Enhanced buried UXO detection via GPR/EMI data fusion

This paper investigates the enhancements to detection of buried unexploded ordinances achieved by combining ground penetrating radar (GPR) data with electromagnetic induction (EMI) data. Novel features from both the GPR and the EMI sensors are concatenated as a long feature vector, on which a non-parametric classifier is then trained. The classifier is a boosting classifier based on tree classifiers, which allows for disparate feature values. The fusion algorithm was applied to a government-provided dataset from an outdoor testing site, and significant performance enhancements were obtained relative to classifiers trained solely on the GPR or EMI data. It is shown that the performance enhancements come from a combination of improvements in detection and in clutter rejection.