Open AccessCloud Detection for Satellite Imagery Using Deep Learning
Author(s) -
Yanan Guo,
Cao Xiaoqun,
Bainian Liu,
Kecheng Peng
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1617/1/012089
Subject(s) - cloud computing , remote sensing , computer science , deep learning , change detection , snow , cloud cover , satellite , climate change , image processing , task (project management) , artificial intelligence , meteorology , environmental science , image (mathematics) , geography , systems engineering , geology , engineering , oceanography , aerospace engineering , operating system
Cloud is the most uncertain factor in the climate system and has a huge impact on climate change. Therefore, the study of changes in cloudiness is of great importance for understanding climate and climate change. Cloud detection is also an important research area in satellite remote sensing image pre-processing. But cloud detection is a difficult task due to various noise disturbances in the remote sensing data itself, as well as factors such as ice and snow on the ground. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in methods such as image processing and classification. In this study, we use the modified U-Net architecture that introduces the attention mechanism for cloud detection. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method, especially in snowy areas and other areas covered by bright non-cloud objects. The effectiveness of this method makes it a great potential for other optical image processing as well.