Extended analysis of atmospheric refraction effects captured by time-lapse imaging: long-term trends and machine learning image shift prediction

Wardeh Al-Younis; Steven Sandoval; David Voelz; Patrick Miller

doi:10.1117/12.2594947

1 August 2021 Extended analysis of atmospheric refraction effects captured by time-lapse imaging: long-term trends and machine learning image shift prediction

Wardeh Al-Younis, Steven Sandoval, David Voelz, Patrick Miller

Author Affiliations +

Proceedings Volume 11834, Laser Communication and Propagation through the Atmosphere and Oceans X; 1183404 (2021) https://doi.org/10.1117/12.2594947
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

This work presents an extended analysis of atmospheric refraction effects captured by time-lapse imagery for near-ground and near-horizontal paths. Monthly trends and multipath analysis of image shift caused by refraction during daytime are studied. Nighttime shift measurements during moonlit nights are also presented. Advanced nonlinear machine learning approaches for image shift prediction are implemented and the performance of the models is evaluated.

Conference Presentation

Citation Download Citation

Wardeh Al-Younis, Steven Sandoval, David Voelz, and Patrick Miller "Extended analysis of atmospheric refraction effects captured by time-lapse imaging: long-term trends and machine learning image shift prediction", Proc. SPIE 11834, Laser Communication and Propagation through the Atmosphere and Oceans X, 1183404 (1 August 2021); https://doi.org/10.1117/12.2594947

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available