Work

Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain

Year: 2025

Type: article

Abstract: 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.

Source: Sensors

DOI: 10.3390/s25051483

Authors: Damián Gulich, Myrian Tebaldi, Daniel Sierra-Sosa

Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de La Plata, Catholic University of America

Language: English

Topic: Adaptive optics and wavefront sensing

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Consejo Nacional de Investigaciones Científicas y Técnicas