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Integration of Hyperspectral Imaging and Geometallurgy
Dean N Riley and Isabel F Barton
Department of Mining and Geological Engineering, University of Arizona
ABSTRACT: This talk covers the integration of hyperspectral imaging in geometallurgy. Imaging spectroscopy
(aka hyperspectral imaging) is an important and powerful tool for mineral identification. Effective mineral
identification using imaging spectroscopy is dependent on many factors. A mineral’s absorption features
wavelengths, grain size, and the scale of imaging spectroscopy measurements are important considerations along
with environmental constraints. In addition to scale, data availability, cost, spectral resolution, signal-to-noise
ratio (SNR) impact the data selection process. These considerations lead to image processing, analysis, and
interpretation focused on mineral identification, followed by integration and modeling with other geological,
geochemical, metallurgical, and mineral processing data to satisfy the geometallurgical objective.
INTRODUCTION
Geometallurgy
Geometallurgy is an interdisciplinary field that incorporates
geology and extractive metallurgy at minimum and often
incorporates minerals engineering, geostatistics and many
other subdisciplines depending on the specific questions
being addressed (Dominy, O’Connor et al. 2018, Hunt,
Berry et al. 2019, Barton, Gabriel et al. 2021). Figure 1
illustrates the interdisciplinary nature along with different
materials and properties that may be of interest in the min-
ing life cycle (Dehaine, Michaux et al. 2020).
Imaging Spectroscopy (aka Hyperspectral Imaging)
Imaging Spectroscopy (aka Hyperspectral Imaging) is an
extension of vibrational spectroscopy that was developed in
the late 1970s and early 1980s. Goetz, Vane et al. (1985)
definition of imaging spectroscopy is relevant today with
the wide variety of instruments that are available across a
variety of collection platforms. Vibrational spectroscopy
includes infrared, optical, and Raman spectroscopy across a
range of wavelengths in the electromagnetic spectrum from
0.2–25 mm (Raven and Self 2017). The linkage between
vibrational spectroscopy and hyperspectral imaging is often
overlooked, in that, each pixel in a hyperspectral image is
a vibrational spectroscopic measurement. Figure 2 illus-
trates a general methodology for applying hyperspectral
imaging to geometallurgical applications. There are four
fundamental remote sensing questions that are relevant to
hyperspectral imaging whether the data is collected from a
unmanned aerial vehicle (UAV), aircraft, satellite, or lab-
oratory instrument (Figure 3). Hyperspectral imaging be
broken into four sub-components, material spectroscopy,
radiative transfer, imaging spectrometry, and hyperspectral
data processing (Eismann 2012) (Figure 4). Raman spec-
troscopy is an extension of vibrational spectroscopy that
has gained additional attention in the mining industry with
handheld and laboratory instruments, it’s an active source,
that can make point measurements and spatial measure-
ments respectively.