Proceedings Article | 23 April 2020
KEYWORDS: Sensors, Image processing, Detection and tracking algorithms, Evolutionary algorithms, 3D modeling, Image sensors, Thermography, Data modeling, Artificial intelligence, Infrared radiation, Computer simulations, Thermal analysis, Scene simulation, Automatic target recognition, Object recognition
The emergence of machine learning into scientific fields has created opportunities for novel and powerful image processing techniques. Algorithms that can perform complex tasks without a “man-in-the-loop” or explicit instructions are invaluable artificial intelligence tools. These algorithms typically require a large set of training data on which to base statistical predictions. In the case of electro-optical infrared (EO/IR) remote sensing, algorithm designers often seek a substantial library of images comprising many weather conditions, times of day, sensor resolutions, etc. These images may be synthetic (predicted) or measured, but should encompass a large variety of targets imaged from a variety of vantage points against numerous backgrounds. Acquiring such a large set of measured imagery with sufficient variation can be difficult, requiring numerous field campaigns. Alternatively, accurate prediction of target signatures in cluttered outdoor scenes may be a viable option. In this work, sensor imagery is generated using CoTherm, a co-simulation tool which operates MuSES (an EO/IR simulation code) in an automated fashion to create a large library of synthetic images. The relevant MuSES inputs – which might include environmental factors, global location, date and time, vehicle engine state, human clothing and activity level, or sensor waveband – can be manipulated by a CoTherm workflow process. The output of this process is a large library of MuSES-generated EO/IR sensor radiance images suitable for algorithm development. If desired, synthetic target pixels can be inserted into measured background images for added realism.
