Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping

Autores
Martinez Vargas, Steven; Genchi, Sibila Andrea; Vitale, Alejandro José; Delrieux, Claudio Augusto
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The combined application of machine or deep learning algorithms and hyperspectral imagery for bathymetry estimation is currently an emerging field with widespread uses and applications. This research topic still requires further investigation to achieve methodological robustness and accuracy. In this study, we introduce a novel methodology for shallow bathymetric mapping using a one-dimensional convolutional neural network (1D-CNN) applied to PRISMA hyperspectral images, including refinements to enhance mapping accuracy, together with the optimization of computational efficiency. Four different 1D-CNN models were developed, incorporating pansharpening and spectral band optimization. Model performance was rigorously evaluated against reference bathymetric data obtained from official nautical charts provided by the Servicio de Hidrografía Naval (Argentina). The BoPsCNN model achieved the best testing accuracy with a coefficient of determination of 0.96 and a root mean square error of 0.65 m for a depth range of 0–15 m. The implementation of band optimization significantly reduced computational overhead, yielding a time-saving efficiency of 31–38%. The resulting bathymetric maps exhibited a coherent depth gradient from nearshore to offshore zones, with enhanced seabed morphology representation, particularly in models using pansharpened data.
Fil: Martinez Vargas, Steven. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina
Fil: Genchi, Sibila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina
Fil: Vitale, Alejandro José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
Materia
BATHYMETRY
SHALLOW WATERS
PRISMA HYPERSPECTRAL IMAGERY
1D-CNN
PANSHARPENING
BAND OPTIMIZATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/284769
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/284769
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution MappingMartinez Vargas, StevenGenchi, Sibila AndreaVitale, Alejandro JoséDelrieux, Claudio AugustoBATHYMETRYSHALLOW WATERSPRISMA HYPERSPECTRAL IMAGERY1D-CNNPANSHARPENINGBAND OPTIMIZATIONhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The combined application of machine or deep learning algorithms and hyperspectral imagery for bathymetry estimation is currently an emerging field with widespread uses and applications. This research topic still requires further investigation to achieve methodological robustness and accuracy. In this study, we introduce a novel methodology for shallow bathymetric mapping using a one-dimensional convolutional neural network (1D-CNN) applied to PRISMA hyperspectral images, including refinements to enhance mapping accuracy, together with the optimization of computational efficiency. Four different 1D-CNN models were developed, incorporating pansharpening and spectral band optimization. Model performance was rigorously evaluated against reference bathymetric data obtained from official nautical charts provided by the Servicio de Hidrografía Naval (Argentina). The BoPsCNN model achieved the best testing accuracy with a coefficient of determination of 0.96 and a root mean square error of 0.65 m for a depth range of 0–15 m. The implementation of band optimization significantly reduced computational overhead, yielding a time-saving efficiency of 31–38%. The resulting bathymetric maps exhibited a coherent depth gradient from nearshore to offshore zones, with enhanced seabed morphology representation, particularly in models using pansharpened data.Fil: Martinez Vargas, Steven. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Genchi, Sibila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Vitale, Alejandro José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaMDPI2025-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/284769Martinez Vargas, Steven; Genchi, Sibila Andrea; Vitale, Alejandro José; Delrieux, Claudio Augusto; Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping; MDPI; Remote Sensing; 17; 21; 10-2025; 1-212072-4292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/17/21/3584info:eu-repo/semantics/altIdentifier/doi/10.3390/rs17213584info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:39:43Zoai:ri.conicet.gov.ar:11336/284769instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:39:43.862CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
title Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
spellingShingle Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
Martinez Vargas, Steven
BATHYMETRY
SHALLOW WATERS
PRISMA HYPERSPECTRAL IMAGERY
1D-CNN
PANSHARPENING
BAND OPTIMIZATION
title_short Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
title_full Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
title_fullStr Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
title_full_unstemmed Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
title_sort Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping
dc.creator.none.fl_str_mv Martinez Vargas, Steven
Genchi, Sibila Andrea
Vitale, Alejandro José
Delrieux, Claudio Augusto
author Martinez Vargas, Steven
author_facet Martinez Vargas, Steven
Genchi, Sibila Andrea
Vitale, Alejandro José
Delrieux, Claudio Augusto
author_role author
author2 Genchi, Sibila Andrea
Vitale, Alejandro José
Delrieux, Claudio Augusto
author2_role author
author
author
dc.subject.none.fl_str_mv BATHYMETRY
SHALLOW WATERS
PRISMA HYPERSPECTRAL IMAGERY
1D-CNN
PANSHARPENING
BAND OPTIMIZATION
topic BATHYMETRY
SHALLOW WATERS
PRISMA HYPERSPECTRAL IMAGERY
1D-CNN
PANSHARPENING
BAND OPTIMIZATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The combined application of machine or deep learning algorithms and hyperspectral imagery for bathymetry estimation is currently an emerging field with widespread uses and applications. This research topic still requires further investigation to achieve methodological robustness and accuracy. In this study, we introduce a novel methodology for shallow bathymetric mapping using a one-dimensional convolutional neural network (1D-CNN) applied to PRISMA hyperspectral images, including refinements to enhance mapping accuracy, together with the optimization of computational efficiency. Four different 1D-CNN models were developed, incorporating pansharpening and spectral band optimization. Model performance was rigorously evaluated against reference bathymetric data obtained from official nautical charts provided by the Servicio de Hidrografía Naval (Argentina). The BoPsCNN model achieved the best testing accuracy with a coefficient of determination of 0.96 and a root mean square error of 0.65 m for a depth range of 0–15 m. The implementation of band optimization significantly reduced computational overhead, yielding a time-saving efficiency of 31–38%. The resulting bathymetric maps exhibited a coherent depth gradient from nearshore to offshore zones, with enhanced seabed morphology representation, particularly in models using pansharpened data.
Fil: Martinez Vargas, Steven. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina
Fil: Genchi, Sibila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina
Fil: Vitale, Alejandro José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina
Fil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; Argentina
description The combined application of machine or deep learning algorithms and hyperspectral imagery for bathymetry estimation is currently an emerging field with widespread uses and applications. This research topic still requires further investigation to achieve methodological robustness and accuracy. In this study, we introduce a novel methodology for shallow bathymetric mapping using a one-dimensional convolutional neural network (1D-CNN) applied to PRISMA hyperspectral images, including refinements to enhance mapping accuracy, together with the optimization of computational efficiency. Four different 1D-CNN models were developed, incorporating pansharpening and spectral band optimization. Model performance was rigorously evaluated against reference bathymetric data obtained from official nautical charts provided by the Servicio de Hidrografía Naval (Argentina). The BoPsCNN model achieved the best testing accuracy with a coefficient of determination of 0.96 and a root mean square error of 0.65 m for a depth range of 0–15 m. The implementation of band optimization significantly reduced computational overhead, yielding a time-saving efficiency of 31–38%. The resulting bathymetric maps exhibited a coherent depth gradient from nearshore to offshore zones, with enhanced seabed morphology representation, particularly in models using pansharpened data.
publishDate 2025
dc.date.none.fl_str_mv 2025-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/284769
Martinez Vargas, Steven; Genchi, Sibila Andrea; Vitale, Alejandro José; Delrieux, Claudio Augusto; Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping; MDPI; Remote Sensing; 17; 21; 10-2025; 1-21
2072-4292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/284769
identifier_str_mv Martinez Vargas, Steven; Genchi, Sibila Andrea; Vitale, Alejandro José; Delrieux, Claudio Augusto; Shallow Bathymetry from Hyperspectral Imagery Using 1D-CNN: An Innovative Methodology for High Resolution Mapping; MDPI; Remote Sensing; 17; 21; 10-2025; 1-21
2072-4292
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/17/21/3584
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs17213584
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.265058

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