Parallel hyperspectral unmixing method via split augmented lagrangian on GPU
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.21/6137 |
Resumo: | One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy. |
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Parallel hyperspectral unmixing method via split augmented lagrangian on GPUGraphics processing unitsGPUHyperspectral endmember extractionOnboard processingSimplex identification via split augmented LagrangianSISALOne of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.IEEE - Institute of Electrical and Electronics Engineers Inc.RCIPLSevilla, JorgeMartin, GabrielNascimento, Jose2016-05-02T11:00:19Z2016-052016-05-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.21/6137engSEVILLA, Jorge; [et al] - Parallel hyperspectral unmixing method via split augmented lagrangian on GPU. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. Vol. 13, N.º 5 (2016), pp. 626-6301545-598X10.1109/LGRS.2016.2522561metadata only accessinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-08-03T09:50:33Zoai:repositorio.ipl.pt:10400.21/6137Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:15:19.638217Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU |
title |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU |
spellingShingle |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU Sevilla, Jorge Graphics processing units GPU Hyperspectral endmember extraction Onboard processing Simplex identification via split augmented Lagrangian SISAL |
title_short |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU |
title_full |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU |
title_fullStr |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU |
title_full_unstemmed |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU |
title_sort |
Parallel hyperspectral unmixing method via split augmented lagrangian on GPU |
author |
Sevilla, Jorge |
author_facet |
Sevilla, Jorge Martin, Gabriel Nascimento, Jose |
author_role |
author |
author2 |
Martin, Gabriel Nascimento, Jose |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
RCIPL |
dc.contributor.author.fl_str_mv |
Sevilla, Jorge Martin, Gabriel Nascimento, Jose |
dc.subject.por.fl_str_mv |
Graphics processing units GPU Hyperspectral endmember extraction Onboard processing Simplex identification via split augmented Lagrangian SISAL |
topic |
Graphics processing units GPU Hyperspectral endmember extraction Onboard processing Simplex identification via split augmented Lagrangian SISAL |
description |
One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-05-02T11:00:19Z 2016-05 2016-05-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.21/6137 |
url |
http://hdl.handle.net/10400.21/6137 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
SEVILLA, Jorge; [et al] - Parallel hyperspectral unmixing method via split augmented lagrangian on GPU. IEEE Geoscience and Remote Sensing Letters. ISSN 1545-598X. Vol. 13, N.º 5 (2016), pp. 626-630 1545-598X 10.1109/LGRS.2016.2522561 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE - Institute of Electrical and Electronics Engineers Inc. |
publisher.none.fl_str_mv |
IEEE - Institute of Electrical and Electronics Engineers Inc. |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799133412015996928 |