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IEEE Top Reference.
0206
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Dundar, M.M.,
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1703
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See also Independent Component Analysis-Based Dimensionality Reduction With Applications in Hyperspectral Image Analysis.
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McGwire, K.[Kenneth],
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Hyperspectral Mixture Modeling for Quantifying Sparse Vegetation Cover
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Elsevier DOI
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PR(36), No. 1, January 2003, pp. 45-60.
Elsevier DOI
0210
Characterize the scale.
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1008
Mixed pixel problem. Exploit spatial context.
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Mei, S.H.[Shao-Hui],
He, M.Y.[Ming-Yi],
Wang, Z.Y.[Zhi-Yong],
Feng, D.[Dagan],
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Chang, C.I.,
Xiong, W.,
Liu, W.,
Chang, M.L.,
Wu, C.C.,
Chen, C.C.C.,
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Virtual Dimensionality in Hyperspectral Imagery,
GeoRS(48), No. 11, November 2010, pp. 3960-3979.
IEEE DOI
1011
BibRef
Cui, J.,
Li, X.,
Zhao, L.,
Linear Mixture Analysis for Hyperspectral Imagery in the Presence
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1307
Convex geometry; endmember extraction;
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Imbiriba, T.,
Moreira Bermudez, J.C.,
Richard, C.,
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Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember
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IP(25), No. 3, March 2016, pp. 1136-1151.
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1602
Gaussian processes
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Shi, C.,
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1606
hyperspectral imaging
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Sample design
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Greer, J.B.,
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Lunga, D.[Dalton],
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1302
BibRef
Earlier:
Kent mixture model for classification of remote sensing data on
spherical manifolds,
AIPR11(1-7).
IEEE DOI
1204
BibRef
Cerra, D.,
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GeoRS(51), No. 5, May 2013, pp. 2887-2898.
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Earlier:
A Classification Algorithm for Hyperspectral Data Based On Synergetics
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HTML Version.
1209
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Cerra, D.,
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SMPR13(103-106).
HTML Version.
1311
BibRef
Müller, R.,
Cerra, D.,
Reinartz, P.,
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Hannover13(257-262).
DOI Link
1308
See also Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion.
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Makarau, A.,
Müller, R.,
Palubinskas, G.,
Reinartz, P.,
Hyperspectral Data Classification Using Factor Graphs,
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DOI Link
1209
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Li, F.[Feng],
Xin, L.[Lei],
Guo, Y.[Yi],
Gao, J.B.[Jun-Bin],
Jia, X.P.[Xiu-Ping],
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IEEE DOI
1702
hyperspectral imaging.
combination of subimage of smooth, edges, and point-like components.
BibRef
Ye, H.,
Li, H.,
Yang, B.,
Cao, F.,
Tang, Y.,
A Novel Rank Approximation Method for Mixture Noise Removal of
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GeoRS(57), No. 7, July 2019, pp. 4457-4469.
IEEE DOI
1907
Noise reduction, Gaussian noise, Matrix decomposition,
Hyperspectral imaging, Mathematical model, Iterative methods,
smooth approximation
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Punzo, A.[Antonio],
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Elsevier DOI
1911
EM algorithm, Factor analysis, Mixture models,
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Wei, Y.H.[Yu-Hong],
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2002
Analytical models, Computational modeling, Data models,
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unsupervised classification
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Yang, L.,
Xu, L.,
Peng, J.,
Song, Y.,
Wong, A.,
Clausi, D.A.,
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GeoRS(58), No. 8, August 2020, pp. 5588-5601.
IEEE DOI
2007
Noise reduction, Principal component analysis, Correlation,
Hyperspectral imaging, Noise measurement, Mixture models,
Mahalanobis distance
BibRef
Denisova, A.[Anna],
Juravel, Y.[Yuliya],
Myasnikov, V.[Vladislav],
Linear Spectral Mixture Analysis of Hyperspectral Images with
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Springer DOI
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Wu, H.[Hao],
Prasad, S.[Saurabh],
Priya, T.[Tanu],
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ICIP14(5027-5031)
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1502
Adaptation models
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Szlam, A.[Arthur],
Guo, Z.H.[Zhao-Hui],
Osher, S.J.[Stanley J.],
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ICIP10(1917-1920).
IEEE DOI
1009
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Shah, C.A.,
Arora, M.K.,
Robila, S.A.,
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IEEE DOI
0210
BibRef
Bakir, T.,
Peter, A.M.,
Riley, R.,
Hackett, J.,
Non-Negative Maximum Likelihood ICA for Blind Source Separation of
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IEEE DOI
0610
BibRef
Nascimento, J.M.P.[José M.P.],
Dias, J.M.B.[José M.B.],
Signal Subspace Identification in Hyperspectral Linear Mixtures,
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Gu, Y.F.[Yan-Feng],
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IEEE DOI
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Gu, Y.F.[Yan-Feng],
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ICIP02(II: 357-360).
IEEE DOI
0210
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Unmixing .