
@inproceedings{5550187,
abstract = {Ground penetrating radar has been widely used in many areas. However, the processing and interpretation of acquired signals remains a challenging task since it requires experienced users to manage the whole operations. In this paper, we propose an automatic classification system to categorise GPR signals based on magnitude spectrum amplitudes and support vector machines. The system is tested on a real-world GPR data set. The experimental results show that our system can correctly distinguish ground penetrating radar signals reflected by different materials.},
added-at = {2012-11-20T10:16:19.000+0100},
author = {Shao, W. and Bouzerdoum, A. and Phung, S.L. and Su, L. and Indraratna, B. and Rujikiatkamjorn, C.},
biburl = {https://www.bibsonomy.org/bibtex/2c3ef162580f13aa38a5d7d00ad6c1a26/andre@ismll},
booktitle = {Ground Penetrating Radar (GPR), 2010 13th International Conference on},
description = {IEEE Xplore - Automatic classification of GPR signals},
doi = {10.1109/ICGPR.2010.5550187},
interhash = {be8c44b2e252638aecc88f4ff6daa8b8},
intrahash = {c3ef162580f13aa38a5d7d00ad6c1a26},
keywords = {GPR SVM},
month = {june},
pages = {1 -6},
timestamp = {2012-11-20T10:16:19.000+0100},
title = {Automatic classification of GPR signals},
url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5550187&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5550187},
year = 2010
}