Fusion of FLIR automatic target recognition algorithms

Abstract

In this paper, we investigate several fusion techniques for designing a composite classifier to improve the performance (probability of correct classification) of forward-looking infrared (FLIR) automatic target recognition (ATR). The motivation behind the fusion of ATR algorithms is that if each contributing technique in a fusion algorithm (composite classifier) emphasizes on learning at least some features of the targets that are not learned by other contributing techniques for making a classification decision, a fusion of ATR algorithms may improve overall probability of correct classification of the composite classifier. In this research, we propose to use four ATR algorithms for fusion. The individual performance of the four contributing algorithms ranges from 73.5% to about 77% of probability of correct classification on the testing set. The set of correctly classified targets by each contributing algorithm usually has a substantial overlap with the set of correctly identified targets by other algorithms (over 50% for the four algorithms being used in this research). There is also a significant part of the set of correctly identified targets that is not shared by all contributing algorithms. The size of this subset of correctly identified targets generally determines the extent of the potential improvement that may result from the fusion of the ATR algorithms. In this research, we propose to use Bayes classifier, committee of experts, stacked-generalization, winner-takes-all, and ranking-based fusion techniques for designing the composite classifiers. The experimental results show an improvement of more than 6.5% over the best individual performance.