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Achieving downscaling of Meteosat thermal infrared imagery using artificial neural networks
Knowledge and Intelligent Computing Laboratory, Informatics and Telecommunication Technology, TEI of Epirus.
Knowledge and Intelligent Computing Laboratory, Informatics and Telecommunication Technology, TEI of Epirus.ORCID iD: 0000-0001-9701-4203
Knowledge and Intelligent Computing Laboratory, Informatics and Telecommunication Technology, TEI of Epirus.
2013 (English)In: International Journal of Remote Sensing, ISSN 0143-1161, E-ISSN 1366-5901, Vol. 34, no 21, p. 7706-7722Article in journal (Refereed) Published
Abstract [en]

This study presents the successful application of artificial neural networks (ANNs) for downscaling Meteosat Second Generation thermal infrared satellite imagery. The scope is to examine, propose, and develop an integrated methodology to improve the spatial resolution of Meteosat satellite images. The proposed approach may contribute to the development of a general methodology for monitoring and downscaling Earth's surface characteristics and cloud systems, where there is a clear need for contiguous, accurate, and high-spatial resolution data sets (e.g. improvement of climate model input data sets, early warning systems about extreme weather phenomena, monitoring of parameters such as solar radiation fluxes, land-surface temperature, etc.). Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to validate the downscaled Meteosat images. In terms of the ANNs, a multilayer perceptron (MLP) is used and the results are shown to compare favourably against a linear regression approach.

Place, publisher, year, edition, pages
Taylor & Francis, 2013. Vol. 34, no 21, p. 7706-7722
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-69829DOI: 10.1080/01431161.2013.825384Scopus ID: 2-s2.0-84884490983OAI: oai:DiVA.org:ltu-69829DiVA, id: diva2:1223093
Available from: 2018-06-25 Created: 2018-06-25 Last updated: 2018-06-25Bibliographically approved

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Georgoulas, George G

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  • de-DE
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  • nn-NB
  • sv-SE
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Output format
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  • asciidoc
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