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Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and Support Vector Machines

, , , , and . Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International, 2, page II-525 -II-528. (July 2008)

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.

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IEEE Xplore - Automatic Detection and Classification of Buried Objects in GPR Images Using Genetic Algorithms and ...

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