High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan

Krupnik, Diana; Khan, Shuhab D.

doi:10.3390/min10110967

Cited by 9 publications

(5 citation statements)

References 60 publications

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“…They applied SVM and the Multi-range Spectral Feature Fitting (SFF) to produce classified images of materials such as limestone, siltstone, shale, hematitic siltstone, calcite, illite, jarosite, iron oxides, and dolomite. They also obtained considerable geological information from mapping the wavelength and depth of absorption features in selected wavelength ranges, which they applied as well later [39] to analyze hyperspectral images of small, laboratory-prepared mixtures of limestone minerals (calcite, dolomite, and chert), with results that were consistent to those provided by point count. The same study reports a successful mapping of mixtures in hyperspectral images of small rock chips by applying machine learning methods and using the spectra of known mixtures as references.…”

Section: Introductionmentioning

confidence: 91%

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: Introductionmentioning

confidence: 91%

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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“…Close-range hyperspectral imagery (HSI) on mineral mapping from rock samples has become popular in the past few years. Several studies on utilizing the availability of more bands compared to the multispectral imagery have proven to be more effective in identifying minerals from the rock samples [1]- [3]. Geologists can take advantage of the higher spectral resolution captured by the hyperspectral sensor.…”

Section: Introductionmentioning

confidence: 99%

Mineral Mapping on Hyperspectral Imageries Using Cohesion-based Self Merging Algorithm

Fakhrurrozi¹,

Qudsi

Noor³

et al. 2022

J. Elektron. dan Telekomun.

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Recently, hybrid clustering algorithms gained much research attention due to better clustering results and are computationally efficient. Hyperspectral image classification studies should be no exception, including mineral mapping. This study aims to tackle the biggest challenge of mapping the mineralogy of drill core samples, which consumes a lot of time. In this paper, we present the investigation using a hybrid clustering algorithm, cohesion-based self-merging (CSM), for mineral mapping to determine the number and location of minerals that formed the rock. The CSM clustering performance was then compared to its classical counterpart, K-means plus-plus (K-means++). We conducted experiments using hyperspectral images from multiple rock samples to understand how well the clustering algorithm segmented minerals that exist in the rock. The samples in this study contain minerals with identical absorption features in certain locations that increase the complexity. The elbow method and silhouette analysis did not perform well in deciding the optimum cluster size due to slight variance and high dimensionality of the datasets. Thus, iterations to the various numbers of k-clusters and m-subclusters of each rock were performed to get the mineral cluster. Both algorithms were able to distinguish slight variations of absorption features of any mineral. The spectral variation within a single mineral found by our algorithm might be studied further to understand any possible unidentified group of clusters. The spatial consideration of the CSM algorithm induced several misclassified pixels. Hence, the mineral maps produced in this study are not expected to be precisely similar to ground truths.

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“…To the former, the main idea of the spectral comparison is to construct a spectral similarity measure to accomplish the lithology classification. For example, spectral angle mapping (SAM) [18,19] and spectral information divergence (SID) [20] are both effective in similarity measurement when the spectral vector is used for direct retrieval. However, this type of method tends to focus on the overall waveform features of the spectrum and ignores some of the detailed features, which makes it difficult to classify similar rock types.…”

Section: Introductionmentioning

confidence: 99%

Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

Liu

et al. 2021

Remote Sensing

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In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

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High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan

Cited by 9 publications

References 60 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

Mineral Mapping on Hyperspectral Imageries Using Cohesion-based Self Merging Algorithm

Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

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