Analysis of the influence of mine and soil properties on features extracted from GPR data

Abstract

This paper presents a parametric study on the influence of target and soil properties, including depth of burial, on features extracted from ground penetrating radar (GPR) data. Understanding this influence is crucial for designing a classifier that uses these features for mine detection and identification. Two types of features have been studied. These are the Wigner-Ville distribution and geometric moments. Using a fast forward modeling program, synthetic GPR data were created for six buried objects, including two plastic minelike objects, for a wide range of soil properties and depths of burial. Both non-lossy and lossy soils were considered. From the computed data the above features were extracted and correlated with each other. The results show that the Wigner-Ville distribution performs much better in discriminating between objects than geometric moments. Furthermore, the features were found to be practically invariant to changes in mine-soil permittivity contrast and depth of burial provided that the soil is non-lossy. In the presence of losses, the GPR pulse is reshaped at the air-ground interface and as it propagates through the soil. As a result of the reshaping, the target response and hence the features can differ substantially from the non-lossy case.