Extending geometallurgy to the mine scale with hyperspectral imaging: a pilot study using drone- and ground-based scanning

Barton, Isabel F.; Gabriel, Matthew J.; Lyons-Baral, John; Barton, Mark D.; Roberts, Carson B.

doi:10.1007/s42461-021-00404-z

Cited by 20 publications

(19 citation statements)

References 52 publications

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“…A few geological studies have employed UAV hyperspectral SWIR cameras (e.g., [14]), but we suggest that significant potential for the development and innovative application of both methods remains. We hope that our novel processing approach helps to realise this potential, by (1) facilitating accurate corrections in areas of steep terrain (e.g., cliffs or open pit mines) and ( 2) retaining true 3-D information by avoiding the need to project data into a 2-D orthomosaic.…”

Section: Discussionmentioning

confidence: 93%

“…[11]); however, weight limitations present significant challenges for SWIR cameras, which require heavy and energy intensive heat pumps to cool the detector to <−100 • C before spectrally accurate data can be acquired. For this reason, SWIR sensors have only recently become small and lightweight enough for deployment on UAVs [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs

et al. 2021

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While uncrewed aerial vehicles are routinely used as platforms for hyperspectral sensors, their application is mostly confined to nadir imaging orientations. Oblique hyperspectral imaging has been impeded by the absence of robust registration and correction protocols, which are essential to extract accurate information. These corrections are especially important for detecting the typically small spectral features produced by minerals, and for infrared data acquired using pushbroom sensors. The complex movements of unstable platforms (such as UAVs) require rigorous geometric and radiometric corrections, especially in the rugged terrain often encountered for geological applications. In this contribution we propose a novel correction methodology, and associated toolbox, dedicated to the accurate production of hyperspectral data acquired by UAVs, without any restriction concerning view angles or target geometry. We make these codes freely available to the community, and thus hope to trigger an increasing usage of hyperspectral data in Earth sciences, and demonstrate them with the production of, to our knowledge, the first fully corrected oblique SWIR drone-survey. This covers a vertical cliff in the Dolomites (Italy), and allowed us to distinguish distinct calcitic and dolomitic carbonate units, map the qualitative abundance of clay/mica minerals, and thus characterise seismic scale facies architecture.

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Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs

et al. 2021

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“…An initial concern in this study was the fact that the used imaging hyperspectral systems were not covering the spectral range 2000-2500 nm, which is diagnostic for many geological materials (see, e.g., [4,9]). Imaging systems covering a wider spectral range have been used to acquire panoramic images in open pit mines [21][22][23][24]29,[74][75][76]. To mention the most recent, Barton et al [75] mapped different mixtures of carbonates, mica-rich muscovite mica, kaolinite, and gypsum in highwalls and outcrops using a system with 640 bands that integrates two sensors to cover from 400 to 2500 nm.…”

Section: Discussionmentioning

confidence: 99%

“…Imaging systems covering a wider spectral range have been used to acquire panoramic images in open pit mines [21][22][23][24]29,[74][75][76]. To mention the most recent, Barton et al [75] mapped different mixtures of carbonates, mica-rich muscovite mica, kaolinite, and gypsum in highwalls and outcrops using a system with 640 bands that integrates two sensors to cover from 400 to 2500 nm. Thiele et al [76] used an equivalent (albeit much heavier) system to map an open-pit mine face in terms of oxidized materials, massive sulfides, Mg-and Fe-rich chlorite, two sericitic units, and shales.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

Lobo

García²,

Barroso

et al. 2021

Remote Sensing

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This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin–tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin–tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450–950 nm and 950–1650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten–tin mine faces.

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“…Krupnik and Khan [ 26 ], in addition to a review of terrestrial applications, presented three case studies of VNIR and SWIR imaging in mine environments, with spectral feature mapping used to identify minerals. Barton et al [ 27 ] presented a case study of field mapping and classification using spectral angle mapper (SAM) classification with both ground-based and unmanned aerial vehicle (UAV) sensing. In particular, they explored the potential to integrate hyperspectral imaging into existing mining operation and interface with common geological software.…”

Section: Introductionmentioning

confidence: 99%

Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

Choros

Job

Edgar³

et al. 2022

Sensors

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This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional LiDAR, mounted on a pan-tilt head, and a positioning system which determined the spatial location of the resultant hyperspectral data cube. This system was used to obtain scans both in the laboratory and at a gold mine in Western Australia. Samples from this mine site were assayed to determine their gold concentration and were scanned using the hyperspectral apparatus in the laboratory to create a library of labelled reference spectra. This library was used as (i) the reference set for spectral angle mapper classification and (ii) a training set for a convolutional neural network classifier. Both classification approaches were found to classify ore and waste on the scanned face with good accuracy when compared to the mine geological model. Greater resolution on the classification of ore grade quality was compromised by the quality and quantity of training data. The work provides evidence that an excavator-mounted hyperspectral system could be used to guide a human or autonomous excavator operator to selectively dig ore and minimise dilution.

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Extending geometallurgy to the mine scale with hyperspectral imaging: a pilot study using drone- and ground-based scanning

Cited by 20 publications

References 52 publications

Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs

Mineralogical Mapping with Accurately Corrected Shortwave Infrared Hyperspectral Data Acquired Obliquely from UAVs

Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin–Tungsten Deposits: Simulation under Indoor Conditions

Can Hyperspectral Imaging and Neural Network Classification Be Used for Ore Grade Discrimination at the Point of Excavation?

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