14.2.8.1 Hyperspectral Mixture Models, Mixed Pixels

Chapter Contents (Back)
Mixed Pixels. Mixture Models. Hyperspectral.
See also Hyperspectral Unmixing.

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


Mahendren, S.[Sutharsan], Fernando, T.[Tharindu], Sridharan, S.[Sridha], Moghadam, P.[Peyman], Fookes, C.[Clinton],
Reduction of Feature Contamination for Hyper Spectral Image Classification,
DICTA21(01-08)
IEEE DOI 2201
Head, Digital images, Computational modeling, Neural networks, Convolutional neural networks, Contamination 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 .


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