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

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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.
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