A Cloud Motion Estimation Method Based on Cloud Image Depth Feature Matching

The movement of clouds directly influences fluctuations in solar radiation. Therefore, cloud motion vector (CMV) estimation techniques are widely applied in sequential cloud images to predict solar radiation and study other meteorologically related fields. However, traditional block matching, optica...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2025, Vol.22, p.1-5
Main Authors: Zou, Lianglin, Tang, Ping, Niu, Yisen, Yan, Zixuan, Lin, Xilong, Song, Jifeng, Wang, Qian
Format: Article
Language:English
Subjects:
Attention
Brightness
cloud motion vector (CMV)
Cloud types
Clouds
Deep learning
Deformation
Detectors
Estimation
Feature extraction
feature point
Filtering
Image enhancement
Machine learning
Matching
Motion simulation
Movement
Optical flow
Optical flow (image analysis)
Radiation-cloud interactions
self-supervised learning
Solar radiation
Training
Vectors
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Description
Summary:The movement of clouds directly influences fluctuations in solar radiation. Therefore, cloud motion vector (CMV) estimation techniques are widely applied in sequential cloud images to predict solar radiation and study other meteorologically related fields. However, traditional block matching, optical flow, and feature point methods struggle to accurately capture the deformation, multilayered, and mixed cloud types' motion due to the lack of deep semantic understanding of cloud images. Additionally, without cloud-motion-labeled, deep learning tools such as CNNs are limited in their utility for motion assessment. Therefore, this letter proposes a method of cloud image depth feature matching to assess the CMV in time series, including image enhancement, self-supervised feature extraction, feature matching, feature fusion, and spatiotemporal filtering. Experimental results demonstrate a significant improvement in accuracy compared to traditional CMV estimation techniques, with higher robustness observed across various complex cloud scenarios.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3491094