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A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites
| Content Provider | MDPI |
|---|---|
| Author | Jiang, Yuhang Cheng, Wei Gao, Feng Zhang, Shaoqing Wang, Shudong Liu, Chang Liu, Juanjuan |
| Copyright Year | 2022 |
| Description | The study of cloud types is critical for understanding atmospheric motions and climate predictions; for example, accurately classified cloud products help improve meteorological predicting accuracies. However, the current satellite cloud classification methods generally analyze the threshold change in a single pixel and do not consider the relationship between the surrounding pixels. The classification development relies heavily on human recourses and does not fully utilize the data-driven advantages of computer models. Here, a new intelligent cloud classification method based on the U-Net network (CLP-CNN) is developed to obtain more accurate, higher frequency, and larger coverage cloud classification products. The experimental results show that the CLP-CNN network can complete a cloud classification task of 800 × 800 pixels in 0.9 s. The classification area covers most of China, and the classification task only needs to use the original L1-level data, which can meet the requirements of a real-time operation. With the Himawari-8 CLTYPE product and the CloudSat 2B-CLDCLASS product as the test comparison target, the CLP-CNN network results match the Himawari-8 product highly, by 84.4%. The probability of detection (POD) is greater than 0.83 for clear skies, deep-convection, and Cirrus–Stratus type clouds. The probability of detection (POD) and accuracy are improved compared with other deep learning methods. |
| Starting Page | 2314 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14102314 |
| Journal | Remote Sensing |
| Issue Number | 10 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-11 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Marine Engineering Cloud Classification Fy-4a Agri Deep Learning Geosynchronous Satellites |
| Content Type | Text |
| Resource Type | Article |
