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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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 178129902 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Preliminary Data Processing of the MSU-GS/VE Device aboard the Arktika-M No. 1 Highly Elliptical Satellite Using Machine Learning Methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Asmus%2C+V%2E+V%2E%22">Asmus, V. V.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> asmus@planet.iitp.ru</i><br /><searchLink fieldCode="AR" term="%22Bloshchinskiy%2C+V%2E+D%2E%22">Bloshchinskiy, V. D.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kramareva%2C+L%2E+S%2E%22">Kramareva, L. S.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kuchma%2C+M%2E+O%2E%22">Kuchma, M. O.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Filei%2C+A%2E+A%2E%22">Filei, A. A.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Russian+Meteorology+%26+Hydrology%22">Russian Meteorology & Hydrology</searchLink>. Apr2024, Vol. 49 Issue 4, p299-303. 5p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Radiometers%22">Radiometers</searchLink><br />*<searchLink fieldCode="DE" term="%22Product+attributes%22">Product attributes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=178129902 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3103/S1068373924040022 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 299 Subjects: – SubjectFull: Image segmentation Type: general – SubjectFull: Radiometers Type: general – SubjectFull: Product attributes Type: general Titles: – TitleFull: Preliminary Data Processing of the MSU-GS/VE Device aboard the Arktika-M No. 1 Highly Elliptical Satellite Using Machine Learning Methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Asmus, V. V. – PersonEntity: Name: NameFull: Bloshchinskiy, V. D. – PersonEntity: Name: NameFull: Kramareva, L. S. – PersonEntity: Name: NameFull: Kuchma, M. O. – PersonEntity: Name: NameFull: Filei, A. A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10683739 Numbering: – Type: volume Value: 49 – Type: issue Value: 4 Titles: – TitleFull: Russian Meteorology & Hydrology Type: main |
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