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Identification of Alteration Minerals from Unstable Reflectance Spectra Using a Deep Learning Method

1
Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
2
Research Institute of Systems Planning Inc., 18-6 Sakuraoka-cho, Shibuya-ku, Tokyo 150-0031, Japan
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(5), 195; https://doi.org/10.3390/geosciences9050195
Received: 22 February 2019 / Revised: 23 April 2019 / Accepted: 23 April 2019 / Published: 28 April 2019
(This article belongs to the Special Issue Image processing and satellite imagery analysis in environments)
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Abstract

Hydrothermal alteration minerals, which are important as indicators in the exploration of ore deposits, exhibit diagnostic absorption peaks in the short-wavelength infrared region. We propose an approach for the identification of alteration minerals that uses a deep learning method and compare it with conventional identification methods which use numerical calculation. Inexpensive spectrometers often tend to show errors in the wavelength direction, even after wavelength calibration, which causes erroneous mineral identification. In this study, deep learning is applied to extract features from reflectance spectra to remove such errors. Two typical deep learning methods—a convolutional neural network and a multi-layer perceptron—were applied to spectral reflectance data, with and without hull quotient processing, and their accuracy rates and f-values were evaluated. There was an improvement in mineral identification accuracy when hull quotient processing was applied to the learning data. View Full-Text
Keywords: alteration minerals; deep learning; multilayer perceptron; convolutional neural network; hull quotient alteration minerals; deep learning; multilayer perceptron; convolutional neural network; hull quotient
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tanaka, S.; Tsuru, H.; Someno, K.; Yamaguchi, Y. Identification of Alteration Minerals from Unstable Reflectance Spectra Using a Deep Learning Method. Geosciences 2019, 9, 195.

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