Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping
Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patte...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/12/2296 |
| _version_ | 1797530474450518016 |
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| author | Isabel Cecilia Contreras Acosta Mahdi Khodadadzadeh Richard Gloaguen |
| author_facet | Isabel Cecilia Contreras Acosta Mahdi Khodadadzadeh Richard Gloaguen |
| author_sort | Isabel Cecilia Contreras Acosta |
| collection | DOAJ |
| description | Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling. |
| first_indexed | 2024-03-10T10:29:30Z |
| format | Article |
| id | doaj.art-a972702624fa4e87957f8fc2bc4f3a81 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T10:29:30Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a972702624fa4e87957f8fc2bc4f3a812023-11-21T23:46:04ZengMDPI AGRemote Sensing2072-42922021-06-011312229610.3390/rs13122296Resolution Enhancement for Drill-Core Hyperspectral Mineral MappingIsabel Cecilia Contreras Acosta0Mahdi Khodadadzadeh1Richard Gloaguen2Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, 09599 Freiberg, GermanyHelmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, 09599 Freiberg, GermanyHelmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Chemnitzer Str. 40, 09599 Freiberg, GermanyDrill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling.https://www.mdpi.com/2072-4292/13/12/2296resolution enhancementsampling enhancementhyperspectralhigh-spatial resolution multi-spectraldrill-coresmineral mapping |
| spellingShingle | Isabel Cecilia Contreras Acosta Mahdi Khodadadzadeh Richard Gloaguen Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping Remote Sensing resolution enhancement sampling enhancement hyperspectral high-spatial resolution multi-spectral drill-cores mineral mapping |
| title | Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping |
| title_full | Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping |
| title_fullStr | Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping |
| title_full_unstemmed | Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping |
| title_short | Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping |
| title_sort | resolution enhancement for drill core hyperspectral mineral mapping |
| topic | resolution enhancement sampling enhancement hyperspectral high-spatial resolution multi-spectral drill-cores mineral mapping |
| url | https://www.mdpi.com/2072-4292/13/12/2296 |
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