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Proceedings Paper

Comparison of polynomial network and model-based target recognition
Author(s): Keith C. Drake; Richard Y. Kim; Tony Y. Kim; Owen D. Johnson
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Paper Abstract

Model-based and data-driven approaches to automatic target recognition each provide a methodology to determine the class of an unknown target. Model-based recognition is a goal-driven approach that compares a representation of the unknown target to a reference library of unknown targets. A comparator algorithm determines a degree of `match' to each reference target. Data-driven approaches use a numeric algorithm to process a set of characterization features to produce a class likelihood estimate. Each approach has advantages and limitations that should be considered for a specific implementation. This research compares a specific implementation of each of these approaches developed for an automatic target recognition system that processes multi- spectral imagery representing military targets. To provide a valid baseline to compare the performance of each approach, a common target set, characterization feature set, and performance metrics are considered.

Paper Details

Date Published: 22 June 1994
PDF: 10 pages
Proc. SPIE 2233, Sensor Fusion and Aerospace Applications II, (22 June 1994); doi: 10.1117/12.179030
Show Author Affiliations
Keith C. Drake, AbTech Corp. (United States)
Richard Y. Kim, AbTech Corp. (United States)
Tony Y. Kim, Air Force Aeronautical System Ctr. (United States)
Owen D. Johnson, Massachusetts Institute of Technology (United States)

Published in SPIE Proceedings Vol. 2233:
Sensor Fusion and Aerospace Applications II
Nagaraj Nandhakumar, Editor(s)

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