Predicting atmospheric refraction with weather modeling and machine learning

Conference paper (2019)

Authors

Wardeh Al-Younis New Mexico State Univ.
Christina Nevarez New Mexico State Univ.
David Voelz New Mexico State Univ.
Steven Sandoval New Mexico State Univ.
S. Basu Atmospheric Remote Sensing - CEG
Research Group
Atmospheric Remote Sensing (CEG) (TU Delft)
Copyright: © 2019 Wardeh Al-Younis, Christina Nevarez, David Voelz, Steven Sandoval, S. Basu
DOI: https://doi.org/10.1117/12.2529533
Machine learning algorithms Atmospheric refraction Remote mobile station Time-lapse imaging Weather modeling
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Published Date

2019

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Atmospheric Remote Sensing

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.

Files

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