14.2.8.1 Hyperspectral Mixture Models, Mixed Pixels

Chapter Contents (Back)
Mixed Pixels. Mixture Models. Hyperspectral.

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.[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

Imbiriba, T., Moreira Bermudez, J.C., Richard, C.,
Band Selection for Nonlinear Unmixing of Hyperspectral Images as a Maximal Clique Problem,
IP(26), No. 5, May 2017, pp. 2179-2191.
IEEE DOI 1704
Coherence 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

Veganzones, M.A., Tochon, G.[Guillaume], Dalla-Mura, M.[Mauro], Plaza, A.J., Chanussot, J.[Jocelyn],
Hyperspectral Image Segmentation Using a New Spectral Unmixing-Based Binary Partition Tree Representation,
IP(23), No. 8, August 2014, pp. 3574-3589.
IEEE DOI 1408
BibRef
Earlier:
Hyperspectral Image Segmentation Using a New Spectral Mixture-Based Binary Partition Tree Representation,
ICIP13(245-249)
IEEE DOI 1402
geophysical image processing Hyperspectral imaging See also Hyperspectral Image Representation and Processing With Binary Partition Trees. BibRef

Tochon, G.[Guillaume], Dalla-Mura, M.[Mauro], Chanussot, J.[Jocelyn],
Segmentation of Multimodal Images Based on Hierarchies of Partitions,
ISMM15(241-252).
Springer DOI 1506
See also Context-Adaptive Pansharpening Based on Binary Partition Tree Segmentation. 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.
HTML Version. 1209
BibRef

Cerra, D., Müller, R., Reinartz, P.,
About the Applications of Unmixing-Based Denoising for Hyperspectral Data,
SMPR13(103-106).
HTML Version. 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


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

Tong, L.[Lei], Zhou, J.[Jun], Xu, C.Y.[Cheng-Yuan], Qian, Y.T.[Yun-Tao], Gao, Y.S.[Yong-Sheng],
Soil Biochar Quantification via Hyperspectral Unmixing,
DICTA13(1-8)
IEEE DOI 1402
charcoal BibRef

Peng, H.H.[Hong-Hong], Rao, R.[Raghuveer], Dianat, S.A.[Sohail A.],
Nonnegative matrix factorization with deterministic annealing for unsupervised unmixing of hyperspectral imagery,
ICIP12(2145-2148).
IEEE DOI 1302
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
Mixed Pixels, Unmixing .


Last update:Apr 20, 2019 at 12:32:38