Automatic shoreline detection by processing planview timex images using bi-LSTM networks

18 pages, 14 figures, 7 tables.-- Data availability: The matlab code and a data package to make it work will be available on github

Detalles Bibliográficos
Autores: Martí-Puig, Pere, Serra-Serra, Moises, Ribas, Francesca, Simarro, Gonzalo, Caballeria, Miquel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/356444
Acceso en línea:http://hdl.handle.net/10261/356444
Access Level:acceso abierto
Palabra clave:Coastal environment
Timex video images
Automatic shoreline detection
Bi-LSTM neural network
CIELAB colour space
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spelling Automatic shoreline detection by processing planview timex images using bi-LSTM networksMartí-Puig, PereSerra-Serra, MoisesRibas, FrancescaSimarro, GonzaloCaballeria, MiquelCoastal environmentTimex video imagesAutomatic shoreline detectionBi-LSTM neural networkCIELAB colour space18 pages, 14 figures, 7 tables.-- Data availability: The matlab code and a data package to make it work will be available on githubA new automatic shoreline detection method by using a bidirectional Long Short-Term Memory (bi-LSTM) Network that processes images column by column is presented. The model is trained on manually extracted shorelines from time-exposure video-images and is very robust against the selection of images for training. Thanks to the novelty of working with image columns, instead of with the whole image, the amount of labelled images for training is limited to a few tens or even less if the conditions are good. Moreover, this column approach makes the model to be robust to variable illuminated images and more easily interpretable, light and fast. There is a wide range of configuration parameters for the bi-LSTM layer by which the system works correctly, which facilitate to use the same network in different video stations. The highest accuracy is obtained by using CIELAB colour space. Without pre-processing the raw colour channels or defining a region of interest and without post-processing the obtained shorelines, the model demonstrates impressive accuracy with mean errors of 2.8 pixels (1.4 m) in Castelldefels and 1.7 pixels (0.85 m) in Barcelona. The method could also be effective for satellite shoreline detection by using as input channel the water index of the satellite detection techniques.This work has been funded by the Spanish government through the research project RTI2018-093941-B-C33 (MINECO/FEDER)With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewedElsevierMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/356444reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093941-B-C33https://doi.org/10.1016/j.eswa.2023.122566Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3564442026-05-22T06:33:51Z
dc.title.none.fl_str_mv Automatic shoreline detection by processing planview timex images using bi-LSTM networks
title Automatic shoreline detection by processing planview timex images using bi-LSTM networks
spellingShingle Automatic shoreline detection by processing planview timex images using bi-LSTM networks
Martí-Puig, Pere
Coastal environment
Timex video images
Automatic shoreline detection
Bi-LSTM neural network
CIELAB colour space
title_short Automatic shoreline detection by processing planview timex images using bi-LSTM networks
title_full Automatic shoreline detection by processing planview timex images using bi-LSTM networks
title_fullStr Automatic shoreline detection by processing planview timex images using bi-LSTM networks
title_full_unstemmed Automatic shoreline detection by processing planview timex images using bi-LSTM networks
title_sort Automatic shoreline detection by processing planview timex images using bi-LSTM networks
dc.creator.none.fl_str_mv Martí-Puig, Pere
Serra-Serra, Moises
Ribas, Francesca
Simarro, Gonzalo
Caballeria, Miquel
author Martí-Puig, Pere
author_facet Martí-Puig, Pere
Serra-Serra, Moises
Ribas, Francesca
Simarro, Gonzalo
Caballeria, Miquel
author_role author
author2 Serra-Serra, Moises
Ribas, Francesca
Simarro, Gonzalo
Caballeria, Miquel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Coastal environment
Timex video images
Automatic shoreline detection
Bi-LSTM neural network
CIELAB colour space
topic Coastal environment
Timex video images
Automatic shoreline detection
Bi-LSTM neural network
CIELAB colour space
description 18 pages, 14 figures, 7 tables.-- Data availability: The matlab code and a data package to make it work will be available on github
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/356444
url http://hdl.handle.net/10261/356444
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093941-B-C33
https://doi.org/10.1016/j.eswa.2023.122566
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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