Modeling synthetic infrared data for classifier development

Bruce A. Weber; Joseph A. Penn

doi:10.1117/12.395562

17 August 2000 Modeling synthetic infrared data for classifier development

Bruce A. Weber, Joseph A. Penn

Proceedings Volume 4050, Automatic Target Recognition X; (2000) https://doi.org/10.1117/12.395562
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

In an effort to improve the usefulness of computer classifiers for military applications, the U.S. Army Research Laboratory has begun to develop a database of synthetic infrared target chips. Once created, this database will aid in the training and testing of both human and computer classifiers, and will provide a way to train classifiers on targets and clutter environments with little real data available. Results presented below will show that classifier performance trained on synthetic data is improving but is, in general, poorer than when trained on real data, that individual synthetic target models perform much better than other models, providing evidence that better overall performance may yet be achievable, that synthetic data thus far created is highly self-similar and/or to some unknown extent represents real data not included in our database, and that enhanced performance of classifiers trained on small amounts of real data can be achieved by adding limited amounts of synthetic data.

Citation Download Citation

Bruce A. Weber and Joseph A. Penn "Modeling synthetic infrared data for classifier development", Proc. SPIE 4050, Automatic Target Recognition X, (17 August 2000); https://doi.org/10.1117/12.395562

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