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.
The volume of image data generated in ground-penetrating radar surveys can severely restrict the practicality of this site investigation technique. This is particularly true in situations where automatic analysis or interpretation is required, as segmentation and classification tasks that utilise multivariate data are critically affected by the volume and dimensionality of the data. A general-purpose unsupervised image segmentation system is presented here for the automatic detection of image regions exhibiting different visual texture properties. A suboptimal feature selection procedure is proposed to automatically select the set of texture features best suited for the particular application. The reduction in the size of the feature set both reduces the computation time and improves the accuracy of the final classification.
Although GPR is normally capable of detecting the responsefrom buried plant, accurate detection and mapping of extended geometrical features in 3-dimensional data is often a major problem faced by the radar operators and geophysicists. This paper presents a pattern recognition approach based on the 3-dimensional Hough Transform for the detection of extended linear targets. By transforming spatially extended patterns into spatially compact features in parameter space, a difficult global detection problem in data space becomes a more easily solved local peak detectionproblem in parameter space. This technique allows the combination of qualitative site information and ground truth in order to increase the accuracy of the final result. Improved freedom of movement and accuracy is achieved by logging the movement of the GPR unit using DGPS. The user is presented with a 3-dimensional site survey report detailing the length, depth and orientations (azimuth and zenith) of any pipes, cables or the like.
Accurate and consistent manual interpretation of the vast quantities of GPR data collected during a typical survey constitute an implementation bottleneck that often limits the practicality and cost-effectiveness of this tool for rapid site investigation. Automatic unsupervised interpretation of GPR data is achieved by training a neural network to discriminate between signals originating from different types of targets and other spurious sources of reflections such as clutter. This is achieved by computing a number of statistical data descriptors for feature extraction. The neural classifier is capable of returning 3-dimensional image outlining regions of extended targets (such as reinforced concrete, disturbed soil or storage tanks) and pinpointing the location of localised targets such as mines and pipes. These reports are accompanied by a written log detailing the depths and geometry of these targets. This classifier was applied to a variety of GPR data sets gathered from a number of sites. The obtained results were in close agreement with those obtained by a trained operator manually, but in a fraction of the time. Different targets have been successfully discriminated, with a consistency greater than that of the operator. Although the system is implemented in software, the rate at which classifications are rendered lends the system Authors would like to thank the Engineering and Physical Sciences Research Council (EPSRC) for funding this work as a part of a larger project regarding automatic data-processing of ground penetrating radar. Authors would like also to express their gratitude to Zetica (UK) Ltd. for supporting this work financially, and providing sites data and related software. favourably to near real-time on-site processing and interpretation.
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