Multi-spectral synthetic image generation for ground vehicle identification training

Christopher M. May; Neil A. Pinto; Jeffrey S. Sanders

doi:10.1117/12.2228727

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3 May 2016 Multi-spectral synthetic image generation for ground vehicle identification training

Christopher M. May, Neil A. Pinto, Jeffrey S. Sanders

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Proceedings Volume 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII; 98201A (2016) https://doi.org/10.1117/12.2228727
Event: SPIE Defense + Security, 2016, Baltimore, Maryland, United States

Abstract

There is a ubiquitous and never ending need in the US armed forces for training materials that provide the warfighter with the skills needed to differentiate between friendly and enemy forces on the battlefield. The current state of the art in battlefield identification training is the Recognition of Combat Vehicles (ROC-V) tool created and maintained by the Communications - Electronics Research, Development and Engineering Center Night Vision and Electronic Sensors Directorate (CERDEC NVESD). The ROC-V training package utilizes measured visual and thermal imagery to train soldiers about the critical visual and thermal cues needed to accurately identify modern military vehicles and combatants. This paper presents an approach to augment the existing ROC-V imagery database with synthetically generated multi-spectral imagery that will allow NVESD to provide improved training imagery at significantly lower costs.

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Christopher M. May, Neil A. Pinto, and Jeffrey S. Sanders "Multi-spectral synthetic image generation for ground vehicle identification training", Proc. SPIE 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII, 98201A (3 May 2016); https://doi.org/10.1117/12.2228727

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