Automatic detection and classification of buried objects in GPR images using genetic algorithms and support vector machines

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.