Landmine detection using an ensemble of continuous HMMs with multiple features

We propose a landmine detection algorithm using ground penetrating radar (GPR) data that uses multiple features and an ensemble of continuous hidden Markov models (CHMMs). Our approach is motivated by the need for different features and multiple models to capture the large variations of mine and clutter types and the different environmental conditions. First, for each feature representation, we fit one continuous HMM to each of the N alarm signatures. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in one N × N log-likelihood distance matrix for each feature. These log-likelihood matrices are combined and partitioned to identify K clusters of alarms where each cluster contain alarms that are similar with respect to one or more features. Second, we learn the parameters of one HMM per group using the relevant features identified in the clustering phase. The mixture of all learned models would be used to build a descriptive model of the data. The third step of our approach consists of training a Neural Network (NN) to fuse the results of the mixture models and assign a final confidence value. Results on large and diverse Ground Penetrating Radar data collections show that the proposed approach can identify meaningful and coherent HMM models that describe different properties of the data. Our initial experiments have also indicated that the proposed mixture model outperforms the baseline mixture model that uses one single feature representation.