%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 }