<title>Automatic target detection in GPR data</title>

Abstract

Automatic detection and characterization of the signatures of solid reflecting targets in ground-penetrating radar data is achieved by a combination of signal and image processing stages. For the class of target under consideration, namely localized or extended linear reflecting targets such as landmines, pipes or cables, the reflections exhibit a broad hyperbolic anomaly in the region of the target. Detection and characterization of these distinctive signatures yields information about the location of the targets as well as the surrounding medium. Edge enhancement and edge processing techniques are developed to trace the envelope of the reflected wavefronts. By fitting hyperbolae to these detected edges, the location of the targets and the relative permittivity of the medium are estimated. This estimate enables the effective elimination of the background clutter that leads to spurious non-hyperbolic reflections. Thus automatic target detection and mapping is achieved without the heavy computational demands of techniques such as synthetic aperture radar processing, enabling on-site data interpretation.

Surface/subsurface mapping with an integrated rover-GPR system: A simulation approach

“…Given the formulated hyperbola for each buried object, it is possible to estimate the position of objects in the subsurface by detecting and modeling hyperbolas in B-Scans and, in particular, by detecting the apex of the hyperbolas. Hyperbola detection in GPR data poses a great challenge that many researchers have tried to overcome over the years using Hough transform, machine learning and numerous other methods [15], [16]. In the scope of this work, a simple hyperbola detection algorithm has been developed, that can be applied only on the GPR data generated using the proposed sonarbased simulated model.…”

Semi‐automatic feature extraction from GPR data for archaeology

“…Basically, there are two classes of methods to fit planes to three‐dimensional data – Hough transforms (Leavers ) and regression‐based ones (Meer et al ). Some research was done using the Hough transform to detect objects in GPR data, mainly to find linear structures such as pipes (Al‐Nuaimy et al ,; Youn and Chen ; Dell’Acqua et al ). However, it is difficult to evolve this method for the detection of more complex objects.…”

Non-Destructive Evaluation of Reinforcing Rebar Radius by Digital Image GPR Technique

“…It is becoming critically important to define size, depth, and the material properties of buried objects. The degree of accuracy with which buried objects can be defined is one of the major considerations in GPR measurements [2]. In general, the digital amplitude of GPR for the physical characterization of reinforced concrete material was rarely studied by researchers.…”