A Bayesian Approach For Classification Of Buried Objects Using Non-Parametric Prior Model

We used kernel density estimation to build a-priori probability distributions on the vector of features used to characterize unexploded ordnance from electromagnetic induction sensor data. This priori information is then used in a Bayesian framework to develop a new suite of estimation and classification algorithms. Based on this prior information several classification algorithms are developed in feature and signal space. Results using real field data show more robust estimation and significant improvement in classification performance for signal space classifiers comparing to conventional Gaussian approximation to the density of the features.