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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