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14 May 2019 Stellar background rendering for space situational awareness algorithm development
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A key component of a night scene background on a clear moonless night is the stellar background. Celestial objects affected by atmospheric distortions and optical system noise become the primary contribution of clutter for detection and tracking algorithms while at the same time providing a solid geolocation or time reference due to their highly predictable motion. Any detection algorithm that needs to operate on a clear night must take into account the stellar background and remove it via background subtraction methods. As with any scenario, the ability to develop detection algorithms depends on the availability of representative data to evaluate the difficulty of the task. Further, the acquisition of measured field data under arbitrary atmospheric conditions is difficult if not impossible. For this reason, a radiometrically accurate simulation of the stellar background is a boon to algorithm developers. To aid in simulating the night sky, we have incorporated a star-field rendering model into the Georgia Tech Simulations Integrated Modeling System (GTSIMS). Rendering a radiometrically accurate star-field requires three major components: positioning the stars as a function of time and observer location, determining the in-band radiance of each star, and simulating the apparent size of each star. We present the models we have incorporated into GTSIMS and provide a representative sample of the images generated with the new model. We then demonstrate how the clutter in the neighborhood of a pixels change by including a radiometrically accurate rendering of a star-field.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keith F. Prussing, Christopher R. Valenta, Christopher E. Cordell, Anissa Zacharias, and Layne R. Churchill "Stellar background rendering for space situational awareness algorithm development", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109860Z (14 May 2019);

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