Data and Decision Level Fusion of Temporal Information for Automatic Target Recognition

Automatic Target Recognition (ATR) is a demanding application that requires separation of targets from a noisy background in a sequence of images. In our previous work [5] the background was adaptively described using twodimensional filters designed by Principle Component Analysis on sampled two-dimensional image patches. Significant improvements in performance have been obtained by decision level fusion over time. In this paper we extend this idea and utilise the temporal nature of the data further to design a set of three-dimensional texture filters based on randomly sampled threedimensional image patches . We show that by virtue of data level fusion, using these new filters the true-positive rate can be increased further whilst reducing the number of false-positives.