Contextual learning in ground-penetrating radar data using Dirichlet process priors

In landmine detection applications, fluctuation of environmental and operating conditions can limit the performance of sensors based on ground-penetrating radar (GPR) technology. As these conditions vary, the classification and fusion rules necessary for achieving high detection and low false alarm rates may change. Therefore, context-dependent learning algorithms that exploit contextual variations of GPR data to alter decision rules have been considered for improving the performance of landmine detection systems. Past approaches to contextual learning have used both generative and discriminative methods to learn a probabilistic mixture of contexts, such as a Gaussian mixture, fuzzy c-means clustering, or a mixture of random sets. However, in these approaches the number of mixture components is pre-defined, which could be problematic if the number of contexts in a data collection is unknown a priori. In this work, a generative context model is proposed which requires no a priori knowledge in the number of mixture components. This was achieved through modeling the contextual distribution in a physics-based feature space with a Gaussian mixture, while also incorporating a Dirichlet process prior to model uncertainty in the number of mixture components. This Dirichlet process Gaussian mixture model (DPGMM) was then incorporated in the previously-developed Context-Dependent Feature Selection (CDFS) framework for fusion of multiple landmine detection algorithms. Experimental results suggest that when the DPGMM was incorporated into CDFS, the degree of performance improvement over conventional fusion was greater than when a conventional fixed-order context model was used. © 2011 SPIE.