Hyper- and multispectral image analysis methods for mineral identification: two cases of study

This study briefly reviews geological applications of optical remote sensing and puts in practice some data analysis methods through two cases of study: ore assessment through the analysis of a hyperspectral image of a test panel of mine samples, and the recognition of mine waste materials through t...

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Detalles Bibliográficos
Autor: Barroso, Gisela
Tipo de recurso: tesis de maestría
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/233456
Acceso en línea:http://hdl.handle.net/10261/233456
Access Level:acceso abierto
Palabra clave:ASTER
Mining
Hyperspectral Remote Sensing
Imaging spectrometry
Mineral exploration
Multispectral Remote Sensing
Machine Learning.
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repository_id_str
spelling Hyper- and multispectral image analysis methods for mineral identification: two cases of studyBarroso, GiselaASTERMiningHyperspectral Remote SensingImaging spectrometryMineral explorationMultispectral Remote SensingMachine Learning.This study briefly reviews geological applications of optical remote sensing and puts in practice some data analysis methods through two cases of study: ore assessment through the analysis of a hyperspectral image of a test panel of mine samples, and the recognition of mine waste materials through the analysis of a multi-spectral satellite image of a mining site (Sotiel, SW Spain). Principal Components and Linear Discriminant Analysis have been applied, as well as classification methods (Random Forest, Support Vector and Spectral Angle Mapper). We also have analysed and interpreted the spectral signatures. In the case of the panel of mine samples, Random Forest classification has been able to discriminate ore (cassiterite and wolframite) with a 96 % of accuracy, which puts in evidence the interest of developing “in situ” systems for the fast assessment of ore grade based on hyperspectral sensors. In the case of the mining area, our results have shown a high consistency of ASTER spectral signatures with the spectra of the endmember minerals previously derived by Spectral Mixture Analysis from aerial hyperspectral imagery by other authors, and a similar geographic distribution of major contaminated areas has been found. Water polluted by mine waste at different intensities in the mine ponds has been detected in the ASTER imagery. Combining the high analytical capabilities of aerial hyperspectral imagery with the extensive monitoring capabilities of satellite imagery can be effective for the control of mining environments.Publiser versionLobo Aleu, AgustínConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2021202120202021info:eu-repo/semantics/masterThesishttp://purl.org/coar/resource_type/c_bdcchttp://hdl.handle.net/10261/233456reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2334562026-05-22T06:33:51Z
dc.title.none.fl_str_mv Hyper- and multispectral image analysis methods for mineral identification: two cases of study
title Hyper- and multispectral image analysis methods for mineral identification: two cases of study
spellingShingle Hyper- and multispectral image analysis methods for mineral identification: two cases of study
Barroso, Gisela
ASTER
Mining
Hyperspectral Remote Sensing
Imaging spectrometry
Mineral exploration
Multispectral Remote Sensing
Machine Learning.
title_short Hyper- and multispectral image analysis methods for mineral identification: two cases of study
title_full Hyper- and multispectral image analysis methods for mineral identification: two cases of study
title_fullStr Hyper- and multispectral image analysis methods for mineral identification: two cases of study
title_full_unstemmed Hyper- and multispectral image analysis methods for mineral identification: two cases of study
title_sort Hyper- and multispectral image analysis methods for mineral identification: two cases of study
dc.creator.none.fl_str_mv Barroso, Gisela
author Barroso, Gisela
author_facet Barroso, Gisela
author_role author
dc.contributor.none.fl_str_mv Lobo Aleu, Agustín
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv ASTER
Mining
Hyperspectral Remote Sensing
Imaging spectrometry
Mineral exploration
Multispectral Remote Sensing
Machine Learning.
topic ASTER
Mining
Hyperspectral Remote Sensing
Imaging spectrometry
Mineral exploration
Multispectral Remote Sensing
Machine Learning.
description This study briefly reviews geological applications of optical remote sensing and puts in practice some data analysis methods through two cases of study: ore assessment through the analysis of a hyperspectral image of a test panel of mine samples, and the recognition of mine waste materials through the analysis of a multi-spectral satellite image of a mining site (Sotiel, SW Spain). Principal Components and Linear Discriminant Analysis have been applied, as well as classification methods (Random Forest, Support Vector and Spectral Angle Mapper). We also have analysed and interpreted the spectral signatures. In the case of the panel of mine samples, Random Forest classification has been able to discriminate ore (cassiterite and wolframite) with a 96 % of accuracy, which puts in evidence the interest of developing “in situ” systems for the fast assessment of ore grade based on hyperspectral sensors. In the case of the mining area, our results have shown a high consistency of ASTER spectral signatures with the spectra of the endmember minerals previously derived by Spectral Mixture Analysis from aerial hyperspectral imagery by other authors, and a similar geographic distribution of major contaminated areas has been found. Water polluted by mine waste at different intensities in the mine ponds has been detected in the ASTER imagery. Combining the high analytical capabilities of aerial hyperspectral imagery with the extensive monitoring capabilities of satellite imagery can be effective for the control of mining environments.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
http://purl.org/coar/resource_type/c_bdcc
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/233456
url http://hdl.handle.net/10261/233456
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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