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The Use of Hyperspectral Remote Sensing Data in Mineral Exploration

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 8210

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Special Issue Editors

Dr. Himanshu Govil

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Guest Editor

Department of Applied Geology, NIT Raipur, Raipur, India
Interests: radar interferometry technique; PS-INSAR; coal mine subsidence; subsurface SAR penetration
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Dr. Sankaran Rajendran

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Guest Editor

Environmental Science Center, Qatar University, Doha P.O. Box 2713, Qatar
Interests: SAR applications to oil spill; landslides; vegetation and agriculture; lithological and structural mapping and monitoring
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Dr. Prashant K Srivastava

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Guest Editor

Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India
Interests: hyperspectral remote sensing; soft computing
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Dr. George P. Petropoulos

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Guest Editor

Department of Geography, Harokopio University of Athens, Eleftheriou Venizelou 70, Kallithea, 17676 Athens, Greece
Interests: Earth observation; GIS; geospatial data analysis; operational products; land surface models; geoinformation; software tools
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Special Issue Information

Dear Colleagues,

In remote sensing, the spectroscopy combined imaging system has been used to develop the hyperspectral remote sensing technique. The relevant techniques and data are currently being investigated for their potential use by researchers and scientists. This technique produces large data sets consisting of 100 to 200 or more spectral bands—in a narrow bandwidth from 5 to 10 nm—for various applications. For these purposes, pre- and post-processing of data and several new image processing methods and algorithms have been developed. Recently, imaging spectroscopy has been utilized well in physicists’ and chemists’ laboratories, in addition to mining industries. The availability of the Italian spaceborne hyperspectral sensor PRISMA has allowed scientists to make use of the data availability arising as a result in a variety of fields. The data acquired by drone or airborne hyperspectral sensor/ imaging systems are utilized widely for the mapping and monitoring of minerals. It is important to document the recent innovative developments in imaging systems, feature extractions, mapping algorithms, and their significant applications in order to endorse current research interests and promote imaging techniques. In addition to those on Earth, multiple extra-terrestrial missions have also made use of hyperspectral sensors, which are being used to understand the geology and mineralogy of the planets.

This Special Issue aims to compile the application of hyperspectral remote sensing in mineral exploration for terrestrial and extra-terrestrial missions.

The aim of this Special Issue, ‘Spectroscopy and Potential applications of Hyperspectral Imaging to date’, is to document updated research knowledge in spectroscopy, imaging systems, and potential applications for the future development of HIS systems and techniques. The themes this Special Issue aims to cover include:

spectroscopy; algorithms development; data analysis; and applications of drone, airborne, and spaceborne hyperspectral imaging;
HSI camera in industry sectors and development mineral exploration; mining and metal industries; agriculture; soil and water resources mapping and monitoring;
application of soft computing of hyperspectral data in mineral exploration;
comparative analysis of the different hyperspectral missions;
spectroscopy of altered/weathered/clay minerals;
thermal spectroscopy application in mineral identification;

Dr. Himanshu Govil
Dr. Sankaran Rajendran
Dr. Prashant K Srivastava
Dr. George P. Petropoulos
Guest Editors

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Keywords

spectroscopy
hyperspectral imaging (HIS)
algorithm’s development
applications
soft computing
mineral exploration
mining
minerology
sensors

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Research

25 pages, 21973 KiB

Open AccessArticle

Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm

by Nan Lin, Jiawei Fu, Ranzhe Jiang, Genjun Li and Qian Yang

Remote Sens. 2023, 15(15), 3764; https://doi.org/10.3390/rs15153764 - 28 Jul 2023

Cited by 11 | Viewed by 1667

Abstract

Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field. Full article

(This article belongs to the Special Issue The Use of Hyperspectral Remote Sensing Data in Mineral Exploration)

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33 pages, 16596 KiB

Open AccessArticle

Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm

by Neelam Agrawal, Himanshu Govil, Gaurav Mishra, Manika Gupta and Prashant K. Srivastava

Remote Sens. 2023, 15(12), 3133; https://doi.org/10.3390/rs15123133 - 15 Jun 2023

Cited by 7 | Viewed by 1935

Abstract

Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework. Full article

(This article belongs to the Special Issue The Use of Hyperspectral Remote Sensing Data in Mineral Exploration)

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18 pages, 7872 KiB

Open AccessArticle

Can Imaging Spectroscopy Divulge the Process Mechanism of Mineralization? Inferences from the Talc Mineralization, Jahazpur, India

by Hrishikesh Kumar, Desikan Ramakrishnan, Ronak Jain and Himanshu Govil

Remote Sens. 2023, 15(9), 2394; https://doi.org/10.3390/rs15092394 - 3 May 2023

Cited by 4 | Viewed by 2351

Abstract

Talc deposits of Jahazpur, Rajasthan, hosted by dolomite, are one of the largest high-quality talc deposits in India. In the present study, we use AVIRIS-NG datasets to study the link between the spatial pattern of talc mineralization, associated alteration minerals, and iron-oxide enrichment. It is noted that the majority of talc-bearing areas are characterized by the presence of clay minerals, such as an intimate mixture of kaolinite and muscovite, illite, dickite (indicative of phyllic and argillic alteration), and also enhanced iron enrichment. The talc-bearing zones are located adjacent to quartz-rich lithologies, and they are aligned along the Jahazpur thrust. Based on mineralogical and geological evidence, hydrothermal alteration of dolomites by silica and iron-rich fluid is proposed as major factorcontrolling talc mineralization. This study has implications for the identification of prospective zones of talc mineralization using imaging spectroscopy. Full article

(This article belongs to the Special Issue The Use of Hyperspectral Remote Sensing Data in Mineral Exploration)

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