Hough Transform and Kirchhoff Migration for Supervised GPR Data Analysis

Abstract

Ground penetrating radar (GPR) is a widely used technology for detecting buried objects in the subsoil. Radar measurements are usually depicted as radargram images, including distorted hyperbola-like shapes representing pipes running non-parallel to the measurement trace. Also because of the heterogeneity of subsoil, human experts are usually analysing radargrams only in a semi-automatic way by adjusting parameters of the detection models (exposed by the software used) to get best detection results. To gain a set of approximate hyperbola apex positions, unsupervised methods such as the Hough transform (HT) or Kirchhoff Migration are often used. By having high-quality, large-scale real-world measurement data collected on a specialized test site at hand, we both (a) analyse differences and similarities of the HT and Kirchhoff Migration quantitatively and analytically with respect to different preprocessing techniques, and (b) embed results from either technique into a supervised framework. The primary contribution of this paper is the conduction of an exhaustive experiment, not only showing their equivalence, but also showing that their application for the automated analysis of GPR data, unlike it is currently assumes, does not improve the detection performance significantly.