Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning-Reference-Cited by-同舟云学术

Advanced mineralogical classification and concentration estimation in mining with MATLAB-powered hyper-spectral imaging and machine learning

Published:2024-06-06 Issue: Volume: Page:1-21
ISSN:1748-0930
Container-title:International Journal of Mining, Reclamation and Environment
language:en
Short-container-title:International Journal of Mining, Reclamation and Environment

Author:

Okada Natsuo¹^ORCID,Bino Sinaice Brian²,Kim Jaewon³,Nozaki Hiromasa¹,Takizawa Kaito¹,Owada Narihiro⁴,Ohtomo Yoko¹,Kawamura Youhei¹

Affiliation:

1. Division of Sustainable Resources Engineering, Graduate School of Engineering, Hokkaido University, Hokkaido, Japan

2. Center for Regional Revitalization in Research and Education, Akita University, Akita, Japan

3. Rock Tools Group, Sales Division, MMC RYOTEC, Tokyo, Japan

4. Graduate School of International Resource Sciences, Department of Earth Resource Engineering and Environmental Science, Akita University, Akita, Japan

Funder

Japan Society for the Promotion of Science

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17480930.2024.2360743

Reference24 articles.

1. A REVIEW OF HYPERSPECTRAL IMAGING IN CLOSE RANGE APPLICATIONS

2. S. Bauer D. Mann and F.P. León Applicability of hyperspectral fluorescence imaging to mineral sorting 2015. Available at https://api.semanticscholar.org/CorpusID:53682649.

3. Hyperspectral imagery for mineral exploration: comparison of data from two airborne sensors

4. A Survey on Hyperspectral Imaging for Mineral Exploration using Machine Learning Algorithms

5. Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing