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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Engineering / Faculty of Engineering >
Peer-reviewed Journal Articles, etc >
Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems
| Title: | Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems |
| Authors: | Sinaice, Brian Bino Browse this author | | Owada, Narihiro Browse this author | | Saadat, Mahdi Browse this author | | Toriya, Hisatoshi Browse this author | | Inagaki, Fumiaki Browse this author | | Bagai, Zibisani Browse this author | | Kawamura, Youhei Browse this author →KAKEN DB |
| Keywords: | hyperspectral imaging | | multispectral imaging | | dimensionality reduction | | neighbourhood component analysis | | artificial intelligence | | machine learning |
| Issue Date: | Aug-2021 |
| Publisher: | MDPI |
| Journal Title: | Minerals |
| Volume: | 11 |
| Issue: | 8 |
| Start Page: | 846 |
| Publisher DOI: | 10.3390/min11080846 |
| Abstract: | 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. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/82786 |
| Appears in Collections: | 工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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