A Bayesian method for discriminative context-dependent fusion of GPR-based detection algorithms

Ground-penetrating radar (GPR) is a very useful technology for buried threat detection applications which is capable of identifying both metallic and non-metallic objects with moderate false alarm rates. Several pattern classication algorithms have been proposed and evaluated which enable GPR systems to achieve robust per- formance. However, comparisons of these algorithms have shown that their relative performance varies with respect to the environmental context under which the GPR is operating. Context-dependent fusion has been proposed as a technique for algorithm fusion and has been shown to improve performance by exploiting the dierences in algorithm performance under dierent environmental and operating conditions. Early approaches to context-dependent fusion clustered observations in the joint condence space of all algorithms and applied fusion rules within each cluster (i.e., discriminative learning). Later approaches exploited physics-based fea- tures extracted from the background data to leverage more environmental information, but decoupled context learning from algorithm fusion (i.e., generative learning). In this work, a Bayesian inference technique which combines the generative and discriminative approaches is proposed for physics-based context-dependent fusion of detection algorithms for GPR. The method uses a Dirichlet process (DP) mixture as a model for context, and relevance vector machines (RVMs) as models for algorithm fusion. Variational Bayes is used as an approximate inference technique for joint learning of the context and fusion models. Experimental results compare the pro- posed Bayesian discriminative technique to generative techniques developed in past work by investigating the similarities and dierences in the contexts learned as well as overall detection performance. © 2012 SPIE.