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Minerals
Special Issues
Application of Deep Learning Approaches in Rocks Hyperspectral Imaging
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Application of Deep Learning Approaches in Rocks Hyperspectral Imaging

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Exploration Methods and Applications".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 3239

Special Issue Editors

Prof. Dr. Youhei Kawamura

E-Mail Website
Guest Editor
Division of Sustainable Resources Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo 060-8628, Japan
Interests: smart mining; mining informatics; future mining
Dr. Hisatoshi Toriya

E-Mail Website
Guest Editor
Department of International Resource Sciences, Akita University, 1-1 Tegatagakuen, Akita 010-8502, Japan
Interests: GAN; SAR; image processing; remote sensing

Special Issue Information

Dear Colleagues,

Spectrum analysis has been used in various fields. In the mining industry, technologies have been developed that utilize the multispectral data obtained from satellites for exploration. On the other hand, in recent years, the technological development of hyperspectral cameras has been remarkable, and it has become possible to easily measure spectra of many wavelengths in the field. While hyperspectral data contain a lot of information, their analysis and interpretation are complicated. Therefore, a methodology for more effectively analyzing hyperspectral data by utilizing deep learning, which is one of the most remarkable artificial intelligence technologies in the field of information engineering, is attracting attention. This Special Issue features research that utilizes this methodology to determine the type and properties of rocks.

Prof. Dr. Youhei Kawamura
Dr. Hisatoshi Toriya
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Minerals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart mining
  • machine learning for mining
  • future mining
  • digital twin for mining
  • rock identification
  • deep learning application for mining

Published Papers (1 paper)

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Research

19 pages, 9479 KiB  
Article
Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV
by Brian Bino Sinaice, Narihiro Owada, Hajime Ikeda, Hisatoshi Toriya, Zibisani Bagai, Elisha Shemang, Tsuyoshi Adachi and Youhei Kawamura
Minerals 2022, 12(2), 268; https://0-doi-org.brum.beds.ac.uk/10.3390/min12020268 - 20 Feb 2022
Cited by 7 | Viewed by 2800
Abstract
The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on [...] Read more.
The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m2, a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices. Full article
(This article belongs to the Special Issue Application of Deep Learning Approaches in Rocks Hyperspectral Imaging)
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