Mineral Composition Analysis

Chapter Contents (Back)
Application, Minerals. Minerals. See also Geological Analysis, Rocks.

Pong, T.C., Haralick, R.M., Craig, J.R., Yoon, R.H., Choi, W.Z.,
The Application of Image Analysis Techniques to Mineral Processing,
PRL(2), 1983, pp. 117-123. BibRef 8300

Larsen, R.[Rasmus], Nielsen, A.A.[Allan Aasbjerg], Flesche, H.[Harald],
Sensitivity study of a semi-automatic training set generator,
PRL(21), No. 13-14, December 2000, pp. 1175-1182. 0011
Sensitivity Study of a Semi-automatic Supervised Classifier Applied to Minerals from X-Ray Mapping Images,
SCIA99(Statistical Methods). BibRef

Nielsen, A.A.[Allan A.], Larsen, R.[Rasmus],
Canonical Analysis of Sentinel-1 Radar and Sentinel-2 Optical Data,
SCIA17(II: 147-158).
Springer DOI 1706

Larsen, R.[Rasmus], Hilger, K.B.[Klaus Baggesen],
Probabilistic Generative Modelling,
Springer DOI 0310

Hilger, K.B., Nielsen, A.A., Larsen, R.,
A Scheme for Initial Exploratory Data Analysis of Multivariate Image Data,
SCIA01(O-Tu4A). 0206

Ross, B.J., Fueten, F., Yashkir, D.Y.,
Automatic mineral identification using genetic programming,
MVA(13), No. 2 2001, pp. 61-69.
Springer DOI 0201

Galvao, L.S.[Lenio Soares], Formaggio, A.R.[Antonio Roberto], Couto, E.G.[Eduardo Guimaraes], Roberts, D.A.[Dar A.],
Relationships between the mineralogical and chemical composition of tropical soils and topography from hyperspectral remote sensing data,
PandRS(63), No. 2, March 2008, pp. 259-271.
Elsevier DOI 0803
Hyperspectral remote sensing; Tropical soils; AVIRIS; Topography; Mineral identification BibRef

Zaini, N., van der Meer, F., van der Werff, H.,
Effect of Grain Size and Mineral Mixing on Carbonate Absorption Features in the SWIR and TIR Wavelength Regions,
RS(4), No. 4, April 2012, pp. 987-1003.
DOI Link 1202

van der Werff, H.[Harald], van der Meer, F.[Freek],
Sentinel-2 for Mapping Iron Absorption Feature Parameters,
RS(7), No. 10, 2015, pp. 12635.
DOI Link 1511

Murphy, R.J.[Richard J.], Monteiro, S.T.[Sildomar T.],
Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430-970 nm),
PandRS(75), No. 1, January 2013, pp. 29-39.
Elsevier DOI 1301
Mining; Iron ore; Remote sensing; Hyperspectral; Derivative analysis; Banded iron formation BibRef

de Q. da Silva, A.[Arnaldo], Paradella, W.R.[Waldir R.], Freitas, C.C.[Corina C.], Oliveira, C.G.[Cleber G.],
Evaluation of Digital Classification of Polarimetric SAR Data for Iron-Mineralized Laterites Mapping in the Amazon Region,
RS(5), No. 6, 2013, pp. 3101-3122.
DOI Link 1307

Liu, L.[Lei], Zhou, J.[Jun], Jiang, D.[Dong], Zhuang, D.[Dafang], Mansaray, L.R.[Lamin R.], Zhang, B.[Bing],
Targeting Mineral Resources with Remote Sensing and Field Data in the Xiemisitai Area, West Junggar, Xinjiang, China,
RS(5), No. 7, 2013, pp. 3156-3171.
DOI Link 1307

Murphy, R.J., Schneider, S., Monteiro, S.T.,
Consistency of Measurements of Wavelength Position From Hyperspectral Imagery: Use of the Ferric Iron Crystal Field Absorption at sim900 nm as an Indicator of Mineralogy,
GeoRS(52), No. 5, May 2014, pp. 2843-2857.
Geology BibRef

Chen, J., Richard, C., Honeine, P.,
Nonlinear Estimation of Material Abundances in Hyperspectral Images With L_1-Norm Spatial Regularization,
GeoRS(52), No. 5, May 2014, pp. 2654-2665.
L_1 -norm regularization BibRef

Notesco, G.[Gila], Kopacková, V.[Veronika], Rojík, P.[Petr], Schwartz, G.[Guy], Livne, I.[Ido], Dor, E.B.[Eyal Ben],
Mineral Classification of Land Surface Using Multispectral LWIR and Hyperspectral SWIR Remote-Sensing Data. A Case Study over the Sokolov Lignite Open-Pit Mines, the Czech Republic,
RS(6), No. 8, 2014, pp. 7005-7025.
DOI Link 1410

Mielke, C.[Christian], Boesche, N.K.[Nina Kristine], Rogass, C.[Christian], Kaufmann, H.[Hermann], Gauert, C.[Christoph], de Wit, M.[Maarten],
Spaceborne Mine Waste Mineralogy Monitoring in South Africa, Applications for Modern Push-Broom Missions: Hyperion/OLI and EnMAP/Sentinel-2,
RS(6), No. 8, 2014, pp. 6790-6816.
DOI Link 1410

Schneider, S.[Sven], Murphy, R.J.[Richard J.], Melkumyan, A.[Arman],
Evaluating the performance of a new classifier: the GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery,
PandRS(98), No. 1, 2014, pp. 145-156.
Elsevier DOI 1411
Hyperspectral BibRef

Huo, H.Y.[Hong-Yuan], Ni, Z.[Zhuoya], Jiang, X.G.[Xiao-Guang], Zhou, P.[Ping], Liu, L.[Liang],
Mineral Mapping and Ore Prospecting with HyMap Data over Eastern Tien Shan, Xinjiang Uyghur Autonomous Region,
RS(6), No. 12, 2014, pp. 11829-11851.
DOI Link 1412

Cochrane, C.J., Blacksberg, J.,
A Fast Classification Scheme in Raman Spectroscopy for the Identification of Mineral Mixtures Using a Large Database With Correlated Predictors,
GeoRS(53), No. 8, August 2015, pp. 4259-4274.
Raman spectra BibRef

Wang, D., Bischof, L., Lagerstrom, R., Hilsenstein, V., Hornabrook, A., Hornabrook, G.,
Automated Opal Grading by Imaging and Statistical Learning,
SMCS(46), No. 2, February 2016, pp. 185-201.
Ash BibRef

Schreiner, S.[Simon], Buddenbaum, H.[Henning], Emmerling, C.[Christoph], Steffens, M.[Markus],
VNIR/SWIR Laboratory Imaging Spectroscopy for Wall-to-Wall Mapping of Elemental Concentrations in Soil Cores,
PFG(2015), No. 6, 2015, pp. 423-435.
DOI Link 1601

Hecker, C., Riley, D., van der Meijde, M., van der Meer, F.D.,
Noise Simulation and Correction in Synthetic Airborne TIR Data for Mineral Quantification,
GeoRS(54), No. 3, March 2016, pp. 1545-1553.
Data models BibRef

Mielke, C.[Christian], Rogass, C.[Christian], Boesche, N.[Nina], Segl, K.[Karl], Altenberger, U.[Uwe],
EnGeoMAP 2.0: Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission,
RS(8), No. 2, 2016, pp. 127.
DOI Link 1603

Price, M.A.[Mark A.], Ramsey, M.S.[Michael S.], Crown, D.A.[David A.],
Satellite-Based Thermophysical Analysis of Volcaniclastic Deposits: A Terrestrial Analog for Mantled Lava Flows on Mars,
RS(8), No. 2, 2016, pp. 152.
DOI Link 1603
With IR data. BibRef

Yokoya, N.[Naoto], Chan, J.C.W.[Jonathan Cheung-Wai], Segl, K.[Karl],
Potential of Resolution-Enhanced Hyperspectral Data for Mineral Mapping Using Simulated EnMAP and Sentinel-2 Images,
RS(8), No. 3, 2016, pp. 172.
DOI Link 1604

Aligholi, S.[Saeed], Lashkaripour, G.R.[Gholam Reza], Khajavi, R.[Reza], Razmara, M.[Morteza],
Automatic mineral identification using color tracking,
PR(65), No. 1, 2017, pp. 164-174.
Elsevier DOI 1702
Automated mineral identification BibRef

Adep, R.N.[Ramesh Nityanand], shetty, A.[Amba], Ramesh, H.,
EXhype: A tool for mineral classification using hyperspectral data,
PandRS(124), No. 1, 2017, pp. 106-118.
Elsevier DOI 1702
Artificial neural network BibRef

Liu, H.J.[Hua-Jian], Lee, S.H.[Sang-Heon], Chahl, J.S.[Javaan Singh],
Transformation of a high-dimensional color space for material classification,
JOSA-A(34), No. 4, April 2017, pp. 523-532.
DOI Link 1704
Image processing; Machine vision; Remote sensing and sensors BibRef

Kopacková, V.[Veronika], Koucká, L.[Lucie],
Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711

Hoang, N.T.[Nguyen Tien], Koike, K.[Katsuaki],
Transformation of Landsat imagery into pseudo-hyperspectral imagery by a multiple regression-based model with application to metal deposit-related minerals mapping,
PandRS(133), No. Supplement C, 2017, pp. 157-173.
Elsevier DOI 1711
Pseudo-band reflectance, Multiple linear regression, Bayesian model averaging, Hyperion image, Landsat ETM+ image, Cuprite BibRef

Xue, J., Zhang, H., Dana, K., Nishino, K.,
Differential Angular Imaging for Material Recognition,
Cameras, Databases, Image capture, Image recognition, Lighting, Robots BibRef

Gao, L.R.[Lian-Ru], Yao, D.[Dan], Li, Q.T.[Qing-Ting], Zhuang, L.[Lina], Zhang, B.[Bing], Bioucas-Dias, J.M.[José M.],
A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712

Wang, Y.B.[Yue-Bin], Mei, J.[Jie], Zhang, L.Q.[Li-Qiang], Zhang, B.[Bing], Li, A.J.[An-Jian], Zheng, Y.B.[Yi-Bo], Zhu, P.P.[Pan-Pan],
Self-Supervised Low-Rank Representation (SSLRR) for Hyperspectral Image Classification,
GeoRS(56), No. 10, October 2018, pp. 5658-5672.
Manifolds, Linear programming, Hyperspectral imaging, Dictionaries, Semantics, Iterative methods, self-supervised BibRef

Wang, Y.B.[Yue-Bin], Mei, J.[Jie], Zhang, L.Q.[Li-Qiang], Zhang, B.[Bing], Zhu, P.P.[Pan-Pan], Li, Y.[Yang], Li, X.G.[Xin-Gang],
Self-Supervised Feature Learning With CRF Embedding for Hyperspectral Image Classification,
GeoRS(57), No. 5, May 2019, pp. 2628-2642.
backpropagation, convolutional neural nets, feature extraction, geophysical image processing, hyperspectral imaging, self-supervision BibRef

Martínez, J.[Julián], Montiel, V.[Violeta], Rey, J.[Javier], Cañadas, F.[Francisco], Vera, P.[Pedro],
Utilization of Integrated Geophysical Techniques to Delineate the Extraction of Mining Bench of Ornamental Rocks (Marble),
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802

Huang, H.[Huaguo],
Accelerated RAPID Model Using Heterogeneous Porous Objects,
RS(10), No. 8, 2018, pp. xx-yy.
DOI Link 1809
3D radiative transfer model. (Porous object: tree crown.) BibRef

Pei, J.[Jie], Wang, L.[Li], Huang, N.[Ni], Geng, J.[Jing], Cao, J.H.[Jian-Hua], Niu, Z.[Zheng],
Analysis of Landsat-8 OLI Imagery for Estimating Exposed Bedrock Fractions in Typical Karst Regions of Southwest China Using a Karst Bare-Rock Index,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Boubanga-Tombet, S.[Stephane], Huot, A.[Alexandrine], Vitins, I.[Iwan], Heuberger, S.[Stefan], Veuve, C.[Christophe], Eisele, A.[Andreas], Hewson, R.[Rob], Guyot, E.[Eric], Marcotte, F.[Frédérick], Chamberland, M.[Martin],
Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Jeong, Y.[Yongsik], Yu, J.[Jaehyung], Wang, L.[Lei], Shin, J.H.[Ji Hye],
Spectral Responses of As and Pb Contamination in Tailings of a Hydrothermal Ore Deposit: A Case Study of Samgwang Mine, South Korea,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812

Xu, Y.[Yuanjin], Chen, J.G.[Jian-Guo], Meng, P.[Pengyan],
Detection of alteration zones using hyperspectral remote sensing data from Dapingliang skarn copper deposit and its surrounding area, Shanshan County, Xinjiang Uygur autonomous region, China,
JVCIR(58), 2019, pp. 67-78.
Elsevier DOI 1901
Hyperspectral remote sensing, Detection of alteration zones, Dapingliang skarn copper deposit, Spectral matching BibRef

Shan, P.F.[Peng-Fei], Lai, X.[Xingping],
Mesoscopic structure PFC~2D model of soil rock mixture based on digital image,
JVCIR(58), 2019, pp. 407-415.
Elsevier DOI 1901
Soil rock mixture, PFC~2D model, Particle flow simulation, Meso mechanical properties BibRef

Shan, P.F.[Peng-Fei], Lai, X.P.[Xing-Ping],
Influence of CT scanning parameters on rock and soil images,
JVCIR(58), 2019, pp. 642-650.
Elsevier DOI 1901
Digital image processing, Relative standard deviation, Parameters, Geotechnical CT image BibRef

Lorenz, S.[Sandra], Beyer, J.[Jan], Fuchs, M.[Margret], Seidel, P.[Peter], Turner, D.[David], Heitmann, J.[Johannes], Gloaguen, R.[Richard],
The Potential of Reflectance and Laser Induced Luminescence Spectroscopy for Near-Field Rare Earth Element Detection in Mineral Exploration,
RS(11), No. 1, 2018, pp. xx-yy.
DOI Link 1901

Huang, S.[Shuang], Chen, S.B.[Sheng-Bo], Zhang, Y.Z.[Yuan-Zhi],
Comparison of altered mineral information extracted from ETM+, ASTER and Hyperion data in Águas Claras iron ore, Brazil,
IET-IPR(13), No. 2, February 2019, pp. 355-364.
DOI Link 1902

Kurata, K.[Kana], Yamaguchi, Y.S.[Yasu-Shi],
Integration and Visualization of Mineralogical and Topographical Information Derived from ASTER and DEM Data,
RS(11), No. 2, 2019, pp. xx-yy.
DOI Link 1902

Xu, Y.J.[Yuan-Jin], Meng, P.Y.[Peng-Yan], Chen, J.G.[Jian-Guo],
Study on clues for gold prospecting in the Maizijing-Shulonggou area, Ningxia Hui autonomous region, China, using ALI, ASTER and WorldView-2 imagery,
JVCIR(60), 2019, pp. 192-205.
Elsevier DOI 1903
Remote sensing, Mineral prospecting, Fracture, Hydrothermal alteration, Gold mineralization, Maizijing-Shulonggou area BibRef

Noori, L.[Lida], Pour, A.B.[Amin Beiranvand], Askari, G.[Ghasem], Taghipour, N.[Nader], Pradhan, B.[Biswajeet], Lee, C.W.[Chang-Wook], Honarmand, M.[Mehdi],
Comparison of Different Algorithms to Map Hydrothermal Alteration Zones Using ASTER Remote Sensing Data for Polymetallic Vein-Type Ore Exploration: Toroud-Chahshirin Magmatic Belt (TCMB), North Iran,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903

Zhou, C.[Chao], Zhang, Y.Z.[Yuan-Zhi], Chen, S.B.[Sheng-Bo], Zhu, B.X.[Bing-Xue],
Analyzing the Magnesium (Mg) Number of Olivine on the Lunar Surface and Its Geological Significance,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907

Zoheir, B.[Basem], Emam, A.[Ashraf], Abdel-Wahed, M.[Mohamed], Soliman, N.[Nehal],
Multispectral and Radar Data for the Setting of Gold Mineralization in the South Eastern Desert, Egypt,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907

Lim, J., Yu, J., Wang, L., Jeong, Y., Shin, J.H.,
Heavy Metal Contamination Index Using Spectral Variables for White Precipitates Induced by Acid Mine Drainage: A Case Study of Soro Creek, South Korea,
GeoRS(57), No. 7, July 2019, pp. 4870-4888.
Contamination, Pollution measurement, Iron, Sediments, Indexes, Minerals, Heavy metal contamination, mineral composition, white precipitate BibRef

Wu, M.J.[Meng-Juan], Zhou, K.[Kefa], Wang, Q.[Quan], Wang, J.L.[Jin-Lin],
Mapping Hydrothermal Zoning Pattern of Porphyry Cu Deposit Using Absorption Feature Parameters Calculated from ASTER Data,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Guo, C., Ling, B., Mavko, G., Liu, R.,
Effect of Microgeometry on Modeling Accuracy of Fluid-Saturated Rock Using Dielectric Permittivity,
GeoRS(57), No. 9, September 2019, pp. 7294-7299.
Permittivity, Dielectrics, Numerical models, Rocks, Analytical models, Solid modeling, Geometry, numerical simulation BibRef

Sun, L.[Lei], Khan, S.[Shuhab], Shabestari, P.[Peter],
Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah,
RS(11), No. 17, 2019, pp. xx-yy.
DOI Link 1909

Zoheir, B.[Basem], El-Wahed, M.A.[Mohamed Abd], Pour, A.B.[Amin Beiranvand], Abdelnasser, A.[Amr],
Orogenic Gold in Transpression and Transtension Zones: Field and Remote Sensing Studies of the Barramiya-Mueilha Sector, Egypt,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Jackisch, R.[Robert], Madriz, Y.[Yuleika], Zimmermann, R.[Robert], Pirttijärvi, M.[Markku], Saartenoja, A.[Ari], Heincke, B.H.[Björn H.], Salmirinne, H.[Heikki], Kujasalo, J.P.[Jukka-Pekka], Andreani, L.[Louis], Gloaguen, R.[Richard],
Drone-Borne Hyperspectral and Magnetic Data Integration: Otanmäki Fe-Ti-V Deposit in Finland,
RS(11), No. 18, 2019, pp. xx-yy.
DOI Link 1909

Qi, L., Xu, Y., Shang, X., Dong, J.,
Fusing Visual Saliency for Material Recognition,
Computational modeling, Computer vision, Visualization, Data models, Task analysis, Training, Pattern recognition BibRef

Badreddine, D.[Dalal], Beck, K.[Kévin], Brunetaud, X.[Xavier], Chaaba, A.[Ali], Al-Mukhtar, M.[Muzahim],
Study of Effectiveness of Treatment by Nanolime of the Altered Calcarenite Stones of the Archeological Site of Volubilis Site (Morocco),
Springer DOI 1811

Gürgey, K., Canbolat, S.,
Application of Multivariate Statistical Analysis to Biomarkers In Se-turkey Crude Oils,
DOI Link 1805

Put, J.[Jeroen], Michiels, N.[Nick],
Material-Specific Chromaticity Priors,
HTML Version. 1805

Blasinski, H., Farrell, J., Wandell, B.,
Designing Illuminant Spectral Power Distributions for Surface Classification,
Algorithm design and analysis, Cameras, Image color analysis, Lighting, Power distribution, Principal, component, analysis BibRef

Patel, A.K., Chatterjee, S., Gorai, A.K.,
Development of online machine vision system using support vector regression (SVR) algorithm for grade prediction of iron ores,
DOI Link 1708
Feature extraction, Image color analysis, Iron, Machine vision, Ores, Support vector machines, Training BibRef

Georgoulis, S., Vanweddingen, V., Proesmans, M., Van Gool, L.J.,
Material Classification under Natural Illumination Using Reflectance Maps,
Cameras, Context, Lighting, Manifolds, Metals, Shape, Three-dimensional, displays BibRef

Zhang, Y.[Yan], Ozay, M., Liu, X.[Xing], Okatani, T.,
Integrating deep features for material recognition,
Benchmark testing, Computational modeling, Employment, Entropy, Feature extraction, Glass, Object, recognition BibRef

Su, S.C.[Shuo-Chen], Heide, F.[Felix], Swanson, R.[Robin], Klein, J.[Jonathan], Callenberg, C.[Clara], Hullin, M.[Matthias], Heidrich, W.[Wolfgang],
Material Classification Using Raw Time-of-Flight Measurements,

Oyen, D., Lanza, N., Porter, R.,
Discovering compositional trends in Mars rock targets from ChemCam spectroscopy and remote imaging,
Mars BibRef

Bianconi, F.[Francesco], Bello, R.[Raquel], Fernández, A.[Antonio], González, E.[Elena],
On Comparing Colour Spaces From a Performance Perspective: Application to Automated Classification of Polished Natural Stones,
Springer DOI 1511

Baklanova, O., Shvets, O.,
Cluster analysis methods for recognition of mineral rocks in the mining industry,
image colour analysis BibRef

Baklanova, O.E.[Olga E.], Shvets, O.Y.[Olga Ya.],
Development of Methods and Algorithms of Reduction for Image Recognition to Assess the Quality of the Mineral Species in the Mining Industry,
Springer DOI 1410

Catakli, A.[Aycan], Mahdi, H.[Hanan], Al-Shukri, H.[Haydar],
Attribute analyses of GPR data for heavy minerals exploration,
Texture analysis of GPR data as a tool for depicting soil mineralogy,
geophysical prospecting BibRef

Zhou, L.L.[Lin-Li], Hu, G.D.[Guang-Dao],
Mineralization Information Extraction Using ETM Remote Sensing Image,

Wang, W.X.[Wei-Xing], Li, L.[Lei],
Pattern Recognition and Computer vision for Mineral Froth,
ICPR06(IV: 622-625).

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Automated Measurement Systems, Close Range Photogrammetry .

Last update:Oct 1, 2019 at 15:23:24