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Related Experiment Video

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HIDSAG: Hyperspectral Image Database for Supervised Analysis in Geometallurgy.

Alejandro Ehrenfeld1, Álvaro F Egaña2, Felipe Santibañez-Leal2

  • 1Advanced Laboratory for Geostatistical Supercomputing (ALGES), Advanced Mining Technology Center (AMTC) - Department of Mining Engineering, University of Chile, Santiago, 8370451, Chile. aehrenfeld@alges.cl.

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|March 24, 2023
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Summary

This study presents a hyperspectral dataset from geometallurgical samples, useful for machine learning regression and classification research. The data includes spectral images and mineral characterization, validated with machine learning models.

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Area of Science:

  • Geoscience
  • Data Science
  • Mineralogy

Background:

  • Supervised spectral analysis demands robust response variable characterization and ample data.
  • Hyperspectral imaging provides rich data for mineral sample analysis.

Purpose of the Study:

  • To present a comprehensive hyperspectral dataset for geometallurgical samples.
  • To facilitate general regression and classification research using spectral data.

Main Methods:

  • Scanning mineral samples using SPECIM VNIR and SWIR hyperspectral cameras.
  • Acquiring hyperspectral reflectance images across VNIR (400-1000 nm) and SWIR (900-2500 nm) ranges.
  • Generating RGB images via sensor fusion and providing response variables for machine learning.

Main Results:

  • A validated hyperspectral dataset comprising spectral images and mineral characterization data.
  • Successful validation of all data subsets using machine learning models.

Conclusions:

  • The presented dataset is suitable for diverse regression and classification tasks in spectral data analysis.
  • The dataset's validation confirms its utility for machine learning applications in geometallurgy.