Automatic target detection using entropy optimized shared-weight neural networks.
Standard shared-weight neural networks previously demonstrated inferior performance to that of morphological shared-weight neural networks for automatic target detection. Empirical analysis showed that entropy measures of the features generated by the standard shared-weight neural networks were consistently lower than those generated by the morphological shared-weight neural networks. Based on this observation, an entropy maximization term was added to the standard shared-weight network objective function. In this paper, we present automatic target detection results for standard shared-weight neural networks trained with and without the added entropy term.
Khabou
M A
MA
Computer Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA.
Gader
P D
PD