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Suen, P.,
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0108
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Thai, B.[Bea],
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Invariant Subpixel Material Identification in Hyperspectral Imagery,
DARPA98(809-814).
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Using a Linear Subspace Approach for Invariant Subpixel Material
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Industrial application for inline material sorting using hyperspectral
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0310
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Merenyi, E.,
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Automated Labeling of Materials in Hyperspectral Imagery,
GeoRS(48), No. 11, November 2010, pp. 4059-4070.
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1011
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Galvao, L.S.[Lenio Soares],
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Relationships between the mineralogical and chemical composition of
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0803
Hyperspectral remote sensing; Tropical soils; AVIRIS; Topography;
Mineral identification
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Mapping the distribution of ferric iron minerals on a vertical mine
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1301
Mining; Iron ore; Remote sensing; Hyperspectral; Derivative analysis;
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Murphy, R.J.,
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Consistency of Measurements of Wavelength Position From Hyperspectral
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1403
Geology
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Schneider, S.[Sven],
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PandRS(98), No. 1, 2014, pp. 145-156.
Elsevier DOI
1411
Hyperspectral
BibRef
Cui, J.,
Li, X.,
Zhao, L.,
Linear Mixture Analysis for Hyperspectral Imagery in the Presence
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GeoRS(51), No. 7, 2013, pp. 4019-4031.
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1307
Convex geometry; endmember extraction;
nonnegative matrix factorization (NMF)
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Chen, J.,
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Honeine, P.,
Nonlinear Estimation of Material Abundances in Hyperspectral Images
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GeoRS(52), No. 5, May 2014, pp. 2654-2665.
IEEE DOI
1403
L_1 -norm regularization
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Hyperspectral fluorescence imaging for mineral classification,
30 July 2015, SPIE Newsroom. DOI:
SPIE(Newsroom), July 30, 2015
DOI Link
1511
A novel approach can be used for industrial sorting and presents
several advantages over conventional hyperspectral imaging techniques.
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Potential of Resolution-Enhanced Hyperspectral Data for Mineral
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1604
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PandRS(124), No. 1, 2017, pp. 106-118.
Elsevier DOI
1702
Artificial neural network
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Hoang, N.T.[Nguyen Tien],
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Transformation of Landsat imagery into pseudo-hyperspectral imagery
by a multiple regression-based model with application to metal
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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
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Wang, Y.B.[Yue-Bin],
Mei, J.[Jie],
Zhang, L.Q.[Li-Qiang],
Zhang, B.[Bing],
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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
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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
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Gao, L.R.[Lian-Ru],
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A New Low-Rank Representation Based Hyperspectral Image Denoising
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1712
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Wang, Y.B.[Yue-Bin],
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Li, X.G.[Xin-Gang],
Self-Supervised Feature Learning with CRF Embedding for Hyperspectral
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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
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Cao, Y.[Yun],
Wang, Y.B.[Yue-Bin],
Peng, J.H.[Jun-Huan],
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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
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Yue, J.[Jun],
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Spectral-Spatial Latent Reconstruction for Open-Set Hyperspectral
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IP(31), 2022, pp. 5227-5241.
IEEE DOI
2208
Feature extraction, Image reconstruction, Training,
Hyperspectral imaging, Calibration, Convolution,
open-set environment
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Tu, B.[Bing],
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Hyperspectral Image Classification via Superpixel Spectral Metrics
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SPLetters(25), No. 10, October 2018, pp. 1520-1524.
IEEE DOI
1810
Training, Measurement, Image segmentation, Entropy,
Hyperspectral imaging, Shape, Hyperspectral image (HSI),
spectral information divergence (SID)
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Tu, B.[Bing],
Li, N.Y.[Nan-Ying],
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Classification of Hyperspectral Images via Weighted Spatial
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JVCIR(56), 2018, pp. 160-166.
Elsevier DOI
1811
Hyperspectral image, Superpixel, Joint sparse representation,
Correlation coefficient
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Fang, L.Y.[Le-Yuan],
Li, S.T.[Shu-Tao],
Duan, W.[Wuhui],
Ren, J.C.[Jin-Chang],
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Classification of Hyperspectral Images by Exploiting Spectral-Spatial
Information of Superpixel via Multiple Kernels,
GeoRS(53), No. 12, December 2015, pp. 6663-6674.
IEEE DOI
1512
geophysical image processing
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Fang, L.Y.[Le-Yuan],
Li, S.T.[Shu-Tao],
Kang, X.D.[Xu-Dong],
Benediktsson, J.A.[Jón Atli],
Spectral-Spatial Hyperspectral Image Classification via Multiscale
Adaptive Sparse Representation,
GeoRS(52), No. 12, December 2014, pp. 7738-7749.
IEEE DOI
1410
geophysical image processing
See also Class-Specific Sparse Multiple Kernel Learning for Spectral-Spatial Hyperspectral Image Classification.
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Duan, P.[Puhong],
Ghamisi, P.[Pedram],
Kang, X.D.[Xu-Dong],
Rasti, B.[Behnood],
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Gloaguen, R.[Richard],
Fusion of Dual Spatial Information for Hyperspectral Image
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GeoRS(59), No. 9, September 2021, pp. 7726-7738.
IEEE DOI
2109
Support vector machines, Smoothing methods, Fuses, Imaging,
Feature extraction, Minerals, Task analysis, Decision fusion,
structural profile (SP)
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Tu, B.[Bing],
Li, N.Y.[Nan-Ying],
Fang, L.Y.[Le-Yuan],
Yang, X.C.[Xian-Chang],
Wu, J.H.[Jian-Hui],
Hyperspectral Image Classification with a Class-Dependent
Spatial-Spectral Mixed Metric,
PRL(123), 2019, pp. 16-22.
Elsevier DOI
1906
Hyperspectral image, Spectral angle mapper,
Spectral information divergence, Joint sparse representation,
Local mean-based nearest neighbors
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Zhang, S.Z.[Shu-Zhen],
Li, S.T.[Shu-Tao],
Fu, W.[Wei],
Fang, L.Y.[Lei-Yuan],
Multiscale Superpixel-Based Sparse Representation for Hyperspectral
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RS(9), No. 2, 2017, pp. xx-yy.
DOI Link
1703
BibRef
Jackisch, R.[Robert],
Madriz, Y.[Yuleika],
Zimmermann, R.[Robert],
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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
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,
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1909
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Sousa, F.J.[Francis J.],
Sousa, D.J.[Daniel J.],
Spatial Patterns of Chemical Weathering at the Basal Tertiary
Nonconformity in California from Multispectral and Hyperspectral
Optical Remote Sensing,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
1911
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Pal, M.[Mahendra],
Rasmussen, T.[Thorkild],
Porwal, A.[Alok],
Optimized Lithological Mapping from Multispectral and Hyperspectral
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DOI Link
2001
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Chung, B.[Baru],
Yu, J.H.[Jae-Hyung],
Wang, L.[Lei],
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Lee, B.H.[Bum Han],
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Lee, S.[Sangin],
Detection of Magnesite and Associated Gangue Minerals using
Hyperspectral Remote Sensing: A Laboratory Approach,
RS(12), No. 8, 2020, pp. xx-yy.
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2004
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Notesco, G.[Gila],
Weksler, S.[Shahar],
Ben-Dor, E.[Eyal],
Application of Hyperspectral Remote Sensing in the Longwave Infrared
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DOI Link
2005
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Lin, H.L.[Hong-Lei],
Lin, Y.T.[Yang-Ting],
Wei, Y.[Yong],
Xu, R.[Rui],
Liu, Y.[Yang],
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Hu, S.[Sen],
Yang, W.[Wei],
He, Z.P.[Zhi-Ping],
Estimation of Noise in the In Situ Hyperspectral Data Acquired by
Chang'E-4 and Its Effects on Spectral Analysis of Regolith,
RS(12), No. 10, 2020, pp. xx-yy.
DOI Link
2006
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Cui, L.J.[Li-Juan],
Dou, Z.G.[Zhi-Guo],
Liu, Z.J.[Zhi-Jun],
Zuo, X.Y.[Xue-Yan],
Lei, Y.R.[Yin-Ru],
Li, J.[Jing],
Zhao, X.S.[Xin-Sheng],
Zhai, X.J.[Xia-Jie],
Pan, X.[Xu],
Li, W.[Wei],
Hyperspectral Inversion of Phragmites Communis Carbon, Nitrogen, and
Phosphorus Stoichiometry Using Three Models,
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DOI Link
2006
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Siebels, K.,
Goïta, K.,
Germain, M.,
Estimation of Mineral Abundance From Hyperspectral Data Using a New
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GeoRS(58), No. 10, October 2020, pp. 6754-6766.
IEEE DOI
2009
Minerals, Mathematical model, Hyperspectral imaging, Data models,
Frequency modulation, Lighting, Hyperspectral, mapping, minerals,
spectral unmixing
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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)
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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
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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
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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
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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
Guha, A.[Arindam],
Ghosh, U.K.[Uday Kumar],
Sinha, J.[Joyasree],
Pour, A.B.[Amin Beiranvand],
Bhaisal, R.[Ratnakar],
Chatterjee, S.[Snehamoy],
Baranval, N.K.[Nikhil Kumar],
Rani, N.[Nisha],
Kumar, K.V.[K. Vinod],
Rao, P.V.N.[Pamaraju V. N.],
Potentials of Airborne Hyperspectral AVIRIS-NG Data in the
Exploration of Base Metal Deposit: A Study in the Parts of Bhilwara,
Rajasthan,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link
2106
BibRef
Huynh, H.H.[Huy Hoa],
Yu, J.[Jaehung],
Wang, L.[Lei],
Kim, N.H.[Nam Hoon],
Lee, B.H.[Bum Han],
Koh, S.M.[Sang-Mo],
Cho, S.[Sehyun],
Pham, T.H.[Trung Hieu],
Integrative 3D Geological Modeling Derived from SWIR Hyperspectral
Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link
2108
BibRef
Lobo, A.[Agustin],
Garcia, E.[Emma],
Barroso, G.[Gisela],
Martí, D.[David],
Fernandez-Turiel, J.L.[Jose-Luis],
Ibáñez-Insa, J.[Jordi],
Machine Learning for Mineral Identification and Ore Estimation from
Hyperspectral Imagery in Tin-Tungsten Deposits: Simulation under
Indoor Conditions,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Pattathal, V.A.[V. Arun],
Sahoo, M.M.[Maitreya Mohan],
Porwal, A.[Alok],
Karnieli, A.[Arnon],
Deep-learning-based latent space encoding for spectral unmixing of
geological materials,
PandRS(183), 2022, pp. 307-320.
Elsevier DOI
2201
Spectral unmixing, Airborne hyperspectral remote sensing,
Deep learning, Denoising, Convolutional neural network,
Geological application
BibRef
Thiele, S.T.[Samuel T.],
Bnoulkacem, Z.[Zakaria],
Lorenz, S.[Sandra],
Bordenave, A.[Aurélien],
Menegoni, N.[Niccolò],
Madriz, Y.[Yuleika],
Dujoncquoy, E.[Emmanuel],
Gloaguen, R.[Richard],
Kenter, J.[Jeroen],
Mineralogical Mapping with Accurately Corrected Shortwave Infrared
Hyperspectral Data Acquired Obliquely from UAVs,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Oh, T.M.[Tae-Min],
Baek, S.[Seungil],
Kong, T.H.[Tae-Hyun],
Koh, S.[Sooyoon],
Ahn, J.H.[Jae-Hun],
Kim, W.[Wonkook],
Hyperspectral Remote Sensing of TiO2 Concentration in Cementitious
Material Based on Machine Learning Approaches,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Ren, Z.L.[Zhong-Liang],
Zhai, Q.P.[Qiu-Ping],
Sun, L.[Lin],
A Novel Method for Hyperspectral Mineral Mapping Based on
Clustering-Matching and Nonnegative Matrix Factorization,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhang, C.[Chuan],
Yi, M.[Min],
Ye, F.[Fawang],
Xu, Q.J.[Qing-Jun],
Li, X.C.[Xin-Chun],
Gan, Q.Q.[Qing-Qing],
Application and Evaluation of Deep Neural Networks for Airborne
Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the
Baiyanghe Uranium Deposit in Northwestern Xinjiang, China,
RS(14), No. 20, 2022, pp. xx-yy.
DOI Link
2211
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
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
Chen, L.[Li],
Sui, X.X.[Xin-Xin],
Liu, R.Y.[Rong-Yuan],
Chen, H.[Hong],
Li, Y.[Yu],
Zhang, X.[Xian],
Chen, H.M.[Hao-Min],
Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote
Sensing Data in Coalbed Methane Enrichment Areas,
RS(15), No. 14, 2023, pp. 3590.
DOI Link
2307
BibRef
Ding, W.Q.[Wen-Qing],
Ding, L.[Lin],
Li, Q.[Qingting],
Li, J.X.[Jin-Xiang],
Zhang, L.Y.[Li-Yun],
Lithium-Rich Pegmatite Detection Integrating High-Resolution and
Hyperspectral Satellite Data in Zhawulong Area, Western Sichuan,
China,
RS(15), No. 16, 2023, pp. 3969.
DOI Link
2309
BibRef
Habashi, J.[Jabar],
Moghadam, H.J.[Hadi Jamshid],
Oskouei, M.M.[Majid Mohammady],
Pour, A.B.[Amin Beiranvand],
Hashim, M.[Mazlan],
PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration
Minerals in Sar-e-Ch¢h-e-Shur Region, Birjand, Iran,
RS(16), No. 7, 2024, pp. 1277.
DOI Link
2404
BibRef
Qasim, M.[Muhammad],
Khan, S.D.[Shuhab D.],
Sisson, V.[Virginia],
Greer, P.[Presley],
Xia, L.[Lin],
Okyay, U.[Unal],
Franco, N.[Nicole],
Identifying Rare Earth Elements Using a Tripod and Drone-Mounted
Hyperspectral Camera: A Case Study of the Mountain Pass Birthday
Stock and Sulphide Queen Mine Pit, California,
RS(16), No. 17, 2024, pp. 3353.
DOI Link
2409
BibRef
Ghadernejad, S.[Saleh],
Esmaeili, K.[Kamran],
Predicting Rock Hardness and Abrasivity Using Hyperspectral Imaging
Data and Random Forest Regressor Model,
RS(16), No. 20, 2024, pp. 3778.
DOI Link
2411
BibRef
Ke, T.[Tian],
Zhong, Y.F.[Yan-Fei],
Song, M.[Mi],
Wang, X.Y.[Xin-Yu],
Zhang, L.P.[Liang-Pei],
Mineral detection based on hyperspectral remote sensing imagery on
Mars: From detection methods to fine mapping,
PandRS(218), 2024, pp. 761-780.
Elsevier DOI Code:
HTML Version.
2412
Martian mineral, Hyperspectral remote sensing,
Detection methods, Spectral unmixing, Mars mapping
BibRef
Rizaldy, A.[Aldino],
Afifi, A.J.[Ahmed Jamal],
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Akbar, S.[Somaieh],
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Applying Knowledge-Based and Data-Driven Methods to Improve Ore Grade
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RS(16), No. 15, 2024, pp. 2823.
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2408
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Gao, L.[Likun],
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From Appearance to Inherence: A Hyperspectral Image Dataset and
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IEEE DOI
2408
Hyperspectral imaging, Surveillance, Image classification,
Feature extraction, Task analysis, Benchmark testing, Labeling,
Convolutional neural networks
BibRef
Ghadernejad, S.[Saleh],
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de Luca, G.[Giandomenico],
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Gioli, B.[Beniamino],
Modica, G.[Giuseppe],
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PRISMA imaging for land covers and surface materials composition in
urban and rural areas adopting multiple endmember spectral mixture
analysis (MESMA),
PandRS(225), 2025, pp. 196-220.
Elsevier DOI
2505
spectral mixture analysis (SMA), Endmemebers selection,
Land cover classification, Satellite hyperspectral, Spatial resolution
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Elhaei, Y.[Yasmin],
Asadzadeh, S.[Saeid],
Mapping the Mineralogical Footprints of Petroleum Microseepage
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2410
Deep learning, Training, Image segmentation, Visualization, Lighting,
Material Segmentation, RGB-D, Hyperspectral Images, LiDAR,
Material Recognition
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Vallez, N.[Noelia],
Ruiz-Santaquiteria, J.[Jesus],
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Deniz, O.[Oscar],
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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
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Zenati, T.[Tarek],
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Surface Oxide Detection and Characterization Using Sparse Unmixing on
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2208
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Generalized Unsupervised Clustering of Hyperspectral Images of
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PBVS21(4289-4298)
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2109
Mars, Pipelines, Memory management, Semisupervised learning,
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Xie, B.S.,
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Martínez Corrales, L.F.,
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1912
The material
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1912
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ICIP16(834-838)
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1610
Hyperspectral imaging
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Aghagolzadeh, M.,
Radha, H.,
Hyperspectral material classification under monochromatic and
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ICIP16(2192-2196)
IEEE DOI
1610
Cameras
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Classification of Roof Materials Using Hyperspectral Data,
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Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Automated Measurement Systems, Close Range Photogrammetry .