Automatic target recognition using a multilayer convolution neural network

Vincent Mirelli; Syed A. Rizvi

doi:10.1117/12.243153

14 June 1996 Automatic target recognition using a multilayer convolution neural network

Vincent Mirelli, Syed A. Rizvi

Proceedings Volume 2755, Signal Processing, Sensor Fusion, and Target Recognition V; (1996) https://doi.org/10.1117/12.243153
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

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.

Citation Download Citation

Vincent Mirelli and Syed A. Rizvi "Automatic target recognition using a multilayer convolution neural network", Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); https://doi.org/10.1117/12.243153

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available