Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 9
Articles | Volume 18, issue 9
https://doi.org/10.5194/amt-18-2083-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2083-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 9
Research article
 | 
12 May 2025
Research article |  | 12 May 2025

Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations

Kélian Sommer, Wassim Kabalan, and Romain Brunet

Viewed

Total article views: 6,035 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,971 1,876 188 6,035 146 225
  • HTML: 3,971
  • PDF: 1,876
  • XML: 188
  • Total: 6,035
  • BibTeX: 146
  • EndNote: 225
Views and downloads (calculated since 22 Mar 2024)
Cumulative views and downloads (calculated since 22 Mar 2024)

Viewed (geographical distribution)

Total article views: 6,035 (including HTML, PDF, and XML) Thereof 6,035 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 20 May 2026
Download
Short summary
Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests based on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observation experiments.
Read more
Share