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dc.contributor.author
Kiani Shahvandi, Mostafa
dc.date.accessioned
2021-03-05T14:28:04Z
dc.date.available
2021-01-05T11:01:20Z
dc.date.available
2021-01-25T12:33:14Z
dc.date.available
2021-03-05T14:28:04Z
dc.date.issued
2021-03
dc.identifier.other
10.1007/s12145-020-00553-7
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/459441
dc.description.abstract
In this paper a new method of image smoothing and its applications in the field of remote sensing are presented. This method is based on the minimization of the iterated Laplace operator of an arbitrary degree in the Cartesian coordinate system. Using the method of finite differences, a linear combination is derived, which represents the solution of the minimization problem. For the special case of the ordinary Laplace operator, the solution is explicitly represented in a 9 × 9 template. To show the potential applications in the field of remote sensing, a study is presented for Iran. In this study, Sentinel-2 satellite imagery is used in 13 bands, with different geometric resolutions. Using the derived template, a comprehensive analysis is presented for each band. It is shown that various phenomena can be detected in the image, including location of different soil types. Comparison of the independent methods of Laplace template, L0 gradient smoothing, local Laplacian smoothing, and tree filtering, with the newly proposed method shows that the new method is more efficient in determining the various phenomena that are present in the area of interest in the satellite imagery. © 2020 Springer-Verlag GmbH Germany, part of Springer Nature.
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.subject
Iterated Laplacian
en_US
dc.subject
Norm minimization
en_US
dc.subject
Linear combination of discrete smoothing function
en_US
dc.subject
Geological remote sensing
en_US
dc.subject
Sentinel-2 satellite imagery
en_US
dc.subject
Deep convolutional neural networks
en_US
dc.title
A new optimal image smoothing method based on generalized discrete iterated Laplacian minimization and its application in the analysis of earth’s surface using satellite remote sensing imagery
en_US
dc.type
Journal Article
dc.date.published
2020-11-13
ethz.journal.title
Earth Science Informatics
ethz.journal.volume
14
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
81
en_US
ethz.pages.end
97
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Heidelberg
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::09707 - Soja, Benedikt / Soja, Benedikt
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02647 - Inst. f. Geodäsie und Photogrammetrie / Institute of Geodesy and Photogrammetry::09707 - Soja, Benedikt / Soja, Benedikt
en_US
ethz.date.deposited
2021-01-05T11:01:28Z
ethz.source
FORM
ethz.eth
no
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-03-05T14:28:15Z
ethz.rosetta.lastUpdated
2022-03-29T05:38:55Z
ethz.rosetta.versionExported
true
ethz.COinS
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