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Predicting atmospheric refraction with weather modeling and machine learning
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Paper Abstract

This work details the analysis of time-lapse images with a point-tracking image processing approach along with the use of an extensive numerical weather model to investigate image displacement due to refraction. The model is applied to create refractive profile estimates along the optical path for the days of interest. Ray trace analysis through the model profiles is performed and comparisons are made with the measured displacement results. Additionally, a supervised machine learning algorithm is used to build a predictive model to estimate the apparent displacement of an object, based on a set of measured metrological values taken in the vicinity of the camera. The predicted results again are compared with the field-imagery ones.

Paper Details

Date Published: 6 September 2019
PDF: 8 pages
Proc. SPIE 11133, Laser Communication and Propagation through the Atmosphere and Oceans VIII, 111330E (6 September 2019); doi: 10.1117/12.2529533
Show Author Affiliations
Wardeh Al-Younis, New Mexico State Univ. (United States)
Christina Nevarez, New Mexico State Univ. (United States)
David Voelz, New Mexico State Univ. (United States)
Steven Sandoval, New Mexico State Univ. (United States)
Sukanta Basu, Technische Univ. Delft (Netherlands)


Published in SPIE Proceedings Vol. 11133:
Laser Communication and Propagation through the Atmosphere and Oceans VIII
Jeremy P. Bos; Alexander M. J. van Eijk; Stephen Hammel, Editor(s)

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