| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-02-23 |
| タイトル |
|
|
タイトル |
Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
hyperspectral imaging |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
multispectral imaging |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
dimensionality reduction |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
neighbourhood component analysis |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
artificial intelligence |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
machine learning |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 作成者 |
Sinaice, BrianBino
Owada, Narihiro
Saadat, Mahdi
Toriya, Hisatoshi
Inagaki, Fumiaki
Bagai, Zibisani
Kawamura, Youhei
|
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
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. |
|
言語 |
en |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 書誌情報 |
en : MINERALS
巻 11,
号 8,
発行日 2021
|
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2075-163X |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 関連情報 |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/min11080846 |
| 権利情報 |
|
|
権利情報 |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
kokuA_2022_14.pdf (8.8 MB)
