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HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy

Supervised analysis using spectral data requires a well-informed characterisation of the response variables and abundant spectral data points. The presented hyperspectral dataset comes from five sets of geometallurgical samples, each characterised by different methods. To provide the spectral data,...

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Autores principales: Ehrenfeld, Alejandro, Egaña, Álvaro F., Santibañez-Leal, Felipe, Garrido, Felipe, Ojeda, Marcia, Townley, Brian, Navarro, Felipe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036318/
https://www.ncbi.nlm.nih.gov/pubmed/36959253
http://dx.doi.org/10.1038/s41597-023-02061-x
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author Ehrenfeld, Alejandro
Egaña, Álvaro F.
Santibañez-Leal, Felipe
Garrido, Felipe
Ojeda, Marcia
Townley, Brian
Navarro, Felipe
author_facet Ehrenfeld, Alejandro
Egaña, Álvaro F.
Santibañez-Leal, Felipe
Garrido, Felipe
Ojeda, Marcia
Townley, Brian
Navarro, Felipe
author_sort Ehrenfeld, Alejandro
collection PubMed
description Supervised analysis using spectral data requires a well-informed characterisation of the response variables and abundant spectral data points. The presented hyperspectral dataset comes from five sets of geometallurgical samples, each characterised by different methods. To provide the spectral data, all mineral samples were scanned with SPECIM VNIR and SWIR hyperspectral cameras. For each subset the following data are provided 1) hyperspectral reflectance images in the VNIR spectral range (400–1000 nm wavelength); 2) hyperspectral reflectance images in the SWIR spectral range (900–2500 nm wavelength); 3) hyperspectral reflectance images in the VNIR-SWIR range (merged to SWIR spatial resolution); 4) RGB images constructed from hyperspectral data using a Bilateral Filter based sensor fusion method; 5) response variables representing mineral sample characterisation results, provided as training and validation data. This dataset is intended for use in general regression and classification research and experiments. All subsets were validated using machine learning models with satisfactory results.
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spelling pubmed-100363182023-03-25 HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy Ehrenfeld, Alejandro Egaña, Álvaro F. Santibañez-Leal, Felipe Garrido, Felipe Ojeda, Marcia Townley, Brian Navarro, Felipe Sci Data Data Descriptor Supervised analysis using spectral data requires a well-informed characterisation of the response variables and abundant spectral data points. The presented hyperspectral dataset comes from five sets of geometallurgical samples, each characterised by different methods. To provide the spectral data, all mineral samples were scanned with SPECIM VNIR and SWIR hyperspectral cameras. For each subset the following data are provided 1) hyperspectral reflectance images in the VNIR spectral range (400–1000 nm wavelength); 2) hyperspectral reflectance images in the SWIR spectral range (900–2500 nm wavelength); 3) hyperspectral reflectance images in the VNIR-SWIR range (merged to SWIR spatial resolution); 4) RGB images constructed from hyperspectral data using a Bilateral Filter based sensor fusion method; 5) response variables representing mineral sample characterisation results, provided as training and validation data. This dataset is intended for use in general regression and classification research and experiments. All subsets were validated using machine learning models with satisfactory results. Nature Publishing Group UK 2023-03-23 /pmc/articles/PMC10036318/ /pubmed/36959253 http://dx.doi.org/10.1038/s41597-023-02061-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Ehrenfeld, Alejandro
Egaña, Álvaro F.
Santibañez-Leal, Felipe
Garrido, Felipe
Ojeda, Marcia
Townley, Brian
Navarro, Felipe
HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
title HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
title_full HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
title_fullStr HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
title_full_unstemmed HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
title_short HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy
title_sort hidsag: hyperspectral image database for supervised analysis in geometallurgy
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036318/
https://www.ncbi.nlm.nih.gov/pubmed/36959253
http://dx.doi.org/10.1038/s41597-023-02061-x
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