Impact study on MuSES-generated EO/IR synthetic imagery for machine learning algorithm development

Corey D. Packard; Mark D. Klein; Alex J. Klaschus; Zachary J. Edel; Jacob N. Hendrickson; Eli J. Datema; Scott C. Gibbs; Peter L. Rynes

doi:10.1117/12.2680322

17 October 2023 Impact study on MuSES-generated EO/IR synthetic imagery for machine learning algorithm development

Corey D. Packard, Mark D. Klein, Alex J. Klaschus, Zachary J. Edel, Jacob N. Hendrickson, Eli J. Datema, Scott C. Gibbs, Peter L. Rynes

Author Affiliations +

Proceedings Volume 12742, Artificial Intelligence for Security and Defence Applications; 127420Q (2023) https://doi.org/10.1117/12.2680322
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

The capabilities of machine learning algorithms for observing image-based scenes and recognizing embedded targets have been demonstrated by data scientists and computer vision engineers. Performant algorithms must be well-trained to complete such a complex task automatically, and this requires a large set of training data on which to base statistical predictions. For electro-optical infrared (EO/IR) remote sensing applications, a substantial image database with suitable variation is necessary. Numerous times of day, sensor perspectives, scene backgrounds, weather conditions and target mission profiles could be included in the training image set to ensure sufficient variety. Acquiring such a diverse image set from measured sources can be a challenge; generating synthetic imagery with appropriate features is possible but must be done with care if robust training is to be accomplished. In this work, MuSES™ and CoTherm™ are used to generate synthetic EO/IR remote sensing imagery of various high-value targets with a range of environmental factors. The impact of simulation choices on image generation and algorithm performance is studied with standard computer vision deep learning convolutional neural networks and a measured imagery benchmark. Differences discovered in the usage and efficacy of synthetic and measured imagery are reported.

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(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Corey D. Packard, Mark D. Klein, Alex J. Klaschus, Zachary J. Edel, Jacob N. Hendrickson, Eli J. Datema, Scott C. Gibbs, and Peter L. Rynes "Impact study on MuSES-generated EO/IR synthetic imagery for machine learning algorithm development", Proc. SPIE 12742, Artificial Intelligence for Security and Defence Applications, 127420Q (17 October 2023); https://doi.org/10.1117/12.2680322

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