A pattern recognition system for extracting buried object characteristics in GPR images

Abstract

In this work, we present a pattern recognition system for the automatic analysis of ground penetrating radar (GPR) images. This system comprises pre-processing, segmentation, object detection, object material recognition, and object dimension estimation stages. Object detection is done using an unsupervised strategy based on genetic algorithms (GA) which 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. Dimension estimation is formulated within a Gaussian process (GP) regression approach. Results on synthetic images, representing random exploration scenarios, are reported and discussed.