Automatic target recognition with Bayesian networks for wide-area airborne minefield detection

A Bayesian network (BN) is a directed acyclic graphical model that encodes probabilistic relationships among variables of interest. BNs not only provide a natural and compact way to represent the domain knowledge and encode joint probability distributions, but also provide a basis for efficient probabilistic inference. We apply BNs to wide area airborne minefield detection (WAAMD) due to their powerful representation ability of encoding the domain knowledge and their flexible structural extendibility for multi-look and multi-sensor data fusion. We first design BN models for both single-look detection and multi-look and multi-sensor data fusion and then refine them via learning from data using a structural expectation-maximization (SEM) algorithm. We evaluate the performance of our landmine detection scheme using data sets collected by three airborne ground penetrating synthetic aperture radars (GPSARs) (Lynx Ku-band, Mirage stepped-frequency (0.3 - 2.8 GHz), and Veridian X-band GPSARs) from various testing sites that have different terrain and vegetation conditions. Experimental results indicate that BNs can help improve the landmine detection performance significantly. The use of BNs for multi-look and multi-sensor data fusion is also shown to provide significant false alarm reductions.