A New Methodology for the Detection and Extraction of Hyperbolas in GPR Images

Abstract

Reliable detection of underground infrastructure is essential for infrastructure modernization works, the implementation of BIM technology, and 3D cadasters. This requires shortening the time of data interpretation and the automation of the stage of selecting the objects. The main factor that influences the quality of radargrams is noise. The paper presents the method of data filtration with use of wavelet analyses and Gabor filtration. The authors were inspired to conduct the research by the fact that the interpretation and analysis of radargrams is time-consuming and by the wish to improve the accuracy of selection of the true objects by inexperienced operators. The authors proposed automated methods for the detection and classification of hyperboles in GPR images, which include the data filtration, detection, and classification of objects. The proposed object classification methodology based on the analytic hierarchy process method introduces a classification coefficient that takes into account the weights of the proposed conditions and weights of the coefficients. The effectiveness and quality of detection and classification of objects in radargrams were assessed. The proposed methods make it possible to shorten the time of the detection of objects. The developed hyperbola classification coefficients show promising results of the detection and classification of objects.