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

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

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Prades Nebot, J.; Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2020). Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering. Remote Sensing. 12(21):1-22. https://doi.org/10.3390/rs12213585

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/176146

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Title: Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
Author: Prades Nebot, José Safont Armero, Gonzalo Salazar Afanador, Addisson Vergara Domínguez, Luís
UPV Unit: Universitat Politècnica de València. Instituto Universitario de Telecomunicación y Aplicaciones Multimedia - Institut Universitari de Telecomunicacions i Aplicacions Multimèdia
Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[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 ...[+]
Subjects: Agglomerative clustering , Principal component analysis , Model-based clustering , Independent component analysis
Copyrigths: Reconocimiento (by)
Source:
Remote Sensing. (issn: 2072-4292 )
DOI: 10.3390/rs12213585
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/rs12213585
Project ID:
info:eu-repo/grantAgreement/AEI//TEC2017-84743-P-AR//METODOS INFORMADOS PARA LA SINTESIS DE SEÑALES/
Thanks:
This work was supported by Spanish Administration (Ministerio de Ciencia, Innovacion y Universidades) and European Union (FEDER) under grant TEC2017-84743-P.
Type: Artículo

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