A Novel ATR Classifier Exploiting Pose Information

This paper describes a new architecture for ATR classifiers based on the premise that the pose of the target is known within a precision of 10 degrees. We recently developed such a pose estimator. The advantage of our classifier is that the input space complexity is decreased by the information of the pose, which enables fewer features to classify targets with higher degree of accuracy. Moreover, the training of the classifier can be done discriminantly, which also improves performance. Although our work is very preliminary, performance comparable with the standard template matcher was obtained in the MSTAR database.