Neural network target identifier based on statistical features of GPR signals

Abstract

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.