Automatic detection and identification of ground vehicles with YOLO-based deep learning using MuSES-generated OPIR synthetic imagery

Space-based sensor platforms, including both current and planned future satellites, are capable of surveilling Earth-based objects and scenes from high altitudes. Overhead persistent infrared (OPIR) is a growing surveillance technique where thermal-waveband infrared sensors are deployed on orbiting satellites to look down and image the Earth. Challenges include having sufficient image resolution to detect, differentiate and identify ground-based objects while monitoring through the atmosphere. Demonstrations have shown machine learning algorithms to be capable of processing imagebased scenes, detecting and recognizing targets amongst surrounding clutter. Performant algorithms must be robustly trained to successfully complete such a complex task, which typically requires a large set of training data on which statistical predictions can be based. Electro-optical infrared (EO/IR) remote sensing applications, including OPIR surveillance, necessitate a substantial image database with suitable variation for adept learning to occur. Diversity in background scenes, vehicle operational state, season, times of day and weather conditions can be included in training image sets to ensure sufficient algorithm input variety for OPIR applications. However, acquiring such a diverse overhead image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR) when adversarial vehicles are of interest. In this work, MuSES™ and CoTherm™ are used to generate synthetic OPIR imagery of several ground vehicles with a range of weather, times of day and background scenes. The performance of a YOLO (“you only look once”) deep learning algorithm is studied and reported, with a focus on how image resolution impacts algorithm detection/recognition performance. The image resolution of future space-based sensor platforms will surely increase, so this study seeks to understand the sensitivity of OPIR algorithm performance to overhead image resolution.