2026-03-09T08:50:10Z https://air.repo.nii.ac.jp/oai

oai:air.repo.nii.ac.jp:00005931 2024-08-22T23:14:27Z 1194:1462:1463

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV Sinaice, BrianBino Owada, Narihiro Ikeda, Hajime Toriya, Hisatoshi Bagai, Zibisani Shemang, Elisha Adachi, Tsuyoshi Kawamura, Youhei open access © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). UAV remote sensing hyperspectral imaging multispectral imaging spectral angle mapping artificial intelligence machine learning deep learning 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(2), 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. MDPI 2022 eng journal article VoR http://hdl.handle.net/10295/00006243 https://air.repo.nii.ac.jp/records/5931 https://doi.org/10.3390/min12020268 MINERALS 12 2 https://air.repo.nii.ac.jp/record/5931/files/kokuA_2022_18.pdf application/pdf 9.7 MB 2023-02-23