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IEEE Top Reference.
0206
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A Cost-Effective Semisupervised Classifier Approach With Kernels,
GeoRS(42), No. 1, January 2004, pp. 264-270.
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0402
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Dundar, M.M.,
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1703
Adaptation models
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Chang, C.I.[Chein-I],
Chiang, S.S.[Shao-Shan],
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GeoRS(40), No. 2, February 2002, pp. 375-392.
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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
0401
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And:
Futher Analysis of method:
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PR(26), No. 5, May 1993, pp. 771-785.
Elsevier DOI
0401
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Marchette, D.J.[David J.],
Priebe, C.E.[Carey E.],
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PR(36), No. 1, January 2003, pp. 45-60.
Elsevier DOI
0210
Characterize the scale.
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Mei, S.H.[Shao-Hui],
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IEEE DOI
1008
Mixed pixel problem. Exploit spatial context.
BibRef
Mei, S.H.[Shao-Hui],
He, M.Y.[Ming-Yi],
Wang, Z.Y.[Zhi-Yong],
Feng, D.[Dagan],
Unsupervised Spectral Mixture Analysis with Hopfield Neural Network for
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ICIP12(2665-2668).
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1302
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Chang, C.I.,
Xiong, W.,
Liu, W.,
Chang, M.L.,
Wu, C.C.,
Chen, C.C.C.,
Linear Spectral Mixture Analysis Based Approaches to Estimation of
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
of Less Prevalent Materials,
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1307
Convex geometry; endmember extraction;
nonnegative matrix factorization (NMF)
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Imbiriba, T.,
Moreira Bermudez, J.C.,
Richard, C.,
Tourneret, J.Y.[Jean-Yves],
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember
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IP(25), No. 3, March 2016, pp. 1136-1151.
IEEE DOI
1602
Gaussian processes
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Shi, C.,
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1606
hyperspectral imaging
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Samat, A.[Alim],
Li, J.[Jun],
Liu, S.[Sicong],
Du, P.J.[Pei-Jun],
Miao, Z.L.[Ze-Lang],
Luo, J.Q.[Jie-Qiong],
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PR(51), No. 1, 2016, pp. 43-58.
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Sample design
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Greer, J.B.,
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Lunga, D.[Dalton],
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Spherical Stochastic Neighbor Embedding of Hyperspectral Data,
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IEEE DOI
1302
BibRef
Earlier:
Kent mixture model for classification of remote sensing data on
spherical manifolds,
AIPR11(1-7).
IEEE DOI
1204
BibRef
Cerra, D.,
Mueller, R.,
Reinartz, P.,
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GeoRS(51), No. 5, May 2013, pp. 2887-2898.
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1305
BibRef
Earlier:
A Classification Algorithm for Hyperspectral Data Based On Synergetics
Theory,
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HTML Version.
1209
BibRef
Cerra, D.,
Müller, R.,
Reinartz, P.,
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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.
BibRef
Makarau, A.,
Müller, R.,
Palubinskas, G.,
Reinartz, P.,
Hyperspectral Data Classification Using Factor Graphs,
ISPRS12(XXXIX-B7:137-140).
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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GeoRS(55), No. 2, February 2017, pp. 1210-1221.
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
Hyperspectral Images,
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
BibRef
Punzo, A.[Antonio],
Blostein, M.[Martin],
McNicholas, P.D.[Paul D.],
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PR(98), 2020, pp. 107031.
Elsevier DOI
1911
EM algorithm, Factor analysis, Mixture models,
Model-based clustering, Heavy-tailed distributions
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Wei, Y.H.[Yu-Hong],
Tang, Y.[Yang],
McNicholas, P.D.[Paul D.],
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IEEE DOI
2002
Analytical models, Computational modeling, Data models,
Unsupervised learning, Covariance matrices,
unsupervised classification
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Yang, L.,
Xu, L.,
Peng, J.,
Song, Y.,
Wong, A.,
Clausi, D.A.,
Nonlocal Band-Weighted Iterative Spectral Mixture Model for
Hyperspectral Imagery Denoising,
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
Liu, C.Y.,
Ren, H.,
Multiple Reflection Effects In Nonlinear Mixture Model For
Hyperspectral Image Analysis,
ISPRS16(B7: 295-297).
DOI Link
1610
BibRef
Denisova, A.[Anna],
Juravel, Y.[Yuliya],
Myasnikov, V.[Vladislav],
Linear Spectral Mixture Analysis of Hyperspectral Images with
Atmospheric Distortions,
ICCVG16(134-141).
Springer DOI
1611
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Wu, H.[Hao],
Prasad, S.[Saurabh],
Priya, T.[Tanu],
Detecting new classes via infinite warped mixture models for
hyperspectral image analysis,
ICIP14(5027-5031)
IEEE DOI
1502
Adaptation models
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Szlam, A.[Arthur],
Guo, Z.H.[Zhao-Hui],
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A split Bregman method for non-negative sparsity penalized least
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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
Images and Signals with Application to Hyperspectral Image Subpixel
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ICIP06(3237-3240).
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,
IbPRIA05(II:207).
Springer DOI
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Gu, Y.F.[Yan-Feng],
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Unsupervised subspace linear spectral mixture analysis for
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ICIP03(I: 801-804).
IEEE DOI
0312
BibRef
Gu, Y.F.[Yan-Feng],
Zhang, Y.[Ye],
Zhang, J.,
A Kernel Based Nonlinear Subspace Projection Method for Reduction of
Hyperspectral, Image Dimensionality,
ICIP02(II: 357-360).
IEEE DOI
0210
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hyperspectral Unmixing .