Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain

Autores
Gulich, Damián; Tebaldi, Myrian Cristina; Sierra Sosa, Daniel Esteban
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Quantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.
Centro de Investigaciones Ópticas
Materia
Física
atmospheric turbulence
deep learning
space-time analysis
video analysis
turbulence intensity quantification
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/181758
Acceder
id SEDICI_6449df37649acca50d3e786e7bbaa58c
oai_identifier_str oai:sedici.unlp.edu.ar:10915/181758
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time DomainGulich, DamiánTebaldi, Myrian CristinaSierra Sosa, Daniel EstebanFísicaatmospheric turbulencedeep learningspace-time analysisvideo analysisturbulence intensity quantificationQuantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.Centro de Investigaciones Ópticas2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/181758enginfo:eu-repo/semantics/altIdentifier/issn/1424-8220info:eu-repo/semantics/altIdentifier/doi/10.3390/s25051483info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:21:24Zoai:sedici.unlp.edu.ar:10915/181758Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:21:25.391SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
title Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
spellingShingle Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
Gulich, Damián
Física
atmospheric turbulence
deep learning
space-time analysis
video analysis
turbulence intensity quantification
title_short Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
title_full Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
title_fullStr Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
title_full_unstemmed Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
title_sort Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
dc.creator.none.fl_str_mv Gulich, Damián
Tebaldi, Myrian Cristina
Sierra Sosa, Daniel Esteban
author Gulich, Damián
author_facet Gulich, Damián
Tebaldi, Myrian Cristina
Sierra Sosa, Daniel Esteban
author_role author
author2 Tebaldi, Myrian Cristina
Sierra Sosa, Daniel Esteban
author2_role author
author
dc.subject.none.fl_str_mv Física
atmospheric turbulence
deep learning
space-time analysis
video analysis
turbulence intensity quantification
topic Física
atmospheric turbulence
deep learning
space-time analysis
video analysis
turbulence intensity quantification
dc.description.none.fl_txt_mv Quantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.
Centro de Investigaciones Ópticas
description Quantifying atmospheric turbulence intensity is a challenging task, particularly when assessing real-world scenarios. In this paper, we propose a deep learning method for quantifying atmospheric turbulence intensity based on the space-time domain analysis from videos depicting different turbulence levels. We capture videos of a static image under controlled air turbulence intensities using an inexpensive camera, and then, by slicing these videos in the space-time domain, we extract spatio-temporal representations of the turbulence dynamics. These representations are then fed into a Convolutional Neural Network for classification. This network effectively learns to discriminate between different turbulence regimes based on the spatio-temporal features extracted from a real-world experiment captured in video slices.
publishDate 2025
dc.date.none.fl_str_mv 2025-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/181758
url http://sedici.unlp.edu.ar/handle/10915/181758
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1424-8220
info:eu-repo/semantics/altIdentifier/doi/10.3390/s25051483
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1842260719118057472
score 13.13397

Publicaciones similares