In this paper, a model is presented for the hyperbolic signature of a buried cylindrical target that takes into account the radius of the cylinder, This allows for cylinders of arbitrary radii to be detected and characterized uniquely from a single radargram, resulting in a more accurate estimation of the relative permittivity of the surrounding medium and of the depth, in addition to the radius information. This is achieved by subjecting the radargrams to a series of image processing stages followed by a curve-fitting procedure specifically developed for hyperbolae. The fitting technique is applied on a variety of real hyperbolic signatures that are collected from a controlled test site, The results indicate this technique is fully capable of successfully estimating the depth and radius to within 10%, which validates the method and justify the assumptions used.
In a typical GPR survey, only a small fraction of the collected data actually represent useful data (i.e. target data), whereas the majority of the data is considered redundant. The first of the post‐processing stages, which relies heavily on a skilled operator, involves indicating those areas that may contain targets and suppressing others. Consequently, this process consumes considerable amounts of time and effort, apart from the fact that the existence of the human factor at this critical stage invariably introduces inconsistency and error into the interpretation. In this paper, automatic detection and segmentation techniques for GPR data are discussed and compared. The techniques rely on the computation of certain features from which a neural network is then able to arrive at a decision whether to classify the data segments in question as targets or otherwise. The first technique is based on extracting statistical features from A‐scan segments while the second technique computes statistical features from B‐scan regions. In the third technique, some regional properties of B‐scan segments are used to achieve discrimination not only between targets and non‐targets, but also between hyperbolic‐shaped and non‐hyperbolic‐shaped targets. All the techniques were tested on different types of GPR data collected from a variety of sites, and they proved to be very efficient in forming a robust automatic technique for data reduction and segmentation. In addition, these techniques are carried out in near real‐time enabling on‐site processing and interpretation of collected data.
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 bank of Gabor filters is used to achieve multi-channel filtering analogous to the processing of information in the visual cortex. 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. Unsupervised neural networks are employed to segment and classify the image data into categories. Results presented for two typical radargrams match closely the interpretation of a human expert, in the fraction of the time. Applications of this technique can be found in disciplines as diverse as medical image analysis and ultrasonic non-destructive testing.
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