Neural detection for buried pipes using fully-polarimetric ground penetrating radar system

Ground penetrating radar (GPR) has been widely used for detecting and locating buried objects. However, the detection method using GPR is often subjected to operator interpretation due to large quantities of data and the presence of undesired clutter and noise. The artificial neural network (ANN) technique gives a promising approach to a more systematic and autonomous detection system. An automatic buried pipe detection algorithm, using a two-step ANN scheme on GPR data, is proposed. The detection performance of each ANN in the presence of different signal-to-noise and signal-to-clutter ratios is discussed. Estimating the linearity and orientation of the pipe by fully-polarimetric GPR is reviewed, and applying these factors to pipe detection is discussed. Examples of the two-step ANN detection application to actual field data measured by fully-polarimetric GPR is also presented.