CONTEXT-DEPENDENT LANDMINE DETECTION WITH GROUND-PENETRATING RADAR USING A HIDDEN MARKOV CONTEXT MODEL

Context-dependent approaches to landmine detection have been developed in recent years to exploit the sensitivity of ground-penetrating radar (GPR) to changes in environmental conditions. Previous approaches to context-dependent fusion have only considered the special case of statistically independent observations. This work proposes the use of Hidden Markov Models, trained on the GPR background, for modeling the context of observation sequences. The performances of context-dependent fusion using two statistical context models were compared in an experiment with field data. One approach utilized a Hidden Markov Context Model (HMCM), and the other utilized a Gaussian mixture. Experimental results illustrated that the HMCM improved performance of context-dependent fusion. These results suggest that spatial dependencies are an important source of contextual information for landmine detection that warrants further investigation.