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Proceedings Paper

Searching for a fast alternative to KNN for infrared ATR
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Paper Abstract

Automatic target recognition (ATR) using an infrared (IR) sensor is a particularly appealing combination, because an IR sensor can overcome various types of concealment and works in both day and night conditions. We present a system for ATR on low resolution IR imagery. We describe the system architecture and methods for feature extraction and feature subset selection. We also compare two types of classifier, K-Nearest Neighbors (KNN) and Random Decision Tree (RDT). Our experiments test the recognition accuracy of the classifiers, within our ATR system, on a variety of IR datasets. Results show that RDT and KNN achieve comparable performance across the tested datasets, but that RDT requires significantly less retrieval time on large datasets and in high dimensional feature spaces. Therefore, we conclude that RDT is a promising classifier to enable a robust, real time ATR solution.

Paper Details

Date Published: 7 May 2007
PDF: 9 pages
Proc. SPIE 6566, Automatic Target Recognition XVII, 65660A (7 May 2007); doi: 10.1117/12.719616
Show Author Affiliations
Ross S. Eaton, Charles River Analytics (United States)
Magnùs S. Snorrason, Charles River Analytics (United States)

Published in SPIE Proceedings Vol. 6566:
Automatic Target Recognition XVII
Firooz A. Sadjadi, Editor(s)

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