A 2D hyperspectral library of mineral reflectance, from 900 to 2500 nm
Date issued
November 2019
In
Scientific Data
Vol
6
From page
268
To page
275
Reviewed by peer
1
Abstract
Mineral identification using machine learning requires a significant amount of training data. We built a
library of 2D hyperspectral images of minerals. The library contains reflectance images of 130 samples,
of 76 distinct minerals, with more than 3.9 million data points. In order to produce this dataset,
various well-characterized mineral samples were scanned, using a SPECIM Short Wave Infrared (SWIR)
camera, which captures wavelengths from 900 to 2500 nm. Minerals were selected to represent all the
mineral classes and the most common mineral occurrences. For each sample, the following data are
provided: (a) At least one hyperspectral image of the sample, consisting of 256 wavelengths between
900 and 2500 nm. The raw data, the high dynamic range (HDR) image, and the masked HDR image are
provided for each scan (each of them in HDF5 format). (b) A text file describing the sample, providing
supplementary information for the subsequent analysis (c) RGB images (JPEG files) and automated
3D reconstructions (Stanford Triangle PLY files). These data help the user to visualize and understand
specific sample characteristics. 2D hyperspectral images were produced for each mineral, which consist
of many different spectra with high diversity. The scans feature similar spectra than the ones in other
available spectral libraries. An artificial neural network was trained to demonstrate the high quality of
the dataset. This spectral library is mainly aimed at training machine learning algorithms, such as neural
networks, but can be also used as validation data for other types of classification algorithms.
library of 2D hyperspectral images of minerals. The library contains reflectance images of 130 samples,
of 76 distinct minerals, with more than 3.9 million data points. In order to produce this dataset,
various well-characterized mineral samples were scanned, using a SPECIM Short Wave Infrared (SWIR)
camera, which captures wavelengths from 900 to 2500 nm. Minerals were selected to represent all the
mineral classes and the most common mineral occurrences. For each sample, the following data are
provided: (a) At least one hyperspectral image of the sample, consisting of 256 wavelengths between
900 and 2500 nm. The raw data, the high dynamic range (HDR) image, and the masked HDR image are
provided for each scan (each of them in HDF5 format). (b) A text file describing the sample, providing
supplementary information for the subsequent analysis (c) RGB images (JPEG files) and automated
3D reconstructions (Stanford Triangle PLY files). These data help the user to visualize and understand
specific sample characteristics. 2D hyperspectral images were produced for each mineral, which consist
of many different spectra with high diversity. The scans feature similar spectra than the ones in other
available spectral libraries. An artificial neural network was trained to demonstrate the high quality of
the dataset. This spectral library is mainly aimed at training machine learning algorithms, such as neural
networks, but can be also used as validation data for other types of classification algorithms.
Publication type
journal article
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