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    dc.contributor.authorChanchí Golondrino, Gabriel Elías-
    dc.contributor.authorSaba, Manuel-
    dc.contributor.authorOspina Alarcón, Manuel Alejando-
    dc.date.accessioned2025-10-14T21:15:13Z-
    dc.date.available2025-10-14T21:15:13Z-
    dc.date.issued2025-01-01-
    dc.identifier.citationG. E. Chanchí Golondrino, M. Saba, y M. A. Ospina Alarcón, «Propuesta de un método computacional para la detección de asbesto en imágenes hiperespectrales a partir de la similitud diferencial espectral», RCTA, vol. 1, n.º 45, pp. 195–203, ene. 2025. https://doi.org/10.24054/rcta.v1i45.3279es_CO
    dc.identifier.issn1692-7257-
    dc.identifier.issn2500-8625-
    dc.identifier.urihttp://repositoriodspace.unipamplona.edu.co/jspui/handle/20.500.12744/10374-
    dc.descriptionTeniendo en cuenta que uno de los desafíos de las imágenes hiperespectrales es la identificación de métodos que permitan la detección de materiales de manera eficaz y eficiente, en este artículo se propuso un nuevo método para la detección de asbesto en imágenes hiperespectrales basado en la similitud diferencial espectral, a través del cual es posible determinar que tan cercana es la firma espectral de un pixel determinado con respecto a la firma espectral del asbesto. El método propuesto fue implementado mediante el uso de librerías del dominio del código abierto tales como: spectral, numpy, pandas y matplotlib, obteniendo que con respecto al método de correlación fue detectado un 0.813% menos pixeles de vegetación. Así mismo, se obtuvo a nivel de la eficiencia computacional que el método propuesto resultó 4.27 veces más rápido que el método de correlación. Los resultados obtenidos permiten concluir que el método propuesto presenta una adecuada eficacia y una excelente eficiencia, lo cual permite que pueda ser considerado para ser integrado en herramientas para el procesamiento y análisis de imágenes hiperespectrales en el dominio académico y empresarial.es_CO
    dc.description.abstractConsidering that one of the challenges of hyperspectral imaging is identifying methods that enable the effective and efficient detection of materials, this article proposes a new method for detecting asbestos in hyperspectral images based on spectral differential similarity. This method determines how closely the spectral signature of a given pixel matches the spectral signature of asbestos. The proposed method was implemented using open-source libraries such as spectral, numpy, pandas, and matplotlib. Compared to the correlation method, it detected 0.813% fewer vegetation pixels. In terms of computational efficiency, the proposed method was 4.27 times faster than the correlation method. The results indicate that the proposed method demonstrates adequate efficacy and excellent efficiency, making it a strong candidate for integration into tools for processing and analyzing hyperspectral images in academic and industrial domains.es_CO
    dc.format.extent9es_CO
    dc.format.mimetypeapplication/pdfes_CO
    dc.language.isoeses_CO
    dc.publisherAldo Pardo García, Revista Colombiana de Tecnologías de Avanzada, Universidad de Pamplona.es_CO
    dc.relation.ispartofseries195;203-
    dc.subjectasbestoes_CO
    dc.subjectcorrelaciónes_CO
    dc.subjectimagen hiperespectrales_CO
    dc.subjectfirma espectrales_CO
    dc.subjectsensado remotoes_CO
    dc.titlePropuesta de un método computacional para la detección de asbesto en imágenes hiperespectrales a partir de la similitud diferencial espectrales_CO
    dc.title.alternativeProposal of a method for asbestos detection in hyperspectral images based on spectral differential similarityes_CO
    dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1es_CO
    dc.description.editionVol. 1 Núm. 45 (2025): Enero – Junioes_CO
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    dc.type.coarversionhttp://purl.org/coar/resource_type/c_2df8fbb1es_CO
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