Coupling NCA Dimensionality Reduction with Machine Learning in Multispectral Rock Classification Problems
| Autor: | Youhei Kawamura, Brian Bino Sinaice, Zibisani Bagai, Mahdi Saadat, Fumiaki Inagaki, Narihiro Owada, Hisatoshi Toriya |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
hyperspectral imaging Multispectral image Machine learning computer.software_genre Reduction (complexity) Component analysis Feature (machine learning) multispectral imaging neighbourhood component analysis dimensionality reduction business.industry Dimensionality reduction Hyperspectral imaging Geology Spectral bands Geotechnical Engineering and Engineering Geology Mineralogy artificial intelligence machine learning Artificial intelligence business computer Curse of dimensionality QE351-399.2 |
| Zdroj: | Minerals, Vol 11, Iss 846, p 846 (2021) Minerals Volume 11 Issue 8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |