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

Autor:	Wardeh Al-Younis, Patrick Miller, David G. Voelz, Steven Sandoval
Rok vydání:	2021
Předmět:	Daytime Artificial neural network business.industry Machine learning computer.software_genre Refraction Term (time) Nonlinear system Atmospheric refraction Time-Lapse Imaging Artificial intelligence business computer Physics::Atmospheric and Oceanic Physics Geology Multipath propagation
Zdroj:	Laser Communication and Propagation through the Atmosphere and Oceans X.
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f2f891be656b99708d0223279668caf0 https://doi.org/10.1117/12.2594947 Zobrazit plný text záznamu