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Automatic Quantification of Atmospheric Turbulence Intensity in Space-Time Domain.

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Summary

This study introduces a deep learning approach to quantify atmospheric turbulence intensity using video analysis. The method effectively classifies turbulence levels by analyzing space-time features extracted from video slices.

Keywords:
atmospheric turbulencedeep learningspace-time analysisturbulence intensity quantificationvideo analysis

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Area of Science:

  • Atmospheric Science
  • Computer Science
  • Machine Learning

Background:

  • Quantifying atmospheric turbulence intensity is crucial but challenging, especially in real-world applications.
  • Existing methods may lack accuracy or practicality for diverse scenarios.

Purpose of the Study:

  • To develop a novel deep learning method for quantifying atmospheric turbulence intensity.
  • To leverage space-time domain analysis of video data for turbulence assessment.

Main Methods:

  • Captured videos of a static image under controlled turbulence intensities using an inexpensive camera.
  • Extracted spatio-temporal representations by slicing videos in the space-time domain.
  • Utilized a Convolutional Neural Network (CNN) for classifying turbulence regimes based on these representations.

Main Results:

  • The proposed deep learning model successfully discriminated between different atmospheric turbulence intensities.
  • Spatio-temporal features extracted from video slices proved effective for turbulence quantification.
  • The method demonstrated feasibility using real-world experimental data.

Conclusions:

  • Deep learning applied to space-time video analysis offers a promising approach for quantifying atmospheric turbulence.
  • This method provides a practical and potentially cost-effective solution for real-world turbulence assessment.