A hidden Markov context model for GPR-based landmine detection incorporating stick-breaking priors

In recent years, context-dependent algorithm fusion has been proposed for improving landmine detection with ground penetrating radar (GPR) across changing environmental and operating conditions. While context-dependent fusion techniques generally assume independent observations, previous work showed that spatial information may be exploited by modeling context with a hidden Markov model (HMM). However, the degree of performance improvement was found to depend the number of states included in the HMM. In this work, stick-breaking priors were employed to automate learning of the number of HMM states, and therefore the number of contexts to consider. The improved spatially-dependent fusion technique was evaluated on GPR data collected over various targets at multiple test sites, and performance was compared to another context-dependent technique which assumed independent observations. Results illustrate the potential for nonparametric, spatially-dependent context modeling to exploit contextual information in sequentially-collected GPR data and improve overall classification performance.