Automatic target recognition using a multilayer convolution neural network
Vincent Mirelli, Syed A. Rizvi · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
We present the design of an automatic target recognition (ATR) system that is part of a hybrid system incorporating some domain knowledge. This design obtains an adaptive trade-off between training performance and memorization capacity by decomposing the learning process with respect to a relevant hidden variable. The probability of correct classification over 10 target classes is 73.4%. The probability of correct classification between the target- class and the clutter-class (where clutters are the false alarms obtained from another ATR) is 95.1%. These performances can be improved by reducing the memorization capacity of this system because its estimation shows that it is too large.