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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/5/1483 |
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| Summary: | 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. |
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| ISSN: | 1424-8220 |