title: Automatic target detection using entropy optimized shared-weight neural networks.
abstract: 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.
authors.affiliation: Computer Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO 65211, USA.
authors.forename: M A
authors.initials: MA
authors.lastname: Khabou
authors.forename: P D
authors.initials: PD
authors.lastname: Gader