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Proceedings Paper

Automatic target recognition using a feature-decomposition and data-decomposition modular neural network
Author(s): Lin-Cheng Wang; Sandor Z. Der; Nasser M. Nasrabadi
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Paper Abstract

A modular neural network classifier has been applied to the problem of automatic target recognition using forward- looking infrared (FLIR) imagery. The classifier consists of several independently trained neural networks operating on features extracted from a local portion of a target image. The classification decisions of the individual networks are combined to determine the final classification. Experiments show that decomposition of the input features results in performance superior to a fully connected network in terms of both network complexity and probability of classification. Performance of the classifier is further improved by the use of multi-resolution features and by the introduction of a higher level neural network on the top of expert networks, a method known as stacked generalization. In addition to feature decomposition, we implemented a data decomposition classifier network and demonstrated improved performance. Experimental results are reported on a large set of FLIR images.

Paper Details

Date Published: 1 April 1998
PDF: 12 pages
Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304646
Show Author Affiliations
Lin-Cheng Wang, U.S. Army Research Lab. (United States)
Sandor Z. Der, U.S. Army Research Lab. (United States)
Nasser M. Nasrabadi, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 3307:
Applications of Artificial Neural Networks in Image Processing III
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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