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Proceedings Paper

Comparable performance for classifier trained on real or synthetic IR-images
Author(s): Bruce A. Weber; Joseph A. Penn
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Paper Abstract

We report results that demonstrate that an infrared (IR) target classifier, trained on synthetic-images of targets and tested on real-images, can perform as well as a classifier trained on real-images alone. We also demonstrate that the sum of real and synthetic-image databases can be used to train a classifier whose performance exceeds that of classifiers trained on either database alone. After creating a large database of 80,000 synthetic-images two subset databases of 7,000 and 8,000 images were selected and used to train and test a classifier against two comparably sized, sequestered databases of real-images. Synthetic-image selection was accomplished using classifiers trained on real-images from the sequestered real-image databases. The images were chosen if they were correctly identified for both target and target aspect. Results suggest that subsets of synthetic-images can be chosen to selectively train target classifiers for specific locations and operational scenarios; and that it should be possible to train classifiers on synthetic-images that outperform classifiers trained on real-images alone.

Paper Details

Date Published: 22 October 2001
PDF: 11 pages
Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); doi: 10.1117/12.445386
Show Author Affiliations
Bruce A. Weber, Army Research Lab. (United States)
Joseph A. Penn, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4379:
Automatic Target Recognition XI
Firooz A. Sadjadi, Editor(s)

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