Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV
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, Sp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Minerals |
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| Online Access: | https://www.mdpi.com/2075-163X/12/2/268 |
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| author | Brian Bino Sinaice Narihiro Owada Hajime Ikeda Hisatoshi Toriya Zibisani Bagai Elisha Shemang Tsuyoshi Adachi Youhei Kawamura |
| author_facet | Brian Bino Sinaice Narihiro Owada Hajime Ikeda Hisatoshi Toriya Zibisani Bagai Elisha Shemang Tsuyoshi Adachi Youhei Kawamura |
| author_sort | Brian Bino Sinaice |
| collection | DOAJ |
| description | 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 m<sup>2</sup>, 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. |
| first_indexed | 2024-03-09T21:21:26Z |
| format | Article |
| id | doaj.art-16a16b1a63db4aa3a4c933205e6bb70f |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-09T21:21:26Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-16a16b1a63db4aa3a4c933205e6bb70f2023-11-23T21:19:27ZengMDPI AGMinerals2075-163X2022-02-0112226810.3390/min12020268Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAVBrian Bino Sinaice0Narihiro Owada1Hajime Ikeda2Hisatoshi Toriya3Zibisani Bagai4Elisha Shemang5Tsuyoshi Adachi6Youhei Kawamura7Department of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, JapanFaculty of International Resource Sciences, Technical Division, Akita University, Akita 010-8502, JapanDepartment of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, JapanDepartment of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, JapanDepartment of Geology, University of Botswana, Private Bag UB 0022, Gaborone, BotswanaDepartment of Earth and Environmental Science, Botswana International University of Science and Technology, Private Bag 16, Palapye, BotswanaDepartment of Geosciences, Geotechnology and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita 010-8502, JapanFaculty of Engineering, Division of Sustainable Resources Engineering, Hokkaido University, Hokkaido 060-8628, JapanThe 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 m<sup>2</sup>, 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.https://www.mdpi.com/2075-163X/12/2/268UAVremote sensinghyperspectral imagingmultispectral imagingspectral angle mappingartificial intelligence |
| spellingShingle | Brian Bino Sinaice Narihiro Owada Hajime Ikeda Hisatoshi Toriya Zibisani Bagai Elisha Shemang Tsuyoshi Adachi Youhei Kawamura Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV Minerals UAV remote sensing hyperspectral imaging multispectral imaging spectral angle mapping artificial intelligence |
| title | Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV |
| title_full | Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV |
| title_fullStr | Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV |
| title_full_unstemmed | Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV |
| title_short | Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV |
| title_sort | spectral angle mapping and ai methods applied in automatic identification of placer deposit magnetite using multispectral camera mounted on uav |
| topic | UAV remote sensing hyperspectral imaging multispectral imaging spectral angle mapping artificial intelligence |
| url | https://www.mdpi.com/2075-163X/12/2/268 |
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