Anisotropic turbulence identification from classifying extreme morphological changes in targets with deep-learning

Darío G. Pérez, Marco Sepúlveda, Leandro Nuñez, Alina Madrid, Hishan Farfán, Bastían Romero

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the last decade, a nascent trend of characterizing turbulence from observing features of distant targets through ground-layer turbulence have been relentless growing. Either from observing regular geometrical features of buildings or arrays of LEDs, it is possible to retrieve the structure constant of the refractive index fluctuations. On the other hand, because of the lack of a definitive theoretical model describing anisotropic or inhomogeneous turbulence, most experimental observations have been reduced to mere descriptions in the event of deviations from expected Obukhov-Kolmogorov predictions. Our group has been able to retrieve power-spectrum exponents, without a prior knowledge of a subjacent model, and henceforth determine anisotropic behavior in controlled optical turbulence; furthermore, under convective turbulence, an exponent can be obtained from time series of the occurrence of power drops in optical communication links: extreme events. In this manuscript, we present a technique identifying as extreme events suden changes in morphological characteristics of an array of point sources observed through real controlled anisotropic turbulence assisted by a deep-learnig ad-hoc. This approach provides an effective approach to reduce high-volume data from imaging targets into a real-time stream of parameters to fully characterize optical turbulence.

Original languageEnglish
Title of host publicationEnvironmental Effects on Light Propagation and Adaptive Systems V
EditorsKarin Stein, Szymon Gladysz
PublisherSPIE
ISBN (Electronic)9781510655355
DOIs
StatePublished - 2022
Externally publishedYes
EventEnvironmental Effects on Light Propagation and Adaptive Systems V 2022 - Berlin, Germany
Duration: 6 Sep 2022 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12266
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceEnvironmental Effects on Light Propagation and Adaptive Systems V 2022
Country/TerritoryGermany
CityBerlin
Period6/09/22 → …

Keywords

  • deep-learning
  • extreme events
  • ground-layer turbulence

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