Spatial Co-Registration and Spectral Concatenation of Panoramic Ground-Based Hyperspectral Images

Okyay, U.; Khan, Shuhab D.

doi:10.14358/pers.84.12.781

Cited by 8 publications

(4 citation statements)

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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%

“…Most hyperspectral images have been remotely acquired from airborne sensors and a few satellites and, in the context of the mining industry, common applications of remotelysensed hyperspectral images are mineral exploration (see examples in [8][9][10][11][12]) and the environmental impact [13][14][15]. Currently, close-range hyperspectral images of hand samples and/or drill cores [16][17][18][19], along with ground-based panoramic hyperspectral imaging of semi-vertical outcrops [20][21][22][23][24], are increasingly used as hyperspectral imaging systems become more portable and widespread. Multi-and hyperspectral image systems have also been developed for ore microscopy [25][26][27][28] with the aim of achieving a quantitative mineralogical analysis.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…The use of two hyperspectral cameras to cover both visible-near-infrared (VNIR) and SWIR spectra brings the challenge of image fusion in panoramic push-broom operation. Okyay and Khan [ 25 ] co-registered images from VNIR and SWIR cameras using the scale-invariant feature transform algorithm, also developing a correction for VNIR spectral information that removes discontinuities between the spectra of each camera. 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.…”

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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“…Most hyperspectral images have been remotely acquired from airborne sensors and a few satellites, and, in the context of the mining industry, common applications of remotely-sensed hyperspectral images are mineral exploration (notably of hydrothermal alteration, see examples in [8][9]) and environmental impact [10] [11]. Currently, close-range hyperspectral images of hand samples and/or drill cores [12] [13] [14] [15], along with ground-based panoramic hyperspectral imaging of semi-vertical outcrops [16] [17] [18] [19] [20], are increasingly used as hyperspectral imaging systems become more portable and widespread. Multi-and hyperspectral image systems have also been developed for ore microscopy [21] [22] [23] [24] with the aim of achieving quantitative mineralogical analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning for Mineral Identification and Ore Estimation From Hyperspectral Imagery in Tin-Tungsten Deposits

Lobo¹,

García²,

Barroso³

et al. 2021

Preprint

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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) 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 – 1780 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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Spatial Co-Registration and Spectral Concatenation of Panoramic Ground-Based Hyperspectral Images

Cited by 8 publications

References 0 publications

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

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?

Machine-Learning for Mineral Identification and Ore Estimation From Hyperspectral Imagery in Tin-Tungsten Deposits

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