Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B
The monitoring of mesoscale convective systems (MCS) is typically based on satellite infrared data. Currently, there is limited research on the identification of MCS using true color composite cloud imagery. In this study, an MCS dataset was created based on the true color composite cloud imagery fr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/23/5572 |
| _version_ | 1827592071352942592 |
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| author | Ruxuanyi Xiang Tao Xie Shuying Bai Xuehong Zhang Jian Li Minghua Wang Chao Wang |
| author_facet | Ruxuanyi Xiang Tao Xie Shuying Bai Xuehong Zhang Jian Li Minghua Wang Chao Wang |
| author_sort | Ruxuanyi Xiang |
| collection | DOAJ |
| description | The monitoring of mesoscale convective systems (MCS) is typically based on satellite infrared data. Currently, there is limited research on the identification of MCS using true color composite cloud imagery. In this study, an MCS dataset was created based on the true color composite cloud imagery from the Fengyun-4B geostationary meteorological satellite. An MCS true color composite cloud imagery identification model was developed based on the Swin-Unet network. The MCS dataset was categorized into continental MCS and oceanic MCS, and the model’s performance in identifying these two different types of MCS was examined. Experimental results indicated that the model achieved a recall rate of 83.3% in identifying continental MCS and 86.1% in identifying oceanic MCS, with a better performance in monitoring oceanic MCS. These results suggest that using true color composite cloud imagery for MCS monitoring is feasible, and the Swin-Unet network outperforms traditional convolutional neural networks. Meanwhile, we find that the frequency and distribution range of oceanic MCS is larger than that of continental MCS, and the area is larger and some parts of it are stronger. This study provides a novel approach for satellite remote-sensing-based MCS monitoring. |
| first_indexed | 2024-03-09T01:43:32Z |
| format | Article |
| id | doaj.art-eb5c37183d524fb7b09233e6d6ad7b6e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T01:43:32Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-eb5c37183d524fb7b09233e6d6ad7b6e2023-12-08T15:25:05ZengMDPI AGRemote Sensing2072-42922023-11-011523557210.3390/rs15235572Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4BRuxuanyi Xiang0Tao Xie1Shuying Bai2Xuehong Zhang3Jian Li4Minghua Wang5Chao Wang6School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaTechnology Innovation Center for Integration Applications in Remote Sensing and Navigation, Ministry of Natural Resources, Nanjing 210044, ChinaThe monitoring of mesoscale convective systems (MCS) is typically based on satellite infrared data. Currently, there is limited research on the identification of MCS using true color composite cloud imagery. In this study, an MCS dataset was created based on the true color composite cloud imagery from the Fengyun-4B geostationary meteorological satellite. An MCS true color composite cloud imagery identification model was developed based on the Swin-Unet network. The MCS dataset was categorized into continental MCS and oceanic MCS, and the model’s performance in identifying these two different types of MCS was examined. Experimental results indicated that the model achieved a recall rate of 83.3% in identifying continental MCS and 86.1% in identifying oceanic MCS, with a better performance in monitoring oceanic MCS. These results suggest that using true color composite cloud imagery for MCS monitoring is feasible, and the Swin-Unet network outperforms traditional convolutional neural networks. Meanwhile, we find that the frequency and distribution range of oceanic MCS is larger than that of continental MCS, and the area is larger and some parts of it are stronger. This study provides a novel approach for satellite remote-sensing-based MCS monitoring.https://www.mdpi.com/2072-4292/15/23/5572satellite observationmesoscale convective systemSwin-Unettransformer |
| spellingShingle | Ruxuanyi Xiang Tao Xie Shuying Bai Xuehong Zhang Jian Li Minghua Wang Chao Wang Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B Remote Sensing satellite observation mesoscale convective system Swin-Unet transformer |
| title | Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B |
| title_full | Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B |
| title_fullStr | Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B |
| title_full_unstemmed | Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B |
| title_short | Monitoring Mesoscale Convective System Using Swin-Unet Network Based on Daytime True Color Composite Images of Fengyun-4B |
| title_sort | monitoring mesoscale convective system using swin unet network based on daytime true color composite images of fengyun 4b |
| topic | satellite observation mesoscale convective system Swin-Unet transformer |
| url | https://www.mdpi.com/2072-4292/15/23/5572 |
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