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Article

Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems

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Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, Japan
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Technical Division, Faculty of International Resource Sciences, Akita University, Akita 010-8502, Japan
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Department of Geology, University of Botswana, Private Bag UB 0022, Gaborone, Botswana
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Division of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Rajive Ganguli, Sean Dessureault, Pratt Rogers and Amin Beiranvand Pour
Minerals 2021, 11(8), 846; https://doi.org/10.3390/min11080846
Received: 20 May 2021 / Revised: 26 July 2021 / Accepted: 3 August 2021 / Published: 5 August 2021
Though multitudes of industries depend on the mining industry for resources, this industry has taken hits in terms of declining mineral ore grades and its current use of traditional, time-consuming and computationally costly rock and mineral identification methods. Therefore, this paper proposes integrating Hyperspectral Imaging, Neighbourhood Component Analysis (NCA) and Machine Learning (ML) as a combined system that can identify rocks and minerals. Modestly put, hyperspectral imaging gathers electromagnetic signatures of the rocks in hundreds of spectral bands. However, this data suffers from what is termed the ‘dimensionality curse’, which led to our employment of NCA as a dimensionality reduction technique. NCA, in turn, highlights the most discriminant feature bands, number of which being dependent on the intended application(s) of this system. Our envisioned application is rock and mineral classification via unmanned aerial vehicle (UAV) drone technology. In this study, we performed a 204-hyperspectral to 5-band multispectral reduction, because current production drones are limited to five multispectral bands sensors. Based on these bands, we applied ML to identify and classify rocks, thereby proving our hypothesis, reducing computational costs, attaining an ML classification accuracy of 71%, and demonstrating the potential mining industry optimisations attainable through this integrated system. View Full-Text
Keywords: hyperspectral imaging; multispectral imaging; dimensionality reduction; neighbourhood component analysis; artificial intelligence; machine learning hyperspectral imaging; multispectral imaging; dimensionality reduction; neighbourhood component analysis; artificial intelligence; machine learning
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MDPI and ACS Style

Sinaice, B.B.; Owada, N.; Saadat, M.; Toriya, H.; Inagaki, F.; Bagai, Z.; Kawamura, Y. Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems. Minerals 2021, 11, 846. https://doi.org/10.3390/min11080846

AMA Style

Sinaice BB, Owada N, Saadat M, Toriya H, Inagaki F, Bagai Z, Kawamura Y. Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems. Minerals. 2021; 11(8):846. https://doi.org/10.3390/min11080846

Chicago/Turabian Style

Sinaice, Brian B., Narihiro Owada, Mahdi Saadat, Hisatoshi Toriya, Fumiaki Inagaki, Zibisani Bagai, and Youhei Kawamura. 2021. "Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems" Minerals 11, no. 8: 846. https://doi.org/10.3390/min11080846

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