Synthetic aperture radar automatic target recognition with three strategies of learning and representation

We describe a new architecture for synthetic aperture radar (SAR) automatic target recognition (ATR) based on the premise that the pose of the target is estimated within a high degree of precision. The advantage of our classi fier design is that the input space complexity is decreased with the pose i nformation, which enables fewer features to classify targets with a higher degree of accuracy. Moreover, the training of the classifier can be done di scriminantly, which also improves performance and decreases the complexity of the classifier. Three strategies of teaming and representation to build the pattern space and discriminant functions are compared: Vapnik's support vector machine (SVM), a newly developed quadratic mutual information (QMI) cost function for neural networks, and a principal component analysis exte nded recently with multiresolution (PCA-M). Experimental results obtained i n the MSTAR database show that the performance of our classifiers is better than that of standard template matching in the same dataset. We also rate the quality of the classifiers for detection using confusers, and show sign ificant improvement in rejection. (C) 2000 Society of Photo-Optical Instrum entation Engineers. [S0091-3286(00)02105-X].