A review and test of shoreline extraction techniques

Shoreline represents the boundary between land and sea, and its accurate extraction is of utmost importance because of the economic and ecological value of coastal areas. Nowadays, satellite remote sensing is widely used for monitoring the natural environment. Indeed, satellite remote sensing data a...

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Detalles Bibliográficos
Autores: Angelini, Riccardo, Angelats Company, Eduard, Luzi, Guido, Ribas Prats, Francesca|||0000-0003-4701-5982, Masiero, Andrea, Mugnai, Francesco
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/405648
Acceso en línea:https://hdl.handle.net/2117/405648
https://dx.doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-17-2023
Access Level:acceso abierto
Palabra clave:Coasts--Remote sensing
Coastal zone management
Climatic changes
Radar
Shoreline extraction
Classifier
Machine learning: Multispectral
Zones costaneres--Ordenació
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria civil::Geologia
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spelling A review and test of shoreline extraction techniquesAngelini, RiccardoAngelats Company, EduardLuzi, GuidoRibas Prats, Francesca|||0000-0003-4701-5982Masiero, AndreaMugnai, FrancescoCoasts--Remote sensingCoastal zone managementClimatic changesRadarShoreline extractionClassifierMachine learning: MultispectralRadarRadarZones costaneres--OrdenacióCanvis climàticsÀrees temàtiques de la UPC::Enginyeria civil::GeologiaShoreline represents the boundary between land and sea, and its accurate extraction is of utmost importance because of the economic and ecological value of coastal areas. Nowadays, satellite remote sensing is widely used for monitoring the natural environment. Indeed, satellite remote sensing data are cost-effective and periodically available over large areas at a relatively high spatial resolution. Hence, the automatic shoreline extraction from satellite images is a fundamental task for coastal monitoring and management. Shoreline extraction methods are usually applied to satellite remote sensing data. The goal of this study is to compare the performance of different shoreline extraction methods, such as thresholding and more complex classification approaches, such as Random Forest (RF), Minimum Distance (MD), Maximum Likelihood (ML) and K-means, using both optical and radar images. The considered case study area is the shallow basin of the Orbetello Lagoon and one of its ayre called Feniglia. The data supplier is the Copernicus program, which, through the Sentinel-1 and Sentinel-2 missions, provides medium-resolution, open-access products. The accuracy of the obtained results from both methodologies is checked by validating the extracted shoreline using an aerial orthomosaic and, subsequently, a manually extracted shoreline. A preliminary accuracy assessment was performed for image classification, focusing on extracting four classes: water, soil, urban, and forest, using manual segmentation as a reference. In terms of deviation from the reference shoreline, the results obtained through the analysed methodologies achieved an accuracy of 3.75 m, less than half of the pixel size of the Sentinel-1 and Sentinel-2 used products.Peer Reviewed20232023-05-2520242024-04-02journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/405648https://dx.doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-17-2023reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4056482026-05-27T15:37:01Z
dc.title.none.fl_str_mv A review and test of shoreline extraction techniques
title A review and test of shoreline extraction techniques
spellingShingle A review and test of shoreline extraction techniques
Angelini, Riccardo
Coasts--Remote sensing
Coastal zone management
Climatic changes
Radar
Shoreline extraction
Classifier
Machine learning: Multispectral
Radar
Radar
Zones costaneres--Ordenació
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria civil::Geologia
title_short A review and test of shoreline extraction techniques
title_full A review and test of shoreline extraction techniques
title_fullStr A review and test of shoreline extraction techniques
title_full_unstemmed A review and test of shoreline extraction techniques
title_sort A review and test of shoreline extraction techniques
dc.creator.none.fl_str_mv Angelini, Riccardo
Angelats Company, Eduard
Luzi, Guido
Ribas Prats, Francesca|||0000-0003-4701-5982
Masiero, Andrea
Mugnai, Francesco
author Angelini, Riccardo
author_facet Angelini, Riccardo
Angelats Company, Eduard
Luzi, Guido
Ribas Prats, Francesca|||0000-0003-4701-5982
Masiero, Andrea
Mugnai, Francesco
author_role author
author2 Angelats Company, Eduard
Luzi, Guido
Ribas Prats, Francesca|||0000-0003-4701-5982
Masiero, Andrea
Mugnai, Francesco
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Coasts--Remote sensing
Coastal zone management
Climatic changes
Radar
Shoreline extraction
Classifier
Machine learning: Multispectral
Radar
Radar
Zones costaneres--Ordenació
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria civil::Geologia
topic Coasts--Remote sensing
Coastal zone management
Climatic changes
Radar
Shoreline extraction
Classifier
Machine learning: Multispectral
Radar
Radar
Zones costaneres--Ordenació
Canvis climàtics
Àrees temàtiques de la UPC::Enginyeria civil::Geologia
description Shoreline represents the boundary between land and sea, and its accurate extraction is of utmost importance because of the economic and ecological value of coastal areas. Nowadays, satellite remote sensing is widely used for monitoring the natural environment. Indeed, satellite remote sensing data are cost-effective and periodically available over large areas at a relatively high spatial resolution. Hence, the automatic shoreline extraction from satellite images is a fundamental task for coastal monitoring and management. Shoreline extraction methods are usually applied to satellite remote sensing data. The goal of this study is to compare the performance of different shoreline extraction methods, such as thresholding and more complex classification approaches, such as Random Forest (RF), Minimum Distance (MD), Maximum Likelihood (ML) and K-means, using both optical and radar images. The considered case study area is the shallow basin of the Orbetello Lagoon and one of its ayre called Feniglia. The data supplier is the Copernicus program, which, through the Sentinel-1 and Sentinel-2 missions, provides medium-resolution, open-access products. The accuracy of the obtained results from both methodologies is checked by validating the extracted shoreline using an aerial orthomosaic and, subsequently, a manually extracted shoreline. A preliminary accuracy assessment was performed for image classification, focusing on extracting four classes: water, soil, urban, and forest, using manual segmentation as a reference. In terms of deviation from the reference shoreline, the results obtained through the analysed methodologies achieved an accuracy of 3.75 m, less than half of the pixel size of the Sentinel-1 and Sentinel-2 used products.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-05-25
2024
2024-04-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/405648
https://dx.doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-17-2023
url https://hdl.handle.net/2117/405648
https://dx.doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-17-2023
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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