Deep-learning-based cloud segmentation and classification for weather monitoring

Cameron Martus; Brian Johnson

doi:10.1117/12.3031208

20 November 2024 Deep-learning-based cloud segmentation and classification for weather monitoring

Cameron Martus, Brian Johnson

Author Affiliations +

Proceedings Volume 13193, Remote Sensing of Clouds and the Atmosphere XXIX; 131930F (2024) https://doi.org/10.1117/12.3031208
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

Traditional cloud detection algorithms for weather monitoring require radiometrically calibrated multispectral visible and infrared (IR) sensors such as those on dedicated weather satellites. In contrast, deep learning methods facilitate the use of proliferated earth observing constellations with uncalibrated sensors and fewer spectral bands for weather applications. This capability detects clouds and classifies types by recognizing spatial and spectral features using a deep neural network. In this study, we leverage existing U-Net architectures and train on several different satellite datasets. Following model development, we compare several ways to segment clouds in remote sensing images, including the number of spectral bands and separation of thin and thick clouds. The separation of thin and thick clouds is a first step in segmenting clouds by type. The capability developed in this work will facilitate the exploitation of rapidly growing data sources from the expanding market of proliferated commercial remote sensing systems.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Cameron Martus and Brian Johnson "Deep-learning-based cloud segmentation and classification for weather monitoring", Proc. SPIE 13193, Remote Sensing of Clouds and the Atmosphere XXIX, 131930F (20 November 2024); https://doi.org/10.1117/12.3031208

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