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To carry out the technical expertise of the IR defense systems that equip the French Armed Forces, DGA Information Superiority relies on simulation and uses the SE-Workbench-EO software to model the operational battlefield, as viewed by an optronic system, and generate synthetic images of military targets in their environment in animated scenarios. The various simulated functions comprise mainly, on sensor side, intelligence, detection/observation, homing and image processing, then, on target side, low detectability/stealth and self-protection. In recent years, DGA IS has experimented with SE-Workbench-EO both the computation of image data sets to train an automatic object acquisition capability by IR imaging using machine learning and the exploitation of the software in the visible color domain. In both cases, the software should meet high requirements regarding physical realism, image quality for human eyes and algorithmic perception, domain coverage and variability, calculation time, ergonomics and implementation efficiency. These experiences have brought many insights but the requirements are higher than ever and it is now necessary to undertake a major evolution of the current software to cover the needs of the years to come, including machine learning and particularly automatic detection and recognition of targets. In addition, the main stakes are the increasing of the entropy to approach that of the real images, the constitution of large volume of data in acceptable times or the complementarity and the right balance with the real images. This article presents the recent experiments, the current state of the software and needs not yet covered, and then the major evolution in preparation. It aims to demonstrate the expected contribution of EO/IR image synthesis to specify, design, evaluate and qualify an imaging system using machine learning.
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