Decision support using SAR and LiDAR machine learning target classification and Bayesian belief networks

Abstract

EXP's IC3D system classifies SAR or LiDAR derived 3D point clouds using a deep representation learning approach, producing as output a vector of categorical posterior probabilities of target classifications. Such posterior probabilities are suitable observational inputs to a Bayesian belief network (BBN), such as the EXP Shadow Compass system. In concert with conditional probabilities of intermediate events depending on the observation states, the Bayesian network computes posterior probabilities for events conditionally dependent on those intermediate event states. We demonstrate this approach by computing posterior event probabilities for sample analyst scenarios with intermediate event conditional probabilities specified by analysts. Future work could include extending the Bayesian network approach to discovery of the network topology from analyst data.