Comparable performance for classifier trained on real or synthetic IR-images

Bruce A. Weber; Joseph A. Penn

doi:10.1117/12.445386

22 October 2001 Comparable performance for classifier trained on real or synthetic IR-images

Bruce A. Weber, Joseph A. Penn

Proceedings Volume 4379, Automatic Target Recognition XI; (2001) https://doi.org/10.1117/12.445386
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Bruce A. Weber and Joseph A. Penn "Comparable performance for classifier trained on real or synthetic IR-images", Proc. SPIE 4379, Automatic Target Recognition XI, (22 October 2001); https://doi.org/10.1117/12.445386

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