GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images

The high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation. The waterpixel algorithm, in particular, extracts segmentation regions called waterpixels and consists of four stages called vectorial g...

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Detalhes bibliográficos
Autores: Quesada Barriuso, Pablo, Blanco Heras, Dora, Argüello Pedreira, Francisco
Tipo de documento: artigo
Data de publicação:2021
País:España
Recursos:Universidad de Santiago de Compostela (USC)
Repositório:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Idioma:espanhol
OAI Identifier:oai:minerva.usc.gal:10347/38537
Acesso em linha:https://hdl.handle.net/10347/38537
Access Level:Acceso aberto
Palavra-chave:Superpixel segmentation
Watershed transform
Waterpixel segmentation
Hyperspectral image
Remote sensing
CUDA
120317 Informática
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spelling GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral ImagesQuesada Barriuso, PabloBlanco Heras, DoraArgüello Pedreira, FranciscoSuperpixel segmentationWatershed transformWaterpixel segmentationHyperspectral imageRemote sensingCUDA120317 InformáticaThe high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation. The waterpixel algorithm, in particular, extracts segmentation regions called waterpixels and consists of four stages called vectorial gradient, spatial regularization, marker selection, and watershed transform. In this paper, an efficient version of a GPU algorithm for waterpixel segmentation using the Compute Unified Device Architecture (CUDA) is presented. The algorithm extracts all the spectral information available in the bands of the hyperspectral image through the vectorial gradient. A cellular automaton is selected for the computation of the watershed transform using a block-asynchronous implementation with 8-connectivity. The experimental analysis shows high speedup values for the resulting GPU algorithm when it is compared to a multicore OpenMP implementation using 8 threads.SpringerUniversidade de Santiago de Compostela. Departamento de Electrónica e ComputaciónUniversidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)20212021-02-2220212021-02-22journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10347/38537reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostelainstname:Universidad de Santiago de Compostela (USC)EspañolspaAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104834GB-I00 COMPUTACION DE ALTAS PRESTACIONES Y CLOUD PARA APLICACIONES DE ALTO INTERESopen 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:minerva.usc.gal:10347/385372026-06-15T12:47:27Z
dc.title.none.fl_str_mv GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
title GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
spellingShingle GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
Quesada Barriuso, Pablo
Superpixel segmentation
Watershed transform
Waterpixel segmentation
Hyperspectral image
Remote sensing
CUDA
120317 Informática
title_short GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
title_full GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
title_fullStr GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
title_full_unstemmed GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
title_sort GPU Accelerated Waterpixel algorithm for Superpixel Segmentation of Hyperspectral Images
dc.creator.none.fl_str_mv Quesada Barriuso, Pablo
Blanco Heras, Dora
Argüello Pedreira, Francisco
author Quesada Barriuso, Pablo
author_facet Quesada Barriuso, Pablo
Blanco Heras, Dora
Argüello Pedreira, Francisco
author_role author
author2 Blanco Heras, Dora
Argüello Pedreira, Francisco
author2_role author
author
dc.contributor.none.fl_str_mv Universidade de Santiago de Compostela. Departamento de Electrónica e Computación
Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías Intelixentes da USC (CiTIUS)
dc.subject.none.fl_str_mv Superpixel segmentation
Watershed transform
Waterpixel segmentation
Hyperspectral image
Remote sensing
CUDA
120317 Informática
topic Superpixel segmentation
Watershed transform
Waterpixel segmentation
Hyperspectral image
Remote sensing
CUDA
120317 Informática
description The high computational cost of the superpixel segmentation algorithms for hyperspectral remote sensing images makes them ideal candidates for parallel computation. The waterpixel algorithm, in particular, extracts segmentation regions called waterpixels and consists of four stages called vectorial gradient, spatial regularization, marker selection, and watershed transform. In this paper, an efficient version of a GPU algorithm for waterpixel segmentation using the Compute Unified Device Architecture (CUDA) is presented. The algorithm extracts all the spectral information available in the bands of the hyperspectral image through the vectorial gradient. A cellular automaton is selected for the computation of the watershed transform using a block-asynchronous implementation with 8-connectivity. The experimental analysis shows high speedup values for the resulting GPU algorithm when it is compared to a multicore OpenMP implementation using 8 threads.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-02-22
2021
2021-02-22
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10347/38537
url https://hdl.handle.net/10347/38537
dc.language.none.fl_str_mv Español
spa
language_invalid_str_mv Español
language spa
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104834GB-I00 COMPUTACION DE ALTAS PRESTACIONES Y CLOUD PARA APLICACIONES DE ALTO INTERES
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.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
instname:Universidad de Santiago de Compostela (USC)
instname_str Universidad de Santiago de Compostela (USC)
reponame_str Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
collection Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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