Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing
In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual informat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Minerals |
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| Online Access: | https://www.mdpi.com/2075-163X/10/9/809 |
| _version_ | 1797553790497325056 |
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| author | Natsuo Okada Yohei Maekawa Narihiro Owada Kazutoshi Haga Atsushi Shibayama Youhei Kawamura |
| author_facet | Natsuo Okada Yohei Maekawa Narihiro Owada Kazutoshi Haga Atsushi Shibayama Youhei Kawamura |
| author_sort | Natsuo Okada |
| collection | DOAJ |
| description | In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%. |
| first_indexed | 2024-03-10T16:21:39Z |
| format | Article |
| id | doaj.art-87b43fb29db24968b5d1d8bc96a6e8a9 |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-10T16:21:39Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-87b43fb29db24968b5d1d8bc96a6e8a92023-11-20T13:35:56ZengMDPI AGMinerals2075-163X2020-09-0110980910.3390/min10090809Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral ProcessingNatsuo Okada0Yohei Maekawa1Narihiro Owada2Kazutoshi Haga3Atsushi Shibayama4Youhei Kawamura5Graduate School of International Resource Sciences, Akita University, 1-1 Tegata Gakuen-machi, Akita 010-8502, JapanDepartment of Transdisciplinary Science and Engineering, School of Environment and Society, Tokyo Institute of Technology, I4-21, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, JapanFaculty of International Resource Sciences, Akita University, 1-1 Tegata Gakuen-machi, Akita 010-8502, JapanGraduate School of International Resource Sciences, Akita University, 1-1 Tegata Gakuen-machi, Akita 010-8502, JapanGraduate School of International Resource Sciences, Akita University, 1-1 Tegata Gakuen-machi, Akita 010-8502, JapanGraduate School of International Resource Sciences, Akita University, 1-1 Tegata Gakuen-machi, Akita 010-8502, JapanIn mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%.https://www.mdpi.com/2075-163X/10/9/809mineral processingmineral identificationCNNmachine learning |
| spellingShingle | Natsuo Okada Yohei Maekawa Narihiro Owada Kazutoshi Haga Atsushi Shibayama Youhei Kawamura Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing Minerals mineral processing mineral identification CNN machine learning |
| title | Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing |
| title_full | Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing |
| title_fullStr | Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing |
| title_full_unstemmed | Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing |
| title_short | Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing |
| title_sort | automated identification of mineral types and grain size using hyperspectral imaging and deep learning for mineral processing |
| topic | mineral processing mineral identification CNN machine learning |
| url | https://www.mdpi.com/2075-163X/10/9/809 |
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