Neural detection of pipe signatures in ground penetrating radar images

A processing chain for the spatial analysis of the data recorded by a ground penetrating radar (GPR) is presented. In particular, the detection and localization of pipes is implemented by exploiting the a priori knowledge that a buried cylinder gives rise to a hyperbolic signature in GPR images. The image interpretation is performed by a suitably trained simple neural detector after some preprocessing steps aiming toward the enhancement of the buried objects' signatures. The algorithm has been tested on actual GPR images and compared with the information extracted by a trained human operator, and the agreement is extremely satisfying. Moreover, the possibilities and advantages to exploiting some sort of "spatial diversity" by combining the analysis of data simultaneously recorded by different antennas are presented and discussed.