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Proceedings Paper

Automatic target recognition using a multilayer convolution neural network
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Paper Abstract

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.

Paper Details

Date Published: 14 June 1996
PDF: 20 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243153
Show Author Affiliations
Vincent Mirelli, Army Research Lab. (United States)
Syed A. Rizvi, SUNY/Buffalo (United States)


Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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