Preliminary Data Processing of the MSU-GS/VE Device aboard the Arktika-M No. 1 Highly Elliptical Satellite Using Machine Learning Methods.
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| Title: | Preliminary Data Processing of the MSU-GS/VE Device aboard the Arktika-M No. 1 Highly Elliptical Satellite Using Machine Learning Methods. |
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| Authors: | Asmus, V. V.1 (AUTHOR) asmus@planet.iitp.ru, Bloshchinskiy, V. D.2 (AUTHOR), Kramareva, L. S.2 (AUTHOR), Kuchma, M. O.2 (AUTHOR), Filei, A. A.2 (AUTHOR) |
| Source: | Russian Meteorology & Hydrology. Apr2024, Vol. 49 Issue 4, p299-303. 5p. |
| Subject Terms: | *Image segmentation, *Radiometers, *Product attributes |
| Abstract: | The paper presents the research work aimed at improving the quality characteristics of information products based on the MSU-GS/VE radiometer aboard the Arktika-M No. 1 satellite, as well as at obtaining data preprocessing products. All described methods are based on using machine learning algorithms, namely, neural networks of various architectures. The results of developing a technology for minimizing the interference that occurs in the channels of the satellite device are provided. The work on detecting cloud formations based on processing the channel data in the visible and infrared ranges is presented. It is shown that the use of neural networks makes it possible to implement automatic algorithms for obtaining thematic products that take into account various factors and have an accuracy that is commensurate with statistical and physical approaches and reduces the time of satellite data processing. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| Abstract: | The paper presents the research work aimed at improving the quality characteristics of information products based on the MSU-GS/VE radiometer aboard the Arktika-M No. 1 satellite, as well as at obtaining data preprocessing products. All described methods are based on using machine learning algorithms, namely, neural networks of various architectures. The results of developing a technology for minimizing the interference that occurs in the channels of the satellite device are provided. The work on detecting cloud formations based on processing the channel data in the visible and infrared ranges is presented. It is shown that the use of neural networks makes it possible to implement automatic algorithms for obtaining thematic products that take into account various factors and have an accuracy that is commensurate with statistical and physical approaches and reduces the time of satellite data processing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10683739 |
| DOI: | 10.3103/S1068373924040022 |
