This work presents a novel pattern recognition approach for the automatic analysis of ground penetrating radar (GPR) images. The developed system comprises pre-processing, segmentation, object detection and material recognition stages. Object detection is done using an innovative unsupervised strategy based on genetic algorithms (GA) that allows to localize linear/hyperbolic patterns in GPR images. Object material recognition is approached as a classification issue, which is solved by means of a support vector machine (SVM) classifier. Results on synthetic images show that the proposed system exhibits promising performances both in terms of object detection and material recognition.
Описание
IEEE Xplore - Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and ...
%0 Conference Paper
%1 4779044
%A Pasolli, E.
%A Melgani, F.
%A Donelli, M.
%A Attoui, R.
%A de Vos, M.
%B Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
%D 2008
%K GA gpr svm type
%P II-525 -II-528
%R 10.1109/IGARSS.2008.4779044
%T Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and Support Vector Machines
%U http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4779044&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4757194%2F4778902%2F04779044.pdf%3Farnumber%3D4779044
%V 2
%X This work presents a novel pattern recognition approach for the automatic analysis of ground penetrating radar (GPR) images. The developed system comprises pre-processing, segmentation, object detection and material recognition stages. Object detection is done using an innovative unsupervised strategy based on genetic algorithms (GA) that allows to localize linear/hyperbolic patterns in GPR images. Object material recognition is approached as a classification issue, which is solved by means of a support vector machine (SVM) classifier. Results on synthetic images show that the proposed system exhibits promising performances both in terms of object detection and material recognition.
@inproceedings{4779044,
abstract = {This work presents a novel pattern recognition approach for the automatic analysis of ground penetrating radar (GPR) images. The developed system comprises pre-processing, segmentation, object detection and material recognition stages. Object detection is done using an innovative unsupervised strategy based on genetic algorithms (GA) that allows to localize linear/hyperbolic patterns in GPR images. Object material recognition is approached as a classification issue, which is solved by means of a support vector machine (SVM) classifier. Results on synthetic images show that the proposed system exhibits promising performances both in terms of object detection and material recognition.},
added-at = {2012-10-22T11:29:28.000+0200},
author = {Pasolli, E. and Melgani, F. and Donelli, M. and Attoui, R. and de Vos, M.},
biburl = {https://www.bibsonomy.org/bibtex/24361b38fc8c3ffb66abc40d88ecce0d0/andre@ismll},
booktitle = {Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International},
description = {IEEE Xplore - Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and ...},
doi = {10.1109/IGARSS.2008.4779044},
interhash = {5ed6842f3b5a191066c476c6618b9415},
intrahash = {4361b38fc8c3ffb66abc40d88ecce0d0},
keywords = {GA gpr svm type},
month = {july},
pages = {II-525 -II-528},
timestamp = {2012-10-22T11:29:28.000+0200},
title = {Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and Support Vector Machines},
url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=4779044&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4757194%2F4778902%2F04779044.pdf%3Farnumber%3D4779044},
volume = 2,
year = 2008
}