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Optical Engineering

Artificial neural networks for automatic target recognition
Author(s): Cindy Evors Daniell; David Kemsley; William P. Lincoln; Walter A. Tackett; Gregory A. Baraghimian
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Paper Abstract

The Self Adaptive Hierarchical Target Identification and Recognition Neural Network (SAHTIRN™) is a unique and powerful combination of state-of-the-art neural network models for automatic target recognition applications. It is a combination of three models: (1) an early vision segmentor based on the Canny edge detector, (2) a hierarchical feature extraction and pattern recognition system based on a modified Neocognitron architecture, and (3) a pattern classifier based on the backpropagation network. Hughes has extensively tested SAHTIRN™ with several ground vehicular targets using terrain board modeled IR imagery under a current neural network program sponsored by the Defense Advanced Research Projects Agency. In addition, extensive testing was conducted using several real IR and handwritten character databases. Hughes has demonstrated successful performance with 91 to 100% probability of correct classification over this wide variety of data. End-to-end system results from these experiments are provided and interim results from each stage of the SAHTIRN™ system are discussed.

Paper Details

Date Published: 1 December 1992
PDF: 11 pages
Opt. Eng. 31(12) doi: 10.1117/12.60744
Published in: Optical Engineering Volume 31, Issue 12
Show Author Affiliations
Cindy Evors Daniell, Hughes Aircraft Co. (United States)
David Kemsley, Hughes Aircraft Co. (United States)
William P. Lincoln, Hughes Aircraft Co. (United States)
Walter A. Tackett, Hughes Aircraft Co. (United States)
Gregory A. Baraghimian, Hughes Aircraft Co. (United States)


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