Dundar, M.M.,
Landgrebe, D.A.,
A model-based mixture-supervised classification approach in
hyperspectral data analysis,
GeoRS(40), No. 12, December 2002, pp. 2692-2699.
IEEE Top Reference.
0301
BibRef
Madhok, V.,
Landgrebe, D.A.,
A process model for remote sensing data analysis,
GeoRS(40), No. 3, March 2002, pp. 680-686.
IEEE Top Reference.
0206
BibRef
Kuo, B.C.,
Landgrebe, D.A.,
A robust classification procedure based on mixture classifiers and
nonparametric weighted feature extraction,
GeoRS(40), No. 11, November 2002, pp. 2486-2494.
IEEE Top Reference.
0301
BibRef
Dundar, M.M.,
Landgrebe, D.A.,
A Cost-Effective Semisupervised Classifier Approach With Kernels,
GeoRS(42), No. 1, January 2004, pp. 264-270.
IEEE Abstract.
0402
BibRef
Dundar, M.M.,
Landgrebe, D.A.,
Toward an Optimal Supervised Classifier for the Analysis of
Hyperspectral Data,
GeoRS(42), No. 1, January 2004, pp. 271-277.
IEEE Abstract.
0402
BibRef
Ifarraguerri, A.,
Chang, C.I.,
Multispectral and Hyperspectral Image Analysis with Convex Cones,
GeoRS(37), No. 2, March 1999, pp. 756.
IEEE Top Reference.
BibRef
9903
Ifarraguerri, A.,
Chang, C.I.[Chein-I],
Unsupervised Hyperspectral Image Analysis with Projection Pursuit,
GeoRS(38), No. 6, November 2000, pp. 2529-2538.
IEEE Top Reference.
0011
BibRef
Chang, C.I.[Chein-I],
Hyperspectral Imaging:
Techniques for Spectral Detection and Classification,
Plenum2004.
ISBN:0-306-47483-2.
HTML Version.
BibRef
0400
Tu, T.M.[Te-Ming],
Shyu, H.C.[Hsuen-Chyun],
Lee, C.H.[Ching-Hai],
Chang, C.I.[Chein-I],
An oblique subspace projection approach for mixed pixel classification
in hyperspectral images,
PR(32), No. 8, August 1999, pp. 1399-1408.
Elsevier DOI
See also Anomaly detection and classification for hyperspectral imagery.
BibRef
9908
Heinz, D.C.,
Chang, C.I.,
Fully constrained least squares linear spectral mixture analysis method
for material quantification in hyperspectral imagery,
GeoRS(39), No. 3, March 2001, pp. 529-545.
IEEE Top Reference.
0104
See also Anomaly detection and classification for hyperspectral imagery.
BibRef
Chang, C.I.[Chein-I],
Ji, B.,
Weighted Abundance-Constrained Linear Spectral Mixture Analysis,
GeoRS(44), No. 2, February 2006, pp. 378-388.
IEEE DOI
0602
BibRef
Chang, C.I.[Chein-I],
Ji, B.,
Fisher's Linear Spectral Mixture Analysis,
GeoRS(44), No. 8, August 2006, pp. 2292-2304.
IEEE DOI
0608
BibRef
Chang, C.I.[Chein-I],
Adaptive Linear Spectral Mixture Analysis,
GeoRS(55), No. 3, March 2017, pp. 1240-1253.
IEEE DOI
1703
Adaptation models
BibRef
Chang, C.I.[Chein-I],
Chiang, S.S.[Shao-Shan],
Smith, J.A.,
Ginsberg, I.W.,
Linear Spectral Random Mixture Analysis for Hyperspectral Imagery,
GeoRS(40), No. 2, February 2002, pp. 375-392.
IEEE Top Reference.
0205
See also Independent Component Analysis-Based Dimensionality Reduction With Applications in Hyperspectral Image Analysis.
BibRef
McGwire, K.[Kenneth],
Minor, T.[Timothy],
Fenstermaker, L.[Lynn],
Hyperspectral Mixture Modeling for Quantifying Sparse Vegetation Cover
in Arid Environments,
RSE(72), No. 3, 2000, pp. 360-374.
0005
BibRef
Priebe, C.E.[Carey E.],
Marchette, D.J.[David J.],
Adaptive mixtures: Recursive nonparametric pattern recognition,
PR(24), No. 12, 1991, pp. 1197-1209.
Elsevier DOI
0401
BibRef
And:
Futher Analysis of method:
Adaptive mixture density estimation,
PR(26), No. 5, May 1993, pp. 771-785.
Elsevier DOI
0401
BibRef
Marchette, D.J.[David J.],
Priebe, C.E.[Carey E.],
Characterizing the scale dimension of a high-dimensional classification
problem,
PR(36), No. 1, January 2003, pp. 45-60.
Elsevier DOI
0210
Characterize the scale.
BibRef
Mei, S.H.[Shao-Hui],
He, M.Y.[Ming-Yi],
Wang, Z.Y.[Zhi-Yong],
Spatial Purity Based Endmember Extraction for Spectral Mixture Analysis,
GeoRS(48), No. 9, September 2010, pp. 3434-3445.
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.D.[David Dagan],
Unsupervised Spectral Mixture Analysis with Hopfield Neural Network for
hyperspectral images,
ICIP12(2665-2668).
IEEE DOI
1302
BibRef
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,
GeoRS(51), No. 7, 2013, pp. 4019-4031.
IEEE DOI
1307
Convex geometry; endmember extraction;
nonnegative matrix factorization (NMF)
BibRef
Imbiriba, T.,
Moreira Bermudez, J.C.,
Richard, C.,
Tourneret, J.Y.[Jean-Yves],
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember
Estimation in Hyperspectral Images,
IP(25), No. 3, March 2016, pp. 1136-1151.
IEEE DOI
1602
Gaussian processes
BibRef
Shi, C.,
Wang, L.,
Linear Spatial Spectral Mixture Model,
GeoRS(54), No. 6, June 2016, pp. 3599-3611.
IEEE DOI
1606
hyperspectral imaging
BibRef
Samat, A.[Alim],
Li, J.[Jun],
Liu, S.[Sicong],
Du, P.J.[Pei-Jun],
Miao, Z.L.[Ze-Lang],
Luo, J.Q.[Jie-Qiong],
Improved hyperspectral image classification by active learning using
pre-designed mixed pixels,
PR(51), No. 1, 2016, pp. 43-58.
Elsevier DOI
1601
Sample design
BibRef
Greer, J.B.,
Sparse Demixing of Hyperspectral Images,
IP(21), No. 1, January 2012, pp. 219-228.
IEEE DOI
1112
BibRef
Lunga, D.[Dalton],
Ersoy, O.[Okan],
Spherical Stochastic Neighbor Embedding of Hyperspectral Data,
GeoRS(51), No. 2, February 2013, pp. 857-871.
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.,
A Classification Algorithm for Hyperspectral Images Based on
Synergetics Theory,
GeoRS(51), No. 5, May 2013, pp. 2887-2898.
IEEE DOI
1305
BibRef
Earlier:
A Classification Algorithm for Hyperspectral Data Based On Synergetics
Theory,
AnnalsPRS(I-7), No. 2012, pp. 71-76.
DOI Link
1209
BibRef
Cerra, D.,
Müller, R.,
Reinartz, P.,
About the Applications of Unmixing-Based Denoising for Hyperspectral
Data,
SMPR13(103-106).
DOI Link
1311
BibRef
Müller, R.,
Cerra, D.,
Reinartz, P.,
Synergetics Framework for Hyperspectral Image Classification,
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
BibRef
Li, F.[Feng],
Xin, L.[Lei],
Guo, Y.[Yi],
Gao, J.B.[Jun-Bin],
Jia, X.P.[Xiu-Ping],
A Framework of Mixed Sparse Representations for Remote Sensing Images,
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.],
High-dimensional unsupervised classification via parsimonious
contaminated mixtures,
PR(98), 2020, pp. 107031.
Elsevier DOI
1911
EM algorithm, Factor analysis, Mixture models,
Model-based clustering, Heavy-tailed distributions
BibRef
Wei, Y.H.[Yu-Hong],
Tang, Y.[Yang],
McNicholas, P.D.[Paul D.],
Flexible High-Dimensional Unsupervised Learning with Missing Data,
PAMI(42), No. 3, March 2020, pp. 610-621.
IEEE DOI
2002
Analytical models, Computational modeling, Data models,
Unsupervised learning, Covariance matrices,
unsupervised classification
BibRef
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
Guo, J.[Jing],
Guo, Y.[Yu],
Jin, Q.Y.[Qi-Yu],
Ng, M.K.P.[Michael Kwok-Po],
Wang, S.P.[Shu-Ping],
Gaussian Patch Mixture Model Guided Low-Rank Covariance Matrix
Minimization for Image Denoising,
SIIMS(15), No. 4, 2022, pp. 1601-1622.
DOI Link
2211
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
BibRef
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
BibRef
Szlam, A.[Arthur],
Guo, Z.H.[Zhao-Hui],
Osher, S.J.[Stanley J.],
A split Bregman method for non-negative sparsity penalized least
squares with applications to hyperspectral demixing,
ICIP10(1917-1920).
IEEE DOI
1009
BibRef
Shah, C.A.,
Arora, M.K.,
Robila, S.A.,
Varshney, P.K.[Pramod K.],
ICA mixture model based unsupervised classification of hyperspectral
imagery,
AIPR02(29-35).
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
Demixing,
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
0509
BibRef
Gu, Y.F.[Yan-Feng],
Zhang, Y.[Ye],
Unsupervised subspace linear spectral mixture analysis for
hyperspectral images,
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 .