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Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning.

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Název: Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning.
Autoři: Okada, Natsuo, Bino Sinaice, Brian, Kim, Jaewon, Nozaki, Hiromasa, Takizawa, Kaito, Owada, Narihiro, Ohtomo, Yoko, Kawamura, Youhei
Zdroj: International Journal of Mining, Reclamation & Environment; Dec2024, Vol. 38 Issue 10, p851-871, 21p
Témata: INDUSTRIAL minerals, GEOLOGICAL surveys, ELECTRONIC data processing, MACHINE learning, MINERAL processing
Abstrakt: The study presents a new technique that combines hyperspectral imaging and machine learning to identify minerals. Overcoming the challenges of processing hyperspectral data, the solution offers a user-friendly hyperspectral analysis tool tailored for constructing datasets and enhancing mineral identification and concentration estimation. The tool integrates hyperspectral data processing with segmentation, simplifying complex operations and making mineral identification accessible to non-experts. The tool's capabilities extend to handling multi-spectral data efficiently, potentially leading to energy-efficient analysis when combined with dimensionality reduction techniques. Presented as a novel approach, it improves geological surveys in mining areas, enabling industrial applications and mineral research. [ABSTRACT FROM AUTHOR]
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Abstrakt:The study presents a new technique that combines hyperspectral imaging and machine learning to identify minerals. Overcoming the challenges of processing hyperspectral data, the solution offers a user-friendly hyperspectral analysis tool tailored for constructing datasets and enhancing mineral identification and concentration estimation. The tool integrates hyperspectral data processing with segmentation, simplifying complex operations and making mineral identification accessible to non-experts. The tool's capabilities extend to handling multi-spectral data efficiently, potentially leading to energy-efficient analysis when combined with dimensionality reduction techniques. Presented as a novel approach, it improves geological surveys in mining areas, enabling industrial applications and mineral research. [ABSTRACT FROM AUTHOR]
ISSN:17480930
DOI:10.1080/17480930.2024.2360743