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Proceedings Paper

Automatic target recognition using neural networks
Author(s): Lin-Chen Wang; Sandor Z. Der; Nasser M. Nasrabadi; Syed A. Rizvi
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Paper Abstract

Composite classifiers that are constructed by combining a number of component classifiers have been designed and evaluated on the problem of automatic target recognition (ATR) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers. A number of classifier fusion algorithms are analyzed. These algorithms combine the outputs of all the component classifiers and classifier selection algorithms, which use a cascade architecture that relies on a subset of the component classifiers. Each composite classifier is implemented and tested on a large data set of real FLIR images. The performances of the proposed composite classifiers are compared based on their classification ability and computational complexity. It is demonstrated that the composite classifier based on a cascade architecture greatly reduces computational complexity with a statistically insignificant decrease in performance in comparison to standard classifier fusion algorithms.

Paper Details

Date Published: 9 October 1998
PDF: 12 pages
Proc. SPIE 3466, Algorithms, Devices, and Systems for Optical Information Processing II, (9 October 1998); doi: 10.1117/12.326795
Show Author Affiliations
Lin-Chen Wang, Army Research Lab. (United States)
Sandor Z. Der, Army Research Lab. (United States)
Nasser M. Nasrabadi, Army Research Lab. (United States)
Syed A. Rizvi, CUNY/Staten Island (United States)


Published in SPIE Proceedings Vol. 3466:
Algorithms, Devices, and Systems for Optical Information Processing II
Bahram Javidi; Demetri Psaltis, Editor(s)

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