Articles | Volume 18, issue 9
https://doi.org/10.5194/amt-18-2083-2025
https://doi.org/10.5194/amt-18-2083-2025
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: 2,349 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,760 520 69 2,349 59 95
  • HTML: 1,760
  • PDF: 520
  • XML: 69
  • Total: 2,349
  • BibTeX: 59
  • EndNote: 95
Views and downloads (calculated since 22 Mar 2024)
Cumulative views and downloads (calculated since 22 Mar 2024)

Viewed (geographical distribution)

Total article views: 2,349 (including HTML, PDF, and XML) Thereof 2,313 with geography defined and 36 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Jan 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.
Share