An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks
The paper explores a method for calculating a cloud mask based on the use of several convolutional neural network classifiers trained for various observation conditions using a bootstrapping method. An algorithm developed on the basis of this method makes it possible to detect clouds in images obtai...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2025-06-01
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| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | https://computeroptics.ru/eng/KO/Annot/KO49-3/490311e.html |
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| author | A.I. Andreev S.I. Malkovsky M.O. Kuchma Y.A. Shamilova |
| author_facet | A.I. Andreev S.I. Malkovsky M.O. Kuchma Y.A. Shamilova |
| author_sort | A.I. Andreev |
| collection | DOAJ |
| description | The paper explores a method for calculating a cloud mask based on the use of several convolutional neural network classifiers trained for various observation conditions using a bootstrapping method. An algorithm developed on the basis of this method makes it possible to detect clouds in images obtained from the Advanced Himawari Imager (AHI) instrument onboard the Himawari-8/9 satellite, regardless of the Sun illumination conditions of the territory of the Asia-Pacific region under surveillance in the warm and cold seasons. The accuracy of the obtained results is assessed using a cloud mask provided by the US National Oceanic and Atmospheric Administration, NOAA. Numerical assessment and visual analysis show high accuracy of the developed algorithm, which is higher than the earlier classifier version offered by the present authors. When compared with the NOAA masks, the average F1-measure ranges from 75% at twilight to 85% during the daytime. |
| format | Article |
| id | doaj-art-22443c5ca01e41dcaac54a58739de6f4 |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-22443c5ca01e41dcaac54a58739de6f42025-08-21T06:53:45ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792025-06-0149345146010.18287/2412-6179-CO-1525An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networksA.I. Andreev0S.I. Malkovsky1M.O. Kuchma2Y.A. Shamilova3Computing Center of the Far Eastern Branch of the Russian Academy of Sciences; Far-Eastern Center of the Federal State Budgetary Institution "State Research Center of Space Hydrometeorology 'Planeta'Computing Center of the Far Eastern Branch of the Russian Academy of SciencesComputing Center of the Far Eastern Branch of the Russian Academy of Sciences; Far-Eastern Center of the Federal State Budgetary Institution "State Research Center of Space Hydrometeorology 'Planeta'Far-Eastern Center of the Federal State Budgetary Institution "State Research Center of Space Hydrometeorology 'Planeta'The paper explores a method for calculating a cloud mask based on the use of several convolutional neural network classifiers trained for various observation conditions using a bootstrapping method. An algorithm developed on the basis of this method makes it possible to detect clouds in images obtained from the Advanced Himawari Imager (AHI) instrument onboard the Himawari-8/9 satellite, regardless of the Sun illumination conditions of the territory of the Asia-Pacific region under surveillance in the warm and cold seasons. The accuracy of the obtained results is assessed using a cloud mask provided by the US National Oceanic and Atmospheric Administration, NOAA. Numerical assessment and visual analysis show high accuracy of the developed algorithm, which is higher than the earlier classifier version offered by the present authors. When compared with the NOAA masks, the average F1-measure ranges from 75% at twilight to 85% during the daytime.https://computeroptics.ru/eng/KO/Annot/KO49-3/490311e.htmlahihimawaricloudsmaskneural networkbootstrapping. ensemble of classifiers |
| spellingShingle | A.I. Andreev S.I. Malkovsky M.O. Kuchma Y.A. Shamilova An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks Компьютерная оптика ahi himawari clouds mask neural network bootstrapping. ensemble of classifiers |
| title | An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks |
| title_full | An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks |
| title_fullStr | An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks |
| title_full_unstemmed | An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks |
| title_short | An ensemble method for cloud mask calculation based on data from the AHI instrument onboard the Himawari-8/9 satellite using convolutional neural networks |
| title_sort | ensemble method for cloud mask calculation based on data from the ahi instrument onboard the himawari 8 9 satellite using convolutional neural networks |
| topic | ahi himawari clouds mask neural network bootstrapping. ensemble of classifiers |
| url | https://computeroptics.ru/eng/KO/Annot/KO49-3/490311e.html |
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