Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain
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 l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1483 |
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| author | Damián Gulich Myrian Tebaldi Daniel Sierra-Sosa |
| author_facet | Damián Gulich Myrian Tebaldi Daniel Sierra-Sosa |
| author_sort | Damián Gulich |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-ee8073c5ce54432baf722b9ab6b4d4f0 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ee8073c5ce54432baf722b9ab6b4d4f02025-08-20T02:52:38ZengMDPI AGSensors1424-82202025-02-01255148310.3390/s25051483Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time DomainDamián Gulich0Myrian Tebaldi1Daniel Sierra-Sosa2Centro de Investigaciones Ópticas (CONICET La Plata-CIC-UNLP), La Plata 1897, ArgentinaCentro de Investigaciones Ópticas (CONICET La Plata-CIC-UNLP), La Plata 1897, ArgentinaElectrical Engineering and Computer Science, The Catholic University of America, Washington, DC 20064, USAQuantifying 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.https://www.mdpi.com/1424-8220/25/5/1483atmospheric turbulencedeep learningspace-time analysisvideo analysisturbulence intensity quantification |
| spellingShingle | Damián Gulich Myrian Tebaldi Daniel Sierra-Sosa Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain Sensors atmospheric turbulence deep learning space-time analysis video analysis turbulence intensity quantification |
| title | 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_short | Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain |
| title_sort | automatic quantification of atmospheric turbulence intensity in space time domain |
| topic | atmospheric turbulence deep learning space-time analysis video analysis turbulence intensity quantification |
| url | https://www.mdpi.com/1424-8220/25/5/1483 |
| work_keys_str_mv | AT damiangulich automaticquantificationofatmosphericturbulenceintensityinspacetimedomain AT myriantebaldi automaticquantificationofatmosphericturbulenceintensityinspacetimedomain AT danielsierrasosa automaticquantificationofatmosphericturbulenceintensityinspacetimedomain |