| Registro completo |
| Provedor de dados: |
ArchiMer
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| País: |
France
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| Título: |
Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method
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| Autores: |
Minghelli, Audrey
Spagnoli, Jérôme
Lei, Manchun
Chami, Malik
Charmasson, Sabine
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| Data: |
2020-08
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| Ano: |
2020
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| Palavras-chave: |
Shoreline
Foam
Classification
WorldView-2
Multispectral
High resolution satellite images
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| Resumo: |
Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images (<20 m). This study focuses on shoreline extraction and shoreline evolution using high spatial resolution satellite images in the presence of foam. A multispectral supervised classification technique is selected, namely the Support Vector Machine (SVM) and applied with three classes which are land, foam and water. The merging of water and foam classes followed by a segmentation procedure enables the separation of land and ocean pixels. The performance of the method is evaluated using a validation dataset acquired on two study areas (south and north of the bay of Sendaï—Japan). On each site, WorldView-2 multispectral images (eight bands, 2 m resolution) were acquired before and after the Fukushima tsunami generated by the Tohoku earthquake in 2011. The consideration of the foam class enables the false negative error to be reduced by a factor of three. The SVM method is also compared with four other classification methods, namely Euclidian Distance, Spectral Angle Mapper, Maximum Likelihood, and Neuronal Network. The SVM method appears to be the most efficient to determine the erosion and the accretion resulting from the tsunami, which are societal issues for littoral management purposes
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| Tipo: |
Text
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| Idioma: |
Inglês
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| Identificador: |
https://archimer.ifremer.fr/doc/00649/76116/77079.pdf
DOI:10.3390/rs12162664
https://archimer.ifremer.fr/doc/00649/76116/
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| Editor: |
MDPI AG
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| Formato: |
application/pdf
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| Fonte: |
Remote Sensing (2072-4292) (MDPI AG), 2020-08 , Vol. 12 , N. 16 , P. 2664 (17p.)
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| Direitos: |
info:eu-repo/semantics/openAccess
restricted use
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