A comparison of segmentation techniques for target extraction in ground‐penetrating radar data

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.