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

Minerals

Zibisani Bagai¹  Fumiaki Inagaki²  Hisatoshi Toriya²  Mahdi Saadat²  Brian Bino Sinaice²  Youhei Kawamura³  Narihiro Owada⁴ 

[1] Department of Geology, University of Botswana, Private Bag UB 0022, Gaborone, Botswana;Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, Japan;Division of Sustainable Resources Engineering, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, Japan;Technical Division, Faculty of International Resource Sciences, Akita University, Akita 010-8502, Japan;

关键词: hyperspectral imaging;    multispectral imaging;    dimensionality reduction;    neighbourhood component analysis;    artificial intelligence;    machine learning;   

DOI  :  10.3390/min11080846

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown