Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering

[EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agg...

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Detalhes bibliográficos
Autores: Prades Nebot, José|||0000-0003-4489-3704, Salazar Afanador, Addisson|||0000-0001-5849-5104, Vergara Domínguez, Luís|||0000-0001-6803-4774, Safont Armero, Gonzalo
Tipo de documento: artigo
Data de publicação:2020
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:riunet.upv.es:10251/176146
Acesso em linha:https://riunet.upv.es/handle/10251/176146
Access Level:Acceso aberto
Palavra-chave:Agglomerative clustering
Principal component analysis
Model-based clustering
Independent component analysis
TEORIA DE LA SEÑAL Y COMUNICACIONES
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network_acronym_str ES
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repository_id_str
spelling Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative ClusteringPrades Nebot, José|||0000-0003-4489-3704Salazar Afanador, Addisson|||0000-0001-5849-5104Vergara Domínguez, Luís|||0000-0001-6803-4774Safont Armero, GonzaloAgglomerative clusteringPrincipal component analysisModel-based clusteringIndependent component analysisTEORIA DE LA SEÑAL Y COMUNICACIONES[EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.This work was supported by Spanish Administration (Ministerio de Ciencia, Innovacion y Universidades) and European Union (FEDER) under grant TEC2017-84743-P.MDPI AGEscuela Técnica Superior de Ingeniería de TelecomunicaciónDepartamento de ComunicacionesInstituto Universitario de Telecomunicación y Aplicaciones MultimediaAGENCIA ESTATAL DE INVESTIGACIONEuropean Regional Development FundRepositorio Institucional de la Universitat Politècnica de València Riunet20202020-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/176146reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 TEC2017-84743-P-AR METODOS INFORMADOS PARA LA SINTESIS DE SEÑALESopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1761462026-06-13T07:49:27Z
dc.title.none.fl_str_mv Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
title Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
spellingShingle Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
Prades Nebot, José|||0000-0003-4489-3704
Agglomerative clustering
Principal component analysis
Model-based clustering
Independent component analysis
TEORIA DE LA SEÑAL Y COMUNICACIONES
title_short Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
title_full Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
title_fullStr Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
title_full_unstemmed Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
title_sort Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
dc.creator.none.fl_str_mv Prades Nebot, José|||0000-0003-4489-3704
Salazar Afanador, Addisson|||0000-0001-5849-5104
Vergara Domínguez, Luís|||0000-0001-6803-4774
Safont Armero, Gonzalo
author Prades Nebot, José|||0000-0003-4489-3704
author_facet Prades Nebot, José|||0000-0003-4489-3704
Salazar Afanador, Addisson|||0000-0001-5849-5104
Vergara Domínguez, Luís|||0000-0001-6803-4774
Safont Armero, Gonzalo
author_role author
author2 Salazar Afanador, Addisson|||0000-0001-5849-5104
Vergara Domínguez, Luís|||0000-0001-6803-4774
Safont Armero, Gonzalo
author2_role author
author
author
dc.contributor.none.fl_str_mv Escuela Técnica Superior de Ingeniería de Telecomunicación
Departamento de Comunicaciones
Instituto Universitario de Telecomunicación y Aplicaciones Multimedia
AGENCIA ESTATAL DE INVESTIGACION
European Regional Development Fund
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Agglomerative clustering
Principal component analysis
Model-based clustering
Independent component analysis
TEORIA DE LA SEÑAL Y COMUNICACIONES
topic Agglomerative clustering
Principal component analysis
Model-based clustering
Independent component analysis
TEORIA DE LA SEÑAL Y COMUNICACIONES
description [EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-11-01
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://riunet.upv.es/handle/10251/176146
url https://riunet.upv.es/handle/10251/176146
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 TEC2017-84743-P-AR METODOS INFORMADOS PARA LA SINTESIS DE SEÑALES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/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
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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