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Proceedings Paper

Multi-spectral synthetic image generation for ground vehicle identification training
Author(s): Christopher M. May; Neil A. Pinto; Jeffrey S. Sanders
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Paper 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.

Paper Details

Date Published: 3 May 2016
PDF: 11 pages
Proc. SPIE 9820, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII, 98201A (3 May 2016); doi: 10.1117/12.2228727
Show Author Affiliations
Christopher M. May, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Neil A. Pinto, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Jeffrey S. Sanders, Trideum Corp. (United States)


Published in SPIE Proceedings Vol. 9820:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVII
Gerald C. Holst; Keith A. Krapels, Editor(s)

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