20.7.3.13 Mineral Composition Analysis, Material Composition

Chapter Contents (Back)
Application, Minerals. Minerals. Material. Also:
See also Geologic Mapping, Geology Analysis, Mineralogy, Fault Zones.
See also Geological Analysis, Rocks.
See also Open Pit Mines, Analysis, Detection.

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
BibRef
Earlier:
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
BibRef

Larsen, R.[Rasmus], Hilger, K.B.[Klaus Baggesen],
Probabilistic Generative Modelling,
SCIA03(861-868).
Springer DOI 0310
BibRef

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

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1403
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.
IEEE DOI 1403
L_1 -norm regularization BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1506
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.
IEEE DOI 1601
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
BibRef

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.
IEEE DOI 1603
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
BibRef

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
BibRef

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
BibRef

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,
CVPR17(6940-6949)
IEEE DOI 1711
Cameras, Databases, Image capture, Image recognition, Lighting, Robots BibRef

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.
IEEE DOI 1810
Manifolds, Linear programming, Hyperspectral imaging, Dictionaries, Semantics, Iterative methods, self-supervised BibRef

Lin, J.[Jie], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le], Jiang, T.X.[Tai-Xiang], Zhuang, L.[Lina],
A Tensor Subspace Representation-Based Method for Hyperspectral Image Denoising,
GeoRS(59), No. 9, September 2021, pp. 7739-7757.
IEEE DOI 2109
Tensors, Gaussian noise, Computational modeling, Noise reduction, Minimization, Computational complexity, Hyperspectral imaging, tensor subspace representation (TenSR) 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
BibRef

Zhuang, L.[Lina], Gao, L.R.[Lian-Ru], Zhang, B.[Bing], Fu, X.Y.[Xi-You], Bioucas-Dias, J.M.[José M.],
Hyperspectral Image Denoising and Anomaly Detection Based on Low-Rank and Sparse Representations,
GeoRS(60), 2022, pp. 1-17.
IEEE DOI 2112
Noise reduction, Hyperspectral imaging, Anomaly detection, Correlation, Image resolution, Dictionaries, self-similarity 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.
IEEE DOI 1905
backpropagation, convolutional neural nets, feature extraction, geophysical image processing, hyperspectral imaging, self-supervision BibRef

Cao, Y.[Yun], Wang, Y.B.[Yue-Bin], Peng, J.H.[Jun-Huan], Qiu, C.P.[Chun-Ping], Ding, L.[Lei], Zhu, X.X.[Xiao Xiang],
SDFL-FC: Semisupervised Deep Feature Learning With Feature Consistency for Hyperspectral Image Classification,
GeoRS(59), No. 12, December 2021, pp. 10488-10502.
IEEE DOI 2112
Feature extraction, Image reconstruction, Generative adversarial networks, Training, Optimization, optimization BibRef

Martínez, J.[Julián], Montiel, V.[Violeta], Rey, J.[Javier], Cańadas, 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
BibRef

Huang, H.G.[Hua-Guo],
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
BibRef

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
BibRef

Xu, Y.J.[Yuan-Jin], Chen, J.G.[Jian-Guo], Meng, P.Y.[Peng-Yan],
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.P.[Xing-Ping],
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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1909
Permittivity, Dielectrics, Numerical models, Rocks, Analytical models, Solid modeling, Geometry, numerical simulation BibRef

Xu, K.[Kai], Wang, X.F.[Xiao-Feng], Kong, C.F.[Chun-Fang], Feng, R.[Ruyi], Liu, G.[Gang], Wu, C.L.[Chong-Long],
Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Krówczynska, M.[Malgorzata], Raczko, E.[Edwin], Staniszewska, N.[Natalia], Wilk, E.[Ewa],
Asbestos-Cement Roofing Identification Using Remote Sensing and Convolutional Neural Networks (CNNs),
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002
BibRef

Gately, J.[Jacob], Liang, Y.[Ying], Wright, M.K.[Matthew Kolessar], Banerjee, N.K.[Natasha Kholgade], Banerjee, S.[Sean], Dey, S.[Soumyabrata],
Automatic Material Classification Using Thermal Finger Impression,
MMMod20(I:239-250).
Springer DOI 2003
BibRef

Ducasse, E.[Etienne], Adeline, K.[Karine], Briottet, X.[Xavier], Hohmann, A.[Audrey], Bourguignon, A.[Anne], Grandjean, G.[Gilles],
Montmorillonite Estimation in Clay-Quartz-Calcite Samples from Laboratory SWIR Imaging Spectroscopy: A Comparative Study of Spectral Preprocessings and Unmixing Methods,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Kasmaeeyazdi, S.[Sara], Mandanici, E.[Emanuele], Balomenos, E.[Efthymios], Tinti, F.[Francesco], Bonduŕ, S.[Stefano], Bruno, R.[Roberto],
Mapping of Aluminum Concentration in Bauxite Mining Residues Using Sentinel-2 Imagery,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Yousefi, B.[Bardia], Ibarra-Castanedo, C.[Clemente], Chamberland, M.[Martin], Maldague, X.P.V.[Xavier P. V.], Beaudoin, G.[Georges],
Unsupervised Identification of Targeted Spectra Applying Rank1-NMF and FCC Algorithms in Long-Wave Hyperspectral Infrared Imagery,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
FCC: False Color Composites. BibRef

Acosta, I.C.C.[Isabel Cecilia Contreras], Khodadadzadeh, M.[Mahdi], Gloaguen, R.[Richard],
Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef
Earlier:
Multi-label Classification for Drill-core Hyperspectral Mineral Mapping,
ISPRS20(B3:383-388).
DOI Link 2012
BibRef

Duan, P.[Puhong], Lai, J.[Jibao], Ghamisi, P.[Pedram], Kang, X.D.[Xu-Dong], Jackisch, R.[Robert], Kang, J.[Jian], Gloaguen, R.[Richard],
Component Decomposition-Based Hyperspectral Resolution Enhancement for Mineral Mapping,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Lin, C.H.[Chuen-Horng], Wang, T.Y.[Ting-You],
A novel convolutional neural network architecture of multispectral remote sensing images for automatic material classification,
SP:IC(97), 2021, pp. 116329.
Elsevier DOI 2107
Terrain reconstruction, Remote sensing image, Multispectral image, Convolutional neural network, Material classification BibRef

Wu, M.J.[Meng-Juan], Wang, J.L.[Jin-Lin], Wang, Q.[Quan], Zhou, K.[Kefa], Zhang, Z.X.[Zhi-Xin], Ma, X.M.[Xiu-Mei], Chen, W.T.[Wei-Tao],
Retrieval of Particle Size of Natural Granite From Multiangular Bidirectional Reflectance Spectra Using the Hapke Model (June 2020),
GeoRS(59), No. 8, August 2021, pp. 6537-6548.
IEEE DOI 2108
Minerals, Atmospheric measurements, Particle measurements, Atmospheric modeling, Scattering, Mathematical model, Rocks, the single-scattering albedo (SSA) BibRef

Zhou, P.[Ping], Zhao, Z.[Zhe], Huo, H.Y.[Hong-Yuan], Liu, Z.[Zhansheng],
Retrieval of Photometric Parameters of Minerals Using a Self-Made Multi-Angle Spectrometer Based on the Hapke Radiative Transfer Model,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Liu, C.Q.[Chang-Qing], Ling, Z.C.[Zong-Cheng], Zhang, J.[Jiang], Wu, Z.C.[Zhong-Chen], Bai, H.C.[Hong-Chun], Liu, Y.H.[Yi-Heng],
A Stand-Off Laser-Induced Breakdown Spectroscopy (LIBS) System Applicable for Martian Rocks Studies,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Fu, L.[Lan], Yu, H.K.[Hong-Kai], Li, X.G.[Xiao-Guang], Przybyla, C.P.[Craig P.], Wang, S.[Song],
Deep Learning for Object Detection in Materials-Science Images: A Tutorial,
SPMag(39), No. 1, January 2022, pp. 78-88.
IEEE DOI 2201
Deep learning, Training data, Tutorials, Microscopy, Materials science, Object detection BibRef

Xue, J.[Jia], Zhang, H.[Hang], Nishino, K.[Ko], Dana, K.J.[Kristin J.],
Differential Viewpoints for Ground Terrain Material Recognition,
PAMI(44), No. 3, March 2022, pp. 1205-1218.
IEEE DOI 2202
Databases, Image recognition, Cameras, Robots, Lighting, Image capture, Material recognition, deep convolutional neural networks, robot navigation BibRef

Song, X.Y.[Xiao-Ying], Chai, L.[Li], Zhang, J.X.[Jing-Xin],
Graph Signal Processing Approach to QSAR/QSPR Model Learning of Compounds,
PAMI(44), No. 4, April 2022, pp. 1963-1973.
IEEE DOI 2203
Compounds, Analytical models, Mathematical model, Biological system modeling, Chemicals, Predictive models, Indexes, multidimensional signal BibRef

Persico, R.[Raffaele], Farhat, I.[Iman], Farrugia, L.[Lourdes], Sammut, C.[Charles],
A Numerical Investigation of the Dispersion Law of Materials by Means of Multi-Length TDR Data,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Qin, L.[Lang], Wu, X.[Xing], Huang, L.Y.[Li-Ying], Liu, Y.[Yang], Zou, Y.L.[Yong-Liao],
Spectroscopic and Petrographic Investigations of Lunar Mg-Suite Meteorite Northwest Africa 8687,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Jin, G.B.[Guo-Bin], Wu, Z.C.[Zhong-Chen], Ling, Z.C.[Zong-Cheng], Liu, C.Q.[Chang-Qing], Liu, W.[Wang], Chen, W.X.[Wen-Xi], Zhang, L.[Li],
A New Spectral Transformation Approach and Quantitative Analysis for MarSCoDe Laser-Induced Breakdown Spectroscopy (LIBS) Data,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Sathyaseelan, C.[Chakkarai], Patro, L.P.P.[L Ponoop Prasad], Rathinavelan, T.[Thenmalarchelvi],
Sequence patterns and HMM profiles to predict proteome wide zinc finger motifs,
PR(135), 2023, pp. 109134.
Elsevier DOI 2212
Zinc finger classification, Zinc finger motif, Zinc finger proteome, Pfam HMM profile, Zinc finger prediction BibRef

Alpers, A.[Andreas], Fiedler, M.[Maximilian], Gritzmann, P.[Peter], Klemm, F.[Fabian],
Turning Grain Maps into Diagrams,
SIIMS(16), No. 1, 2023, pp. 223-249.
DOI Link 2302
BibRef

Yang, X.[Xu], Chen, J.G.[Jian-Guo], Chen, Z.J.[Zhi-Jun],
Classification of Alteration Zones Based on Drill Core Hyperspectral Data Using Semi-Supervised Adversarial Autoencoder: A Case Study in Pulang Porphyry Copper Deposit, China,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Li, N.[Na], Gong, C.G.[Chen-Geng], Zhao, H.J.[Hui-Jie], Ma, Y.[Yun],
Space Target Material Identification Based on Graph Convolutional Neural Network,
RS(15), No. 7, 2023, pp. 1937.
DOI Link 2304
BibRef

Kouremadas, G.[Georgios], Christodoulakis, J.[John], Varotsos, C.[Costas], Xue, Y.[Yong],
Satellite Sensed Data-Dose Response Functions: A Totally New Approach for Estimating Materials' Deterioration from Space,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
BibRef

Yu, Y.[Yan], Yao, M.[Meibao],
When Convolutional Neural Networks Meet Laser-Induced Breakdown Spectroscopy: End-to-End Quantitative Analysis Modeling of ChemCam Spectral Data for Major Elements Based on Ensemble Convolutional Neural Networks,
RS(15), No. 13, 2023, pp. 3422.
DOI Link 2307
BibRef

Liu, Z.[Ziyi], Li, L.[Luning], Xu, W.M.[Wei-Ming], Xu, X.[Xuesen], Cui, Z.C.[Zhi-Cheng], Jia, L.C.[Liang-Chen], Lv, W.H.[Wen-Hao], Shen, Z.H.[Zhi-Hui], Shu, R.[Rong],
Investigation into the Affect of Chemometrics and Spectral Data Preprocessing Approaches upon Laser-Induced Breakdown Spectroscopy Quantification Accuracy Based on MarSCoDe Laboratory Model and MarSDEEP Equipment,
RS(15), No. 13, 2023, pp. 3311.
DOI Link 2307
BibRef

Guo, S.[Senmiao], Jiang, Q.G.[Qi-Gang],
Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data,
RS(15), No. 22, 2023, pp. 5334.
DOI Link 2311
BibRef


Drehwald, M.S.[Manuel S.], Eppel, S.[Sagi], Li, J.[Jolina], Hao, H.[Han], Aspuru-Guzik, A.[Alan],
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering,
ICCV23(23467-23476)
IEEE DOI Code:
WWW Link. 2401
BibRef

Pace, C.D.[Cesare Davide], Bria, A.[Alessandro], Focareta, M.[Mariano], Lozupone, G.[Gabriele], Marrocco, C.[Claudio], Meoli, G.[Giuseppe], Molinara, M.[Mario],
End-to-end Asbestos Roof Detection on Orthophotos Using Transformer-based Yolo Deep Neural Network,
CIAP23(I:232-244).
Springer DOI 2312
BibRef

Dashpute, A.[Aniket], Saragadam, V.[Vishwanath], Alexander, E.[Emma], Willomitzer, F.[Florian], Katsaggelos, A.[Aggelos], Veeraraghavan, A.[Ashok], Cossairt, O.[Oliver],
Thermal Spread Functions (TSF): Physics-Guided Material Classification,
CVPR23(1641-1650)
IEEE DOI 2309
BibRef

Rodriguez-Pardo, C.[Carlos], Dominguez-Elvira, H.[Henar], Pascual-Hernandez, D.[David], Garces, E.[Elena],
UMat: Uncertainty-Aware Single Image High Resolution Material Capture,
CVPR23(5764-5774)
IEEE DOI 2309
BibRef

Chhipa, P.C.[Prakash Chandra], Upadhyay, R.[Richa], Saini, R.[Rajkumar], Lindqvist, L.[Lars], Nordenskjold, R.[Richard], Uchida, S.[Seiichi], Liwicki, M.[Marcus],
Depth Contrast: Self-supervised Pretraining on 3dpm Images for Mining Material Classification,
CVCivil22(212-227).
Springer DOI 2304
BibRef

Qiao, D.[Dexin], Zhang, X.Y.[Xiao-Yu], Ren, Y.[Yili], Liang, J.[Jia],
Comparison of the Rock Core Image Segmentation Algorithm,
ICIVC22(335-339)
IEEE DOI 2301
Geological data in oil field exploration. Image segmentation, Oils, Image edge detection, Semantics, Rocks, Reservoirs, Inference algorithms, image segmentation, K-means algorithm BibRef

Li, Y.C.[Yu-Chen], Upadhyay, U.[Ujjwal], Slim, H.[Habib], Abdelreheem, A.[Ahmed], Prajapati, A.[Arpit], Pothigara, S.[Suhail], Wonka, P.[Peter], Elhoseiny, M.[Mohamed],
3D CoMPaT: Composition of Materials on Parts of 3D Things,
ECCV22(VIII:110-127).
Springer DOI 2211
BibRef

Yao, Y.[Yao], Zhang, J.Y.[Jing-Yang], Liu, J.B.[Jing-Bo], Qu, Y.H.[Yi-Hang], Fang, T.[Tian], McKinnon, D.[David], Tsin, Y.H.[Yang-Hai], Quan, L.[Long],
NeILF: Neural Incident Light Field for Physically-based Material Estimation,
ECCV22(XXXI:700-716).
Springer DOI 2211
BibRef

Velasco-Mata, A.[Alberto], Vallez, N.[Noelia], Ruiz-Santaquiteria, J.[Jesus], Pedraza, A.[Anibal], Deniz, O.[Oscar], Bueno, G.[Gloria],
Hyperdeep: Comparison of AI-Based Methods for Predicting Chemical Components in Hyperspectral Images,
ICIP22(4287-4291)
IEEE DOI 2211
Support vector machines, Parameter estimation, Neural networks, Data preprocessing, Estimation, Production, Hyperspectral, support vector regression BibRef

Liang, Y.P.[Yu-Peng], Wakaki, R.[Ryosuke], Nobuhara, S.[Shohei], Nishino, K.[Ko],
Multimodal Material Segmentation,
CVPR22(19768-19776)
IEEE DOI 2210
Photography, Image segmentation, Visualization, Shape, Semantics, Information filters, Computational photography, grouping and shape analysis BibRef

Zenati, T.[Tarek], Figliuzzi, B.[Bruno], Ham, S.H.[Shu Hui],
Surface Oxide Detection and Characterization Using Sparse Unmixing on Hyperspectral Images,
ISHAPE22(291-302).
Springer DOI 2208
BibRef

Otani, H.[Haru], Komuro, T.[Takashi],
BRDF Measurement of Real Materials Using Handheld Cameras,
ISVC21(I:65-77).
Springer DOI 2112
BibRef

Shi, F.M.[Feng-Min], Guo, J.[Jie], Zhang, H.[Haonan], Yang, S.[Shan], Wang, X.[Xiying], Guo, Y.[Yanwen],
GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition,
CVPR21(14428-14437)
IEEE DOI 2111
Geometry, Deep learning, Visualization, Feature extraction, Pattern recognition, Task analysis BibRef

Brorsson, A.[Andreas], Nordberg, M.[Markus], Gustafsson, D.[David],
Reconstruction of CASSI-Raman Images with Machine-Learning,
PBVS21(4383-4390)
IEEE DOI 2109
Raman spectroscopy. Training, Surface reconstruction, TV, Reconstruction algorithms, Time measurement, Convolutional neural networks, Spatial resolution BibRef

Noh, D.[Donghun], Nam, H.W.[Hyun-Woo], Ahn, M.S.[Min Sung], Chae, H.[Hosik], Lee, S.J.[Sang-Joon], Gillespie, K.[Kyle], Hong, D.[Dennis],
Surface Material Dataset for Robotics Applications (SMDRA): A Dataset with Friction Coefficient and RGB-D for Surface Segmentation,
ICPR21(6275-6281)
IEEE DOI 2105
Legged locomotion, Training, Image segmentation, Friction, Neural networks, Color BibRef

Lim, S.[Sangrak], Lee, Y.O.[Yong Oh],
Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES Representation,
ICPR21(3146-3153)
IEEE DOI 2105
Uniform resource locators, Learning systems, Computational modeling, Predictive models BibRef

Wang, Y.L.[Yun-Long], Zhang, K.[Kunbo], Sun, Z.A.[Zhen-An],
A Novel Deep-learning Pipeline for Light Field Image Based Material Recognition,
ICPR21(2422-2429)
IEEE DOI 2105
Dimensionality reduction, Image segmentation, Visualization, Image edge detection, Pipelines, Semantics BibRef

Asselin, L.P., Laurendeau, D., Lalonde, J.F.,
Deep SVBRDF Estimation on Real Materials,
3DV20(1157-1166)
IEEE DOI 2102
Estimation, Deep learning, Cameras, Light sources, Training, Rendering (computer graphics), Lighting BibRef

Sixiang, X., Damien, M., Alain, T., Robert, L.,
Confidence-based Local Feature Selection for Material Classification,
IVCNZ20(1-6)
IEEE DOI 2012
Feature extraction, Calibration, Convolutional neural networks, Image classification, Material Classification BibRef

Xie, B.S., Zhou, S.Y., Wu, L.X.,
An Integrated Mineral Spectral Library Using Shared Data For Hyperspectral Remote Sensing and Geological Mapping,
ISPRS20(B5:69-75).
DOI Link 2012
BibRef

Gallwey, J., Yeomans, C., Tonkins, M., Coggan, J., Vogt, D., Eyre, M.,
Using Deep Learning and Hough Transformations to Infer Mineralised Veins From Lidar Data Over Historic Mining Areas,
ISPRS20(B2:1561-1568).
DOI Link 2012
BibRef

Tsunomura, M.[Mari], Shishikura, M.[Masami], Ishii, T.[Toru], Takahashi, R.[Ryo], Tsumura, N.[Norimichi],
Segmentation of Microscopic Image of Colorants Using U-net Based Deep Convolutional Networks for Material Appearance Design,
ICISP20(197-204).
Springer DOI 2009
BibRef

Wang, J., Ma, C., Zhang, Z., Wang, Y., Peng, M., Wan, W., Feng, X., Wang, X., He, X., You, Y.,
Lunar Surface Sampling Feasibility Evaluation Method for Chang'e-5 Mission,
PRSM19(1463-1469).
DOI Link 1912
BibRef

Bo, Z., Wan, W., Liu, C., Di, K., Liu, Z., Peng, M., Wang, Y.,
Coaxiality Calculation Method for Dropping Operation of Lunar Surface Sampling Mission Based on Monocular Vision Using Ellipse and Line Features,
ISPRS20(B3:1099-1104).
DOI Link 2012
BibRef

Alfarrarjeh, A., Trivedi, D., Kim, S.H., Park, H., Huang, C., Shahabi, C.,
Recognizing Material of a Covered Object: A Case Study With Graffiti,
ICIP19(2491-2495)
IEEE DOI 1910
Surface Material, Covered Material Recognition, Material Classification, Graffiti BibRef

Qi, L., Xu, Y., Shang, X., Dong, J.,
Fusing Visual Saliency for Material Recognition,
Cognitive18(2046-20463)
IEEE DOI 1812
Computational modeling, 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),
EuroMed18(I:248-258).
Springer DOI 1811
BibRef

Gürgey, K., Canbolat, S.,
Application of Multivariate Statistical Analysis to Biomarkers In Se-turkey Crude Oils,
GeoAdvances17(63-65).
DOI Link 1805
BibRef

Put, J.[Jeroen], Michiels, N.[Nick],
Material-Specific Chromaticity Priors,
BMVC16(xx-yy).
HTML Version. 1805
BibRef

Blasinski, H., Farrell, J., Wandell, B.,
Designing Illuminant Spectral Power Distributions for Surface Classification,
CVPR17(2682-2691)
IEEE DOI 1711
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,
MVA17(149-152)
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,
WACV17(244-253)
IEEE DOI 1609
Cameras, Context, Lighting, Manifolds, Metals, Shape, Three-dimensional, displays 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,
CVPR16(3503-3511)
IEEE DOI 1612
BibRef

Oyen, D., Lanza, N., Porter, R.,
Discovering compositional trends in Mars rock targets from ChemCam spectroscopy and remote imaging,
AIPR15(1-8)
IEEE DOI 1605
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,
CMTR15(71-78).
Springer DOI 1511
BibRef

Baklanova, O., Shvets, O.,
Cluster analysis methods for recognition of mineral rocks in the mining industry,
IPTA14(1-5)
IEEE DOI 1503
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,
ICCVG14(75-83).
Springer DOI 1410
BibRef

Catakli, A.[Aycan], Mahdi, H.[Hanan], Al-Shukri, H.[Haydar],
Attribute analyses of GPR data for heavy minerals exploration,
AIPR12(1-9)
IEEE DOI 1307
BibRef
Earlier:
Texture analysis of GPR data as a tool for depicting soil mineralogy,
AIPR11(1-8).
IEEE DOI 1204
geophysical prospecting BibRef

Zhou, L.L.[Lin-Li], Hu, G.D.[Guang-Dao],
Mineralization Information Extraction Using ETM Remote Sensing Image,
CISP09(1-3).
IEEE DOI 0910
BibRef

Wang, W.X.[Wei-Xing], Li, L.[Lei],
Pattern Recognition and Computer vision for Mineral Froth,
ICPR06(IV: 622-625).
IEEE DOI 0609
BibRef

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


Last update:Mar 16, 2024 at 20:36:19