Hidden Markov Models (HMMs) are useful tools for landmine detection and discrimination using Ground Penetrating
Radar (GPR). The performance of HMMs, as well as other feature-based methods, depends not only on the design of the
classifier but on the features. Traditionally, algorithms for learning the parameters of classifiers have been intensely
investigated while algorithms for learning parameters of the feature extraction process have been much less intensely
investigated. In this paper, we describe experiments for learning feature extraction and classification parameters
simultaneously in the context of using hidden Markov models for landmine detection.