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: 3,112 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,420 607 85 3,112 67 103
  • HTML: 2,420
  • PDF: 607
  • XML: 85
  • Total: 3,112
  • BibTeX: 67
  • EndNote: 103
Views and downloads (calculated since 22 Mar 2024)
Cumulative views and downloads (calculated since 22 Mar 2024)

Viewed (geographical distribution)

Total article views: 3,112 (including HTML, PDF, and XML) Thereof 3,112 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Mar 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