Automatic mapping of linear structures in 3-dimensional space from ground-penetrating radar data

Non-invasive geophysical techniques such as ground-penetrating radar allow rapid and low-cost investigation of the shallow subsurface for the detection of such features as utilities and plant. This paper presents a pattern recognition approach based on the 3-dimensional Hough transform for the detection of extended linear targets in ground-penetrating radar data. 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 detection problem in parameter space. Due to the sparseness and variability of the data, the accumulator peak detection stage is replaced by a novel algorithm called the adaptive non-accumulated Hough transform (ANHT) 3-dimensional clustering algorithm. This technique allows the combination of qualitative site information and ground truth in order to increase the accuracy of the final result. 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. The ANHT performs substantially superior to the standard Hough transform implementation in computer memory requirement. Our experimental results on the artificial 3-dimensional linear objects indicate that method works quite well under various background conditions. The automatic mapping of linear structures in 3-dimensional space from ground-penetrating radar data is achieved by implementing the ANHT in the detection of linear targets in ground penetrating radar data.