14.2.8.1.1 Hyperspectral Unmixing

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

Nielsen, A.A.[Allan Aasbjerg],
Spectral Mixture Analysis: Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data,
IJCV(42), No. 1-2, April-May 2001, pp. 17-37.
DOI Link 0106
BibRef
And: JMIV(15), No. 1/2, July 2001, pp. 17-37.
DOI Link 0106
BibRef
Earlier:
Linear Mixture Models, Full and Partial Unmixing in Multi- and Hyperspectral Image Data,
SCIA99(Remote Sensing). BibRef

Asner, G.P., Heidebrecht, K.B.,
Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations,
JRS(23), No. 19, October 2002, pp. 3939-3958.
WWW Link. 0211
BibRef

Nascimento, J.M.P.[José M. P.], Bioucas-Dias, J.M.B.[José M.B.],
Does Independent Component Analysis Play a Role in Unmixing Hyperspectral Data?,
GeoRS(43), No. 1, January 2005, pp. 175-187.
IEEE Abstract. 0501
BibRef

Nascimento, J.M.P.[José M. P.], Bioucas-Dias, J.M.B.[José M.B.],
Hyperspectral Unmixing Based on Mixtures of Dirichlet Components,
GeoRS(50), No. 3, March 2012, pp. 863-878.
IEEE DOI 1203
BibRef

Nascimento, J.M.P.[José M. P.], Bioucas-Dias, J.M.B.[José M.B.],
Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data,
GeoRS(43), No. 4, April 2005, pp. 898-910.
IEEE Abstract. 0501
BibRef
And:
Dependent Component Analysis: A Hyperspectral Unmixing Algorithm,
IbPRIA07(II: 612-619).
Springer DOI 0706
BibRef

Jia, S.[Sen], Qian, Y.T.[Yun-Tao],
Spectral and Spatial Complexity-Based Hyperspectral Unmixing,
GeoRS(45), No. 12, December 2007, pp. 3867-3879.
IEEE DOI 0711
BibRef
Earlier:
Improved Stone's Complexity Pursuit for Hyperspectral Imagery Unmixing,
ICPR06(IV: 817-820).
IEEE DOI 0609
BibRef
And:
MRF Based Spatial Complexity for Hyperspectral Imagery Unmixing,
SSPR06(531-540).
Springer DOI 0608
BibRef

Jia, S.[Sen], Qian, Y.T.[Yun-Tao], Ji, Z.[Zhen],
Band Selection for Hyperspectral Imagery Using Affinity Propagation,
DICTA08(137-141).
IEEE DOI 0812
BibRef

Plaza, J.[Javier], Plaza, A.[Antonio], Perez, R.[Rosa], Martinez, P.[Pablo],
On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images,
PR(42), No. 11, November 2009, pp. 3032-3045.
Elsevier DOI 0907
Hyperspectral; Image processing; Mixed pixels; Spectral mixture analysis; Multi-layer perceptron; Automatic training sample generation algorithms; Mixed training samples; Nonlinear spectral unmixing BibRef

Plaza, A.[Antonio], Plaza, J.[Javier],
Impact of Vector Ordering Strategies on Morphological Unmixing of Remotely Sensed Hyperspectral Images,
ICPR10(4412-4415).
IEEE DOI 1008
BibRef

Huck, A., Guillaume, M., Blanc-Talon, J.,
Minimum Dispersion Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data,
GeoRS(48), No. 6, June 2010, pp. 2590-2602.
IEEE DOI 1006
BibRef

Schmidt, F., Schmidt, A., Treguier, E., Guiheneuf, M., Moussaoui, S., Dobigeon, N.,
Implementation Strategies for Hyperspectral Unmixing Using Bayesian Source Separation,
GeoRS(48), No. 11, November 2010, pp. 4003-4013.
IEEE DOI 1011
BibRef

Liu, X., Xia, W., Wang, B., Zhang, L.,
An Approach Based on Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data,
GeoRS(49), No. 2, February 2011, pp. 757-772.
IEEE DOI 1102
BibRef

Xia, W., Liu, X., Wang, B., Zhang, L.,
Independent Component Analysis for Blind Unmixing of Hyperspectral Imagery With Additional Constraints,
GeoRS(49), No. 6, June 2011, pp. 2165-2179.
IEEE DOI 1106
BibRef

Xia, W., Pu, H., Wang, B., Zhang, L.,
Triangular Factorization-Based Simplex Algorithms for Hyperspectral Unmixing,
GeoRS(50), No. 11, November 2012, pp. 4420-4440.
IEEE DOI 1210
BibRef

Pu, H.Y.[Han-Ye], Chen, Z.[Zhao], Wang, B.[Bin], Xia, W.[Wei],
Constrained Least Squares Algorithms for Nonlinear Unmixing of Hyperspectral Imagery,
GeoRS(53), No. 3, March 2015, pp. 1287-1303.
IEEE DOI 1412
deconvolution
See also Novel Spatial-Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery, A. BibRef

Tong, L.[Lei], Zhou, J.[Jun], Qian, Y.T.[Yun-Tao], Bai, X.[Xiao], Gao, Y.,
Nonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers,
GeoRS(54), No. 11, November 2016, pp. 6531-6544.
IEEE DOI 1610
Dictionaries BibRef

Li, X.[Xue], Zhou, J.[Jun], Tong, L.[Lei], Yu, X.[Xun], Guo, J.H.[Jian-Hui], Zhao, C.X.[Chun-Xia],
Structured Discriminative Nonnegative Matrix Factorization for Hyperspectral Unmixing,
ICIP16(1848-1852)
IEEE DOI 1610
Hyperspectral imaging BibRef

Shu, Z.Q.[Zhen-Qiu], Zhou, J.[Jun], Tong, L.[Lei], Bai, X.[Xiao], Zhao, C.X.[Chun-Xia],
Multilayer manifold and sparsity constrained nonnegative matrix factorization for hyperspectral unmixing,
ICIP15(2174-2178)
IEEE DOI 1512
NMF BibRef

Li, X.R.[Xiao-Run], Cui, J.T.[Jian-Tao], Zhao, L.Y.[Liao-Ying],
Blind nonlinear hyperspectral unmixing based on constrained kernel nonnegative matrix factorization,
SIViP(8), No. 8, November 2014, pp. 1555-1567.
WWW Link. 1411
BibRef

Huang, R.S.[Ri-Sheng], Li, X.R.[Xiao-Run], Zhao, L.Y.[Liao-Ying],
Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711
BibRef

Huang, R.S.[Ri-Sheng], Li, X.R.[Xiao-Run], Zhao, L.Y.[Liao-Ying],
Spectral-Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing,
GeoRS(57), No. 10, October 2019, pp. 8235-8254.
IEEE DOI 1910
geophysical image processing, hyperspectral imaging, image denoising, learning (artificial intelligence), robust nonnegative matrix factoriztion BibRef

Huang, R.S.[Ri-Sheng], Li, X.R.[Xiao-Run], Zhao, L.Y.[Liao-Ying],
Hyperspectral Unmixing Based on Incremental Kernel Nonnegative Matrix Factorization,
GeoRS(56), No. 11, November 2018, pp. 6645-6662.
IEEE DOI 1811
Kernel, Hyperspectral imaging, Matrix decomposition, Matrix converters, Memory management, Mixture models, Block matrix, nonlinear spectral unmixing BibRef

Huang, R.S.[Ri-Sheng], Jiao, H.Y.[Hui-Yun], Li, X.R.[Xiao-Run], Chen, S.H.[Shu-Han], Xia, C.Q.[Chao-Qun],
Hyperspectral Unmixing Using Robust Deep Nonnegative Matrix Factorization,
RS(15), No. 11, 2023, pp. 2900.
DOI Link 2306
BibRef

Li, X.R.[Xiao-Run], Huang, R.S.[Ri-Sheng], Zhao, L.Y.[Liao-Ying],
Correntropy-Based Spatial-Spectral Robust Sparsity-Regularized Hyperspectral Unmixing,
GeoRS(59), No. 2, February 2021, pp. 1453-1471.
IEEE DOI 2101
Hyperspectral imaging, Noise measurement, Robustness, Matrix decomposition, Sparse matrices, Adaptation models, spatial-spectral robustness BibRef

Huang, R.S.[Ri-Sheng], Li, X.R.[Xiao-Run], Fang, Y.M.[Yi-Ming], Cao, Z.[Zeyu], Xia, C.Q.[Chao-Qun],
Robust Hyperspectral Unmixing with Practical Learning-Based Hyperspectral Image Denoising,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Qian, Y.T.[Yun-Tao], Jia, S.[Sen], Zhou, J.[Jun], Robles-Kelly, A.[Antonio],
Hyperspectral Unmixing via L_1/2 Sparsity-Constrained Nonnegative Matrix Factorization,
GeoRS(49), No. 11, November 2011, pp. 4282-4297.
IEEE DOI 1112
BibRef
Earlier:
L_1/2 Sparsity Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing,
DICTA10(447-453).
IEEE DOI 1012
BibRef

Li, X.[Xue], Cao, S.[Sifan], Huang, D.[Dan], Zhang, M.[Ming], Li, Y.W.[Yi-Wei],
Global centralised and structured discriminative non-negative matrix factorisation for hyperspectral unmixing,
IET-CV(17), No. 5, 2023, pp. 549-564.
DOI Link 2309
clustering, feature extraction, hyperspectral unmixing, local affinity, non-negative matrix factorisation BibRef

Ceamanos, X., Doute, S., Luo, B.[Bin], Schmidt, F., Jouannic, G., Chanussot, J.,
Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study,
GeoRS(49), No. 11, November 2011, pp. 4341-4358.
IEEE DOI 1112
BibRef

Mianji, F.A., Zhang, Y.[Ye],
SVM-Based Unmixing-to-Classification Conversion for Hyperspectral Abundance Quantification,
GeoRS(49), No. 11, November 2011, pp. 4318-4327.
IEEE DOI 1112
BibRef

Canham, K., Schlamm, A., Ziemann, A., Basener, B., Messinger, D.,
Spatially Adaptive Hyperspectral Unmixing,
GeoRS(49), No. 11, November 2011, pp. 4248-4262.
IEEE DOI 1112
BibRef

Ambikapathi, A., Chan, T., Ma, W.K.[Wing-Kin], Chi, C.Y.[Chong-Yung],
Chance-Constrained Robust Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing,
GeoRS(49), No. 11, November 2011, pp. 4194-4209.
IEEE DOI 1112
BibRef

Licciardi, G.A., del Frate, F.,
Pixel Unmixing in Hyperspectral Data by Means of Neural Networks,
GeoRS(49), No. 11, November 2011, pp. 4163-4172.
IEEE DOI 1112
BibRef

Halimi, A., Altmann, Y., Dobigeon, N., Tourneret, J.Y.[Jean-Yves],
Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model,
GeoRS(49), No. 11, November 2011, pp. 4153-4162.
IEEE DOI 1112
BibRef

Wei, Q., Chen, M., Tourneret, J.Y., Godsill, S.,
Unsupervised Nonlinear Spectral Unmixing Based on a Multilinear Mixing Model,
GeoRS(55), No. 8, August 2017, pp. 4534-4544.
IEEE DOI 1708
Estimation, Hyperspectral imaging, Minerals, Mixture models, Optimization, Block coordinate descent (BCD), gradient projection method, multilinear model, nonlinear, unmixing, (NLU) BibRef

Thouvenin, P.A., Dobigeon, N., Tourneret, J.Y.[Jean-Yves],
Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability,
IP(25), No. 9, September 2016, pp. 3979-3990.
IEEE DOI 1609
approximation theory BibRef

Halimi, A., Dobigeon, N., Tourneret, J.Y.,
Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability,
IP(24), No. 12, December 2015, pp. 4904-4917.
IEEE DOI 1512
Monte Carlo methods BibRef

Altmann, Y., Dobigeon, N., McLaughlin, S., Tourneret, J.Y.,
Residual Component Analysis of Hyperspectral Images: Application to Joint Nonlinear Unmixing and Nonlinearity Detection,
IP(23), No. 5, May 2014, pp. 2148-2158.
IEEE DOI 1405
Gaussian noise BibRef

Altmann, Y., Halimi, A., Dobigeon, N., Tourneret, J.Y.,
Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery,
IP(21), No. 6, June 2012, pp. 3017-3025.
IEEE DOI 1202
BibRef

Altmann, Y., Dobigeon, N., Tourneret, J.Y.,
Nonlinearity Detection in Hyperspectral Images Using a Polynomial Post-Nonlinear Mixing Model,
IP(22), No. 4, April 2013, pp. 1267-1276.
IEEE DOI 1303
BibRef

Altmann, Y., Dobigeon, N., Tourneret, J.Y.,
Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm,
IP(23), No. 6, June 2014, pp. 2663-2675.
IEEE DOI 1406
Approximation algorithms BibRef

Heylen, R., Burazerovic, D., Scheunders, P.,
Fully Constrained Least Squares Spectral Unmixing by Simplex Projection,
GeoRS(49), No. 11, November 2011, pp. 4112-4122.
IEEE DOI 1112
BibRef

Heylen, R.[Rob], Scheunders, P.[Paul],
A Multilinear Mixing Model for Nonlinear Spectral Unmixing,
GeoRS(54), No. 1, January 2016, pp. 240-251.
IEEE DOI 1601
BibRef
And:
Hyperspectral unmixing using an active set algorithm,
ICIP14(694-697)
IEEE DOI 1502
hyperspectral imaging. Algorithm design and analysis BibRef

Li, C., Sun, T., Kelly, K.F., Zhang, Y.,
A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing,
IP(21), No. 3, March 2012, pp. 1200-1210.
IEEE DOI 1203
BibRef

Karathanassi, V., Sykas, D., Topouzelis, K.N.,
Development of a Network-Based Method for Unmixing of Hyperspectral Data,
GeoRS(50), No. 3, March 2012, pp. 839-849.
IEEE DOI 1203
BibRef

Averbuch, A., Zheludev, M.,
Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images,
RS(4), No. 2, February 2012, pp. 532-560.
DOI Link 1203
BibRef

Honeine, P., Richard, C.,
Geometric Unmixing of Large Hyperspectral Images: A Barycentric Coordinate Approach,
GeoRS(50), No. 6, June 2012, pp. 2185-2195.
IEEE DOI 1205
BibRef

Eches, O., Benediktsson, J.A., Dobigeon, N., Tourneret, J.Y.,
Adaptive Markov Random Fields for Joint Unmixing and Segmentation of Hyperspectral Images,
IP(22), No. 1, January 2013, pp. 5-16.
IEEE DOI 1301
BibRef

Lagrange, A., Fauvel, M., May, S., Dobigeon, N.,
Matrix Cofactorization for Joint Spatial-Spectral Unmixing of Hyperspectral Images,
GeoRS(58), No. 7, July 2020, pp. 4915-4927.
IEEE DOI 2006
Hyperspectral imaging, Feature extraction, Task analysis, Matrix decomposition, Couplings, Context modeling, Cofactorization, spectral unmixing BibRef

Luo, B., Yang, C., Chanussot, J., Zhang, L.,
Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery,
GeoRS(51), No. 1, January 2013, pp. 162-173.
IEEE DOI 1301
BibRef

Zare, A.[Alina], Gader, P.D.[Paul D.], Casella, G.,
Sampling Piecewise Convex Unmixing and Endmember Extraction,
GeoRS(51), No. 3, March 2013, pp. 1655-1665.
IEEE DOI 1303
BibRef

Lu, X.Q.[Xiao-Qiang], Wu, H.[Hao], Yuan, Y.[Yuan], Yan, P.K.[Ping-Kun], Li, X.L.[Xue-Long],
Manifold Regularized Sparse NMF for Hyperspectral Unmixing,
GeoRS(51), No. 5, May 2013, pp. 2815-2826.
IEEE DOI 1305
BibRef

Dong, L.[Le], Yuan, Y.[Yuan], Luxs, X.Q.[Xiao-Qiang],
Spectral-Spatial Joint Sparse NMF for Hyperspectral Unmixing,
GeoRS(59), No. 3, March 2021, pp. 2391-2402.
IEEE DOI 2103
Hyperspectral imaging, Sparse matrices, Correlation, Imaging, Dictionaries, Matrix decomposition, sparse expression BibRef

Lu, X.Q.[Xiao-Qiang], Wu, H.[Hao], Yuan, Y.[Yuan],
Double Constrained NMF for Hyperspectral Unmixing,
GeoRS(52), No. 5, May 2014, pp. 2746-2758.
IEEE DOI 1403
Clustering-based regularization BibRef

Yuan, Y.[Yuan], Fu, M.[Min], Lu, X.Q.[Xiao-Qiang],
Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing,
GeoRS(53), No. 6, June 2015, pp. 2975-2986.
IEEE DOI 1503
geophysical image processing
See also Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images. BibRef

Lu, X.Q.[Xiao-Qiang], Dong, L.[Le], Yuan, Y.[Yuan],
Subspace Clustering Constrained Sparse NMF for Hyperspectral Unmixing,
GeoRS(58), No. 5, May 2020, pp. 3007-3019.
IEEE DOI 2005
Hyperspectral unmixing, self-expression, spatial structure, subspace clustering BibRef

Stites, M., Gunther, J., Moon, T., Williams, G.,
Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches,
RS(5), No. 4, April 2013, pp. 1974-1997.
DOI Link 1305
BibRef

Zhao, X.L., Wang, F., Huang, T.Z., Ng, M.K., Plemmons, R.J.,
Deblurring and Sparse Unmixing for Hyperspectral Images,
GeoRS(51), No. 7, 2013, pp. 4045-4058.
IEEE DOI 1307
Matrix decomposition; deblurring; total variation (TV) BibRef

Wang, L.G.[Li-Guo], Liu, D.F.[Dan-Feng], Wang, Q.M.[Qun-Ming],
Geometric Method of Fully Constrained Least Squares Linear Spectral Mixture Analysis,
GeoRS(51), No. 6, 2013, pp. 3558-3566.
IEEE DOI 1307
mixture analysis; hyperspectral data processing; spectral unmixing BibRef

Dowler, S.W., Takashima, R., Andrews, M.,
Reducing the Complexity of the N-FINDR Algorithm for Hyperspectral Image Analysis,
IP(22), No. 7, 2013, pp. 2835-2848.
IEEE DOI 1307
LDU decomposition; pixel removal; N-FINDR; unmixing BibRef

Zhu, F.Y.[Fei-Yun], Wang, Y.[Ying], Xiang, S.M.[Shi-Ming], Fan, B.[Bin], Pan, C.H.[Chun-Hong],
Structured Sparse Method for Hyperspectral Unmixing,
PandRS(88), No. 1, 2014, pp. 101-118.
Elsevier DOI 1402
Hyperspectral Unmixing (HU) BibRef

Wang, Y.[Ying], Pan, C.H.[Chun-Hong], Xiang, S.M.[Shi-Ming], Zhu, F.Y.[Fei-Yen],
Robust Hyperspectral Unmixing With Correntropy-Based Metric,
IP(24), No. 11, November 2015, pp. 4027-4040.
IEEE DOI 1509
geophysical image processing BibRef

Shi, Z., Tang, W., Duren, Z., Jiang, Z.,
Subspace Matching Pursuit for Sparse Unmixing of Hyperspectral Data,
GeoRS(52), No. 6, June 2014, pp. 3256-3274.
IEEE DOI 1403
Hyperspectral imaging BibRef

Henrot, S., Soussen, C., Dossot, M., Brie, D.,
Does Deblurring Improve Geometrical Hyperspectral Unmixing?,
IP(23), No. 3, March 2014, pp. 1169-1180.
IEEE DOI 1403
Raman spectroscopy BibRef

Henrot, S., Chanussot, J., Jutten, C.,
Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images,
IP(25), No. 7, July 2016, pp. 3219-3232.
IEEE DOI 1606
BibRef
And: Correction: IP(25), No. 9, September 2016, pp. 4443-4443.
IEEE DOI 1609
Hyperspectral imaging; digital signatures BibRef

Iordache, M.D., Bioucas-Dias, J.M., Plaza, A.,
Sparse Unmixing of Hyperspectral Data,
GeoRS(49), No. 6, June 2011, pp. 2014-2039.
IEEE DOI 1106
BibRef

Iordache, M.D., Bioucas-Dias, J.M., Plaza, A.,
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing,
GeoRS(50), No. 11, November 2012, pp. 4484-4502.
IEEE DOI 1210

See also Spectral-Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields. BibRef

Iordache, M.D., Bioucas-Dias, J.M., Plaza, A.,
Collaborative Sparse Regression for Hyperspectral Unmixing,
GeoRS(52), No. 1, January 2014, pp. 341-354.
IEEE DOI 1402
geophysical image processing BibRef

Iordache, M.D., Bioucas-Dias, J.M., Plaza, A., Somers, B.,
MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression,
GeoRS(52), No. 7, July 2014, pp. 4364-4382.
IEEE DOI 1403
Correlation BibRef

Ma, W.K., Bioucas-Dias, J.M., Chan, T.H.[Tsung-Han], Gillis, N., Gader, P., Plaza, A.J., Ambikapathi, A., Chi, C.Y.[Chong-Yung],
A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing,
SPMag(31), No. 1, January 2014, pp. 67-81.
IEEE DOI 1403
blind source separation BibRef

Sánchez, S.[Sergio], Plaza, A.[Antonio],
Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs,
RealTimeIP(9), No. 3, September 2014, pp. 397-405.
WWW Link. 1408
BibRef

Li, J.[Jun], Dopido, I., Gamba, P., Plaza, A.,
Complementarity of Discriminative Classifiers and Spectral Unmixing Techniques for the Interpretation of Hyperspectral Images,
GeoRS(53), No. 5, May 2015, pp. 2899-2912.
IEEE DOI 1502
computational complexity BibRef

Marrero, R., Lopez, S., Callico, G.M., Veganzones, M.A., Plaza, A., Chanussot, J., Sarmiento, R.,
A Novel Negative Abundance-Oriented Hyperspectral Unmixing Algorithm,
GeoRS(53), No. 7, July 2015, pp. 3772-3790.
IEEE DOI 1503
Algorithm design and analysis BibRef

Li, J., Agathos, A., Zaharie, D., Bioucas-Dias, J.M., Plaza, A., Li, X.,
Minimum Volume Simplex Analysis: A Fast Algorithm for Linear Hyperspectral Unmixing,
GeoRS(53), No. 9, September 2015, pp. 5067-5082.
IEEE DOI 1506
Algorithm design and analysis BibRef

Zhang, S., Agathos, A., Li, J.,
Robust Minimum Volume Simplex Analysis for Hyperspectral Unmixing,
GeoRS(55), No. 11, November 2017, pp. 6431-6439.
IEEE DOI 1711
Algorithm design and analysis, Approximation algorithms, Optimization, Random variables, Robustness, Chance constraints, endmember identification, hyperspectral imaging, minimum volume simplex analysis (MVSA), spectral unmixing BibRef

Li, J., Bioucas-Dias, J.M., Plaza, A., Liu, L.,
Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing,
GeoRS(54), No. 10, October 2016, pp. 6076-6090.
IEEE DOI 1610
hyperspectral imaging BibRef

Feng, X.R.[Xin-Ru], Li, H.C.[Heng-Chao], Li, J.[Jun], Du, Q.[Qian], Plaza, A.[Antonio], Emery, W.J.[William J.],
Hyperspectral Unmixing Using Sparsity-Constrained Deep Nonnegative Matrix Factorization With Total Variation,
GeoRS(56), No. 10, October 2018, pp. 6245-6257.
IEEE DOI 1810
Hyperspectral imaging, Estimation, Sparse matrices, TV, Periodic structures, Artificial neural networks, total variation (TV) BibRef

Peng, J.T.[Jiang-Tao], Zhou, Y.C.[Yi-Cong], Sun, W.W.[Wei-Wei], Du, Q.[Qian], Xia, L.[Lekang],
Self-Paced Nonnegative Matrix Factorization for Hyperspectral Unmixing,
GeoRS(59), No. 2, February 2021, pp. 1501-1515.
IEEE DOI 2101
Hyperspectral imaging, Adaptation models, Measurement, Data models, Data mining, Sun, Hyperspectral unmixing, self-paced learning (SPL) BibRef

Jiang, Q.[Qin], Dong, Y.F.[Yi-Fei], Peng, J.T.[Jiang-Tao], Yan, M.[Mei], Sun, Y.[Yi],
Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Sánchez, S.[Sergio], Ramalho, R.[Rui], Sousa, L.[Leonel], Plaza, A.[Antonio],
Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs,
RealTimeIP(10), No. 3, September 2015, pp. 469-483.
Springer DOI 1509
BibRef

Yokoya, N., Zhu, X.X., Plaza, A.,
Multisensor Coupled Spectral Unmixing for Time-Series Analysis,
GeoRS(55), No. 5, May 2017, pp. 2842-2857.
IEEE DOI 1705
calibration, geophysical image processing, hyperspectral imaging, remote sensing, time series, AD 2011 to 2015, Fukushima, Hyperion images, Japan, Landsat-8 images, MuCSUn framework, amospheric normalization, cross calibration, BibRef

Marinoni, A., Plaza, A., Gamba, P.,
A Novel Preunmixing Framework for Efficient Detection of Linear Mixtures in Hyperspectral Images,
GeoRS(55), No. 8, August 2017, pp. 4325-4333.
IEEE DOI 1708
Algorithm design and analysis, Hyperspectral imaging, Linearity, Optimization, Reliability, Nonorthogonal projection, remote sensing, spectral analysis, spectral, unmixing BibRef

Gillis, N.[Nicolas], Ma, W.K.[Wing-Kin],
Enhancing Pure-Pixel Identification Performance via Preconditioning,
SIIMS(8), No. 2, 2015, pp. 1161-1186.
DOI Link 1507
used for blind hyperspectral unmixing. BibRef

Dobigeon, N., Tourneret, J.Y., Richard, C., Bermudez, J.C.M., McLaughlin, S., Hero, A.O.,
Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms,
SPMag(31), No. 1, January 2014, pp. 82-94.
IEEE DOI 1403
hyperspectral imaging BibRef

Zare, A., Ho, K.C.,
Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing,
SPMag(31), No. 1, January 2014, pp. 95-104.
IEEE DOI 1403
geophysical image processing BibRef

Tang, W.[Wei], Shi, Z.W.[Zhen-Wei], Wu, Y.[Ying],
Regularized Simultaneous Forward-Backward Greedy Algorithm for Sparse Unmixing of Hyperspectral Data,
GeoRS(52), No. 9, Sept 2014, pp. 5271-5288.
IEEE DOI 1407
computational complexity BibRef

Tang, W.[Wei], Shi, Z.W.[Zhen-Wei], Wu, Y.[Ying], Zhang, C.S.[Chang-Shui],
Sparse Unmixing of Hyperspectral Data Using Spectral A Priori Information,
GeoRS(53), No. 2, February 2015, pp. 770-783.
IEEE DOI 1411
data analysis BibRef

Xu, X.[Xia], Shi, Z.W.[Zhen-Wei],
Multi-objective based spectral unmixing for hyperspectral images,
PandRS(124), No. 1, 2017, pp. 54-69.
Elsevier DOI 1702
Hyperspectral image BibRef

Xu, X.[Xia], Shi, Z.W.[Zhen-Wei], Pan, B.[Bin],
l0-based sparse hyperspectral unmixing using spectral information and a multi-objectives formulation,
PandRS(141), 2018, pp. 46-58.
Elsevier DOI 1806
Hyperspectral image, Sparse unmixing, Multi-objective, Non-convex optimization BibRef

Pan, B.[Bin], Shi, Z.W.[Zhen-Wei], Xu, X.[Xia],
Analysis for the Weakly Pareto Optimum in Multiobjective-Based Hyperspectral Band Selection,
GeoRS(57), No. 6, June 2019, pp. 3729-3740.
IEEE DOI 1906
Pareto optimization, Sociology, Hyperspectral imaging, Linear programming, Band selection, hyperspectral imagery (HSI), weakly Pareto optimum BibRef

Yang, S.[Shuo], Shi, Z.W.[Zhen-Wei], Tang, W.[Wei],
Robust Hyperspectral Image Target Detection Using an Inequality Constraint,
GeoRS(53), No. 6, June 2015, pp. 3389-3404.
IEEE DOI 1503
geophysical image processing BibRef

Yang, S.[Shuo], Shi, Z.W.[Zhen-Wei],
Hyperspectral Image Target Detection Improvement Based on Total Variation,
IP(25), No. 5, May 2016, pp. 2249-2258.
IEEE DOI 1604
Detection algorithms BibRef

Bendoumi, M., He, M., Mei, S.,
Hyperspectral Image Resolution Enhancement Using High-Resolution Multispectral Image Based on Spectral Unmixing,
GeoRS(52), No. 10, October 2014, pp. 6574-6583.
IEEE DOI 1407
Hyperspectral sensors BibRef

Sigurdsson, J., Ulfarsson, M.O., Sveinsson, J.R.,
Hyperspectral Unmixing With L_q Regularization,
GeoRS(52), No. 11, November 2014, pp. 6793-6806.
IEEE DOI 1407
Cost function BibRef

Sigurdsson, J., Ulfarsson, M.O., Sveinsson, J.R.,
Blind Hyperspectral Unmixing Using Total Variation and L_q Sparse Regularization,
GeoRS(54), No. 11, November 2016, pp. 6371-6384.
IEEE DOI 1610
Estimation BibRef

Sigurdsson, J., Ulfarsson, M.O., Sveinsson, J.R., Bioucas-Dias, J.M.,
Sparse Distributed Multitemporal Hyperspectral Unmixing,
GeoRS(55), No. 11, November 2017, pp. 6069-6084.
IEEE DOI 1711
Hyperspectral imaging, Alternating direction method of multipliers (ADMM), unmixing BibRef

Wang, N.[Nan], Du, B.[Bo], Zhang, L.P.[Liang-Pei], Zhang, L.[Lifu],
An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing,
GeoRS(53), No. 1, January 2015, pp. 416-428.
IEEE DOI 1410
geophysical image processing BibRef

Zhang, L.P.[Liang-Pei], Zhu, X.J.[Xiao-Jie], Zhang, L.F.[Le-Fei], Du, B.[Bo],
Multidomain Subspace Classification for Hyperspectral Images,
GeoRS(54), No. 10, October 2016, pp. 6138-6150.
IEEE DOI 1610
hyperspectral imaging BibRef

Zhang, B.[Bing], Zhuang, L.[Lina], Gao, L.[Lianru], Luo, W.F.[Wen-Fei], Ran, Q.[Qiong], Du, Q.[Qian],
PSO-EM: A Hyperspectral Unmixing Algorithm Based On Normal Compositional Model,
GeoRS(52), No. 12, December 2014, pp. 7782-7792.
IEEE DOI 1410
expectation-maximisation algorithm BibRef

Feng, R.[Ruyi], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Adaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery,
PandRS(97), No. 1, 2014, pp. 9-24.
Elsevier DOI 1410
Hyperspectral imagery BibRef

Ammanouil, R., Ferrari, A., Richard, C., Mary, D.,
Blind and Fully Constrained Unmixing of Hyperspectral Images,
IP(23), No. 12, December 2014, pp. 5510-5518.
IEEE DOI 1412
convex programming BibRef

Ammanouil, R., Ferrari, A., Richard, C., Mathieu, S.,
Nonlinear Unmixing of Hyperspectral Data With Vector-Valued Kernel Functions,
IP(26), No. 1, January 2017, pp. 340-354.
IEEE DOI 1612
Hilbert spaces BibRef

Zhu, F.Y.[Fei-Yun], Wang, Y.[Ying], Fan, B.[Bin], Xiang, S.M.[Shi-Ming], Meng, G.F.[Geo-Feng], Pan, C.H.[Chun-Hong],
Spectral Unmixing via Data-Guided Sparsity,
IP(23), No. 12, December 2014, pp. 5412-5427.
IEEE DOI 1412
hyperspectral imaging BibRef

Bauer, S., Stefan, J., Michelsburg, M., Laengle, T., Leon, F.P.,
Robustness Improvement of Hyperspectral Image Unmixing by Spatial Second-Order Regularization,
IP(23), No. 12, December 2014, pp. 5209-5221.
IEEE DOI 1412
hyperspectral imaging BibRef

Akhtar, N.[Naveed], Shafait, F.[Faisal], Mian, A.[Ajmal],
Futuristic Greedy Approach to Sparse Unmixing of Hyperspectral Data,
GeoRS(53), No. 4, April 2015, pp. 2157-2174.
IEEE DOI 1502
BibRef
Earlier:
SUnGP: A Greedy Sparse Approximation Algorithm for Hyperspectral Unmixing,
ICPR14(3726-3731)
IEEE DOI 1412
BibRef

Akhtar, N.[Naveed], Shafait, F.[Faisal], Mian, A.[Ajmal],
Discriminative Bayesian Dictionary Learning for Classification,
PAMI(38), No. 12, December 2016, pp. 2374-2388.
IEEE DOI 1609
BibRef
Earlier:
Bayesian sparse representation for hyperspectral image super resolution,
CVPR15(3631-3640)
IEEE DOI 1510
BibRef
Earlier:
Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution,
ECCV14(VII: 63-78).
Springer DOI 1408
Analytical models. geophysical image processing. Approximation algorithms BibRef

Akhtar, N.[Naveed], Mian, A.[Ajmal],
RCMF: Robust Constrained Matrix Factorization for Hyperspectral Unmixing,
GeoRS(55), No. 6, June 2017, pp. 3354-3366.
IEEE DOI 1706
Blind source separation, Dictionaries, Hyperspectral imaging, Indexes, Robustness, Sparse matrices, Blind source separation, hyperspectral unmixing, robust matrix factorization, sparse representation, unsupervised, unmixing BibRef

Akhtar, N.[Naveed], Shafait, F.[Faisal], Mian, A.[Ajmal],
Hierarchical Beta Process with Gaussian Process Prior for Hyperspectral Image Super Resolution,
ECCV16(III: 103-120).
Springer DOI 1611
BibRef

Lin, C.H.[Chia-Hsiang], Ma, W.K.[Wing-Kin], Li, W.C.A.[Wei-Chi-Ang], Chi, C.Y.[Chong-Yung], Ambikapathi, A.,
Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No-Pure-Pixel Case,
GeoRS(53), No. 10, October 2015, pp. 5530-5546.
IEEE DOI 1509
geophysical image processing BibRef

Li, C.Z.[Chun-Zhi], Chen, X.H.[Xiao-Hua], Jiang, Y.L.[Yun-Liang],
On Diverse Noises in Hyperspectral Unmixing,
GeoRS(53), No. 10, October 2015, pp. 5388-5402.
IEEE DOI 1509
hyperspectral imaging BibRef

Fevotte, C., Dobigeon, N.,
Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization,
IP(24), No. 12, December 2015, pp. 4810-4819.
IEEE DOI 1512
geophysical image processing BibRef

Uezato, T.[Tatsumi], Fauvel, M.[Mathieu], Dobigeon, N.[Nicolas],
Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
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Altmann, Y.[Yoann], Pereyra, M.[Marcelo], Bioucas-Dias, J.[José],
Collaborative sparse regression using spatially correlated supports-Application to hyperspectral unmixing,
IP(24), No. 12, December 2015, pp. 5800-5811.
IEEE DOI 1512
Bayes methods BibRef

Li, Z.[Zeng], Altmann, Y.[Yoann], Chen, J.[Jie], Mclaughlin, S.[Stephen], Rahardja, S.[Susanto],
Sparse Spectral Unmixing of Hyperspectral Images using Expectation-Propagation,
VCIP20(197-200)
IEEE DOI 2102
Hyperspectral imaging, Covariance matrices, Bayes methods, Signal to noise ratio, Libraries, Slabs, Gaussian noise, Approximate Bayesian method BibRef

Guerra, R., Santos, L., Lopez, S., Sarmiento, R.,
A New Fast Algorithm for Linearly Unmixing Hyperspectral Images,
GeoRS(53), No. 12, December 2015, pp. 6752-6765.
IEEE DOI 1512
geophysical image processing BibRef

Guerra, R., López, S., Sarmiento, R.,
A Computationally Efficient Algorithm for Fusing Multispectral and Hyperspectral Images,
GeoRS(54), No. 10, October 2016, pp. 5712-5728.
IEEE DOI 1610
hyperspectral imaging BibRef

Díaz, M., Guerra, R., López, S., Sarmiento, R.,
An Algorithm for an Accurate Detection of Anomalies in Hyperspectral Images With a Low Computational Complexity,
GeoRS(56), No. 2, February 2018, pp. 1159-1176.
IEEE DOI 1802
Algorithm design and analysis, Covariance matrices, Detectors, Hyperspectral imaging, Principal component analysis, real-time applications BibRef

Zheng, C.Y.[Cheng Yong], Li, H.[Hong], Wang, Q.[Qiong], Chen, C.L.P.[C.L. Philip],
Reweighted Sparse Regression for Hyperspectral Unmixing,
GeoRS(54), No. 1, January 2016, pp. 479-488.
IEEE DOI 1601
geophysical image processing BibRef

Salehani, Y.E.[Yaser Esmaeili], Gazor, S.[Saeed], Kim, I.M.[Il-Min], Yousefi, S.[Shahram],
L_0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing,
RS(8), No. 3, 2016, pp. 187.
DOI Link 1604
BibRef

Salehani, Y.E.[Yaser Esmaeili], Cheriet, M.[Mohamed],
Non-dictionary Aided Sparse Unmixing of Hyperspectral Images via Weighted Nonnegative Matrix Factorization,
ICIAR17(596-604).
Springer DOI 1706
BibRef

Salehani, Y.E.[Yaser Esmaeili], Gazor, S.[Saeed],
Collaborative Unmixing Hyperspectral Imagery via Nonnegative Matrix Factorization,
ICISP16(118-126).
WWW Link. 1606
BibRef

Zhang, G., Xu, Y., Fang, F.,
Framelet-Based Sparse Unmixing of Hyperspectral Images,
IP(25), No. 4, April 2016, pp. 1516-1529.
IEEE DOI 1604
approximation theory BibRef

Liu, R.[Rong], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF,
RS(8), No. 6, 2016, pp. 464.
DOI Link 1608
BibRef

Li, C.[Chang], Ma, Y.[Yong], Mei, X.G.[Xiao-Guang], Liu, C.Y.[Cheng-Yin], Ma, J.Y.[Jia-Yi],
Hyperspectral Unmixing with Robust Collaborative Sparse Regression,
RS(8), No. 7, 2016, pp. 588.
DOI Link 1608
BibRef

Ma, Y.[Yong], Li, C.[Chang], Mei, X.G.[Xiao-Guang], Liu, C.Y.[Cheng-Yin], Ma, J.Y.[Jia-Yi],
Robust Sparse Hyperspectral Unmixing With L_2,1 Norm,
GeoRS(55), No. 3, March 2017, pp. 1227-1239.
IEEE DOI 1703
Collaboration BibRef

Heylen, R., Zare, A., Gader, P., Scheunders, P.,
Hyperspectral Unmixing With Endmember Variability via Alternating Angle Minimization,
GeoRS(54), No. 8, August 2016, pp. 4983-4993.
IEEE DOI 1608
geophysics computing BibRef

Giampouras, P.V., Themelis, K.E., Rontogiannis, A.A., Koutroumbas, K.D.,
Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing,
GeoRS(54), No. 8, August 2016, pp. 4775-4789.
IEEE DOI 1608
geophysical image processing BibRef

Drumetz, L., Veganzones, M.A., Henrot, S., Phlypo, R., Chanussot, J., Jutten, C.,
Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability,
IP(25), No. 8, August 2016, pp. 3890-3905.
IEEE DOI 1608
data analysis BibRef

Hong, D., Yokoya, N., Chanussot, J., Zhu, X.X.,
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing,
IP(28), No. 4, April 2019, pp. 1923-1938.
IEEE DOI 1901
geophysical image processing, hyperspectral imaging, learning (artificial intelligence), remote sensing, spectral variability BibRef

Fu, X., Ma, W.K., Bioucas-Dias, J.M., Chan, T.H.,
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches,
GeoRS(54), No. 9, September 2016, pp. 5171-5184.
IEEE DOI 1609
geophysical image processing BibRef

Halimi, A., Honeine, P., Bioucas-Dias, J.M.,
Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity, or Mismodeling Effects,
IP(25), No. 10, October 2016, pp. 4565-4579.
IEEE DOI 1610
computational complexity BibRef

Kong, F.Q.[Fan-Qiang], Li, Y.S.[Yun-Song], Guo, W.J.[Wen-Jun],
Regularized MSBL algorithm with spatial correlation for sparse hyperspectral unmixing,
JVCIR(40, Part B), No. 1, 2016, pp. 525-537.
Elsevier DOI 1610
Hyperspectral unmixing BibRef

Zhong, Y.F.[Yan-Fei], Wang, X.Y.[Xin-Yu], Zhao, L.[Lin], Feng, R.[Ruyi], Zhang, L.P.[Liang-Pei], Xu, Y.Y.[Yan-Yan],
Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery,
PandRS(119), No. 1, 2016, pp. 49-63.
Elsevier DOI 1610
Hyperspectral remote sensing BibRef

Feng, R.[Ruyi], Wang, L.Z.[Li-Zhe], Zhong, Y.F.[Yan-Fei],
Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Feng, R.[Ruyi], Zhong, Y.F.[Yan-Fei], Wang, L.Z.[Li-Zhe], Lin, W.J.[Wen-Juan],
Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Zhou, Y., Rangarajan, A., Gader, P.D.,
A Spatial Compositional Model for Linear Unmixing and Endmember Uncertainty Estimation,
IP(25), No. 12, December 2016, pp. 5987-6002.
IEEE DOI 1612
geophysical image processing BibRef

Zhou, Y., Rangarajan, A., Gader, P.D.,
A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing,
IP(27), No. 5, May 2018, pp. 2242-2256.
IEEE DOI 1804
Gaussian processes, hyperspectral imaging, image processing, maximum likelihood estimation, remote sensing, linear unmixing BibRef

Nakhostin, S., Clenet, H., Corpetti, T., Courty, N.,
Joint Anomaly Detection and Spectral Unmixing for Planetary Hyperspectral Images,
GeoRS(54), No. 12, December 2016, pp. 6879-6894.
IEEE DOI 1612
data analysis BibRef

Chen, P.[Peng], Nelson, J.D.B.[James D.B.], Tourneret, J.Y.[Jean-Yves],
Toward a Sparse Bayesian Markov Random Field Approach to Hyperspectral Unmixing and Classification,
IP(26), No. 1, January 2017, pp. 426-438.
IEEE DOI 1612
Markov 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

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

Qian, Y., Xiong, F., Zeng, S., Zhou, J., Tang, Y.Y.,
Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery,
GeoRS(55), No. 3, March 2017, pp. 1776-1792.
IEEE DOI 1703
Distance measurement BibRef

Zhu, F., Halimi, A., Honeine, P., Chen, B., Zheng, N.,
Correntropy Maximization via ADMM: Application to Robust Hyperspectral Unmixing,
GeoRS(55), No. 9, September 2017, pp. 4944-4955.
IEEE DOI 1709
geophysical image processing, hyperspectral imaging, learning (artificial intelligence), maximum entropy methods, correntropy maximization, cuprite mining image, fully constrained unmixing, robust supervised spectral unmixing, Convex functions, Kernel, Noise measurement, Optimization, Robustness, Alternating direction method of multipliers (ADMM), correntropy, BibRef

Kizel, F., Shoshany, M., Netanyahu, N.S., Even-Tzur, G., Benediktsson, J.A.,
A Stepwise Analytical Projected Gradient Descent Search for Hyperspectral Unmixing and Its Code Vectorization,
GeoRS(55), No. 9, September 2017, pp. 4925-4943.
IEEE DOI 1709
estimation theory, gradient methods, hyperspectral imaging, optimisation, spectral analysis, EM estimation, endmembers estimation, projected gradient descent search, Lighting, Linear programming, BibRef

Drumetz, L., Ehsandoust, B., Chanussot, J., Rivet, B., Babaie-Zadeh, M., Jutten, C.,
Relationships Between Nonlinear and Space-Variant Linear Models in Hyperspectral Image Unmixing,
SPLetters(24), No. 10, October 2017, pp. 1567-1571.
IEEE DOI 1710
hyperspectral imaging, mixture models, remote sensing, source separation, BibRef

Zhou, Y., Feng, L., Hou, C., Kung, S.Y.,
Hyperspectral and Multispectral Image Fusion Based on Local Low Rank and Coupled Spectral Unmixing,
GeoRS(55), No. 10, October 2017, pp. 5997-6009.
IEEE DOI 1710
convergence, hyperspectral imaging, image fusion, image resolution, spectral analysis, convergence, coupled spectral unmixing, fusion algorithm, hyperspectral image fusion, linear spectral unmixing, local low-rank property, multiscale postprocessing procedure, multispectral image fusion, patch size, BibRef

Wang, X., Zhong, Y., Zhang, L., Xu, Y.,
Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing,
GeoRS(55), No. 11, November 2017, pp. 6287-6304.
IEEE DOI 1711
Blind source separation, Estimation, Matrix decomposition, Shape, Sparse matrices, Hyperspectral unmixing (HU), nonnegative matrix factorization (NMF). BibRef

Lanaras, C.[Charis], Baltsavias, E.[Emmanuel], Schindler, K.[Konrad],
Hyperspectral Super-Resolution with Spectral Unmixing Constraints,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef
Earlier:
Hyperspectral Super-Resolution by Coupled Spectral Unmixing,
ICCV15(3586-3594)
IEEE DOI 1602
BibRef
And:
Advances in Hyperspectral and Multispectral Image Fusion and Spectral Unmixing,
GeoHyper15(451-458).
DOI Link 1602
Cameras BibRef

Li, C.[Chang], Ma, Y.[Yong], Mei, X.G.[Xiao-Guang], Fan, F.[Fan], Huang, J.[Jun], Ma, J.Y.[Jia-Yi],
Sparse Unmixing of Hyperspectral Data with Noise Level Estimation,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Jiang, X., Gong, M., Li, H., Zhang, M., Li, J.,
A Two-Phase Multiobjective Sparse Unmixing Approach for Hyperspectral Data,
GeoRS(56), No. 1, January 2018, pp. 508-523.
IEEE DOI 1801
Algorithm design and analysis, Hyperspectral imaging, Libraries, Pareto optimization, Sociology, Better decision, two-phase multiobjective sparse unmixing (Tp-MoSU) BibRef

Yang, B., Wang, B., Wu, Z.,
Nonlinear Hyperspectral Unmixing Based on Geometric Characteristics of Bilinear Mixture Models,
GeoRS(56), No. 2, February 2018, pp. 694-714.
IEEE DOI 1802
gradient methods, hyperspectral imaging, image processing, bilinear mixture models, gradient algorithm, simplex BibRef

Rizkinia, M.[Mia], Okuda, M.[Masahiro],
Joint Local Abundance Sparse Unmixing for Hyperspectral Images,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Wang, D., Shi, Z., Cui, X.,
Robust Sparse Unmixing for Hyperspectral Imagery,
GeoRS(56), No. 3, March 2018, pp. 1348-1359.
IEEE DOI 1804
geophysical image processing, hyperspectral imaging, regression analysis, remote sensing, substitution BibRef

Zhang, X.R.[Xiang-Rong], Li, C.[Chen], Zhang, J.Y.[Jing-Yan], Chen, Q.M.[Qi-Meng], Feng, J.[Jie], Jiao, L.C.[Li-Cheng], Zhou, H.Y.[Hui-Yu],
Hyperspectral Unmixing via Low-Rank Representation with Space Consistency Constraint and Spectral Library Pruning,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Zhang, X.R.[Xiang-Rong], Ma, X.X.[Xiao-Xiao], Ning, H.Y.[Hu-Yan], Gu, J.[Jing], Tang, X.[Xu], Jiao, L.C.[Li-Cheng],
Spectral-Difference Low-Rank Representation Learning for Hyperspectral Anomaly Detection,
GeoRS(59), No. 12, December 2021, pp. 10364-10377.
IEEE DOI 2112
Dictionaries, Anomaly detection, Hyperspectral imaging, Sparse matrices, Matrix decomposition, Machine learning, spectral-difference BibRef

Yang, B.[Bin], Wang, B.[Bin], Wu, Z.M.[Zong-Min],
Unsupervised Nonlinear Hyperspectral Unmixing Based on Bilinear Mixture Models via Geometric Projection and Constrained Nonnegative Matrix Factorization,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Yin, Z.Q.[Zhang-Qiang], Yang, B.[Bin],
Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation,
RS(15), No. 20, 2023, pp. 5028.
DOI Link 2310
BibRef

Liu, J.[Jun], Luo, B.[Bin], Douté, S.[Sylvain], Chanussot, J.[Jocelyn],
Exploration of Planetary Hyperspectral Images with Unsupervised Spectral Unmixing: A Case Study of Planet Mars,
RS(10), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Cao, J.J.[Jing-Jing], Zhuo, L.[Li], Tao, H.Y.[Hai-Yan],
An Endmember Initialization Scheme for Nonnegative Matrix Factorization and Its Application in Hyperspectral Unmixing,
IJGI(7), No. 5, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Kizel, F.[Fadi], Shoshany, M.[Maxim],
Spatially adaptive hyperspectral unmixing through endmembers analytical localization based on sums of anisotropic 2D Gaussians,
PandRS(141), 2018, pp. 185-207.
Elsevier DOI 1806
Spectral unmixig, Hyper-spectral imaging, Gradient optimization, Spatial adaptation, Spatial localization BibRef

Zhang, Z.Y.[Zuo-Yu], Liao, S.Y.[Shou-Yi], Zhang, H.X.[He-Xin], Wang, S.C.[Shi-Cheng], Wang, Y.C.[Yong-Chao],
Bilateral Filter Regularized L2 Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef

Zhang, X.R.[Xiang-Rong], Zhang, J.Y.[Jing-Yan], Li, C.[Chen], Cheng, C.[Cai], Jiao, L.C.[Li-Cheng], Zhou, H.Y.[Hui-Yu],
Hybrid Unmixing Based on Adaptive Region Segmentation for Hyperspectral Imagery,
GeoRS(56), No. 7, July 2018, pp. 3861-3875.
IEEE DOI 1807
geophysical image processing, geophysical techniques, hyperspectral imaging, image segmentation, remote sensing, sparse constraint BibRef

Zhang, J.Y.[Jing-Yan], Zhang, X.R.[Xiang-Rong], Tang, X.[Xu], Chen, P.H.[Pu-Hua], Jiao, L.C.[Li-Cheng],
Sketch-Based Region Adaptive Sparse Unmixing Applied to Hyperspectral Image,
GeoRS(58), No. 12, December 2020, pp. 8840-8856.
IEEE DOI 2012
Hyperspectral imaging, Manifolds, Estimation, Linear programming, Sparse matrices, Task analysis, Hyperspectral sparse unmixing, sketch map BibRef

Das, S.[Samiran], Routray, A.[Aurobinda], Deb, A.K.[Alok Kanti],
Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
BibRef

Wu, Z.B.[Ze-Bin], Liu, J.J.[Jian-Jun], Ye, S.[Shun], Sun, L.[Le], Wei, Z.H.[Zhi-Hui],
Optimization of minimum volume constrained hyperspectral image unmixing on CPU-GPU heterogeneous platform,
RealTimeIP(15), No. 2, August 2018, pp. 265-277.
WWW Link. 1808
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Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
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Shi, Z., Shi, T., Zhou, M., Xu, X.,
Collaborative Sparse Hyperspectral Unmixing Using L_0 Norm,
GeoRS(56), No. 9, September 2018, pp. 5495-5508.
IEEE DOI 1809
Hyperspectral imaging, Collaboration, Libraries, Optimization, Sparse matrices, Relaxation methods, l0 norm BibRef

Amiri, F., Kahaei, M.H.,
A sparsity-based Bayesian approach for hyperspectral unmixing using normal compositional model,
SIViP(12), No. 7, October 2018, pp. 1361-1367.
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Band selection using variational mode decomposition applied in sparsity-based hyperspectral unmixing algorithms,
SIViP(12), No. 8, November 2018, pp. 1463-1470.
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Fernandez-Beltran, R., Plaza, A., Plaza, J., Pla, F.,
Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis,
GeoRS(56), No. 11, November 2018, pp. 6344-6360.
IEEE DOI 1811
Semantics, Hyperspectral imaging, Probabilistic logic, Data models, Biological system modeling, Computational modeling, topic models BibRef

Yang, B., Wang, B.,
Band-Wise Nonlinear Unmixing for Hyperspectral Imagery Using an Extended Multilinear Mixing Model,
GeoRS(56), No. 11, November 2018, pp. 6747-6762.
IEEE DOI 1811
Scattering, Biological system modeling, Hyperspectral imaging, Kernel, Mixture models, Optimization, Mathematical model, wavelength-wise nonlinearity BibRef

Li, C.[Chang], Liu, Y.[Yu], Cheng, J.[Juan], Song, R.[Rencheng], Peng, H.[Hu], Chen, Q.A.[Qi-Ang], Chen, X.[Xun],
Hyperspectral Unmixing with Bandwise Generalized Bilinear Model,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
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Revel, C.[Charlotte], Deville, Y.[Yannick], Achard, V.[Véronique], Briottet, X.[Xavier], Weber, C.[Christiane],
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: A Hyperspectral Unmixing Method Dealing with Intra-Class Variability,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Cubero-Castan, M.[Manuel], Chanussot, J.[Jocelyn], Achard, V.[Veronique], Briottet, X.[Xavier], Shimoni, M.[Michal],
A physics-based unmixing method for thermal hyperspectral images,
ICIP14(5082-5086)
IEEE DOI 1502
Asphalt BibRef

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Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria,
RS(12), No. 14, 2020, pp. xx-yy.
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Ortiz, A.[Alberto], Rodríguez, A.[Alfonso], Guerra, R.[Raúl], López, S.[Sebastián], Otero, A.[Andrés], Sarmiento, R.[Roberto], de la Torre, E.[Eduardo],
A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images,
RS(10), No. 11, 2018, pp. xx-yy.
DOI Link 1812
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Arablouei, R.,
Spectral Unmixing With Perturbed Endmembers,
GeoRS(57), No. 1, January 2019, pp. 194-211.
IEEE DOI 1901
Perturbation methods, Mixture models, Estimation, Libraries, Data models, Hyperspectral imaging, total variation BibRef

Alam, F.I., Zhou, J., Liew, A.W.C.[Alan Wee-Chung], Jia, X., Chanussot, J., Gao, Y.,
Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification,
GeoRS(57), No. 3, March 2019, pp. 1612-1628.
IEEE DOI 1903
convolutional neural nets, feature extraction, geophysical image processing, image classification, image classification BibRef

Alam, F.I., Zhou, J., Tong, L., Liew, A.W.C.[Alan Wee-Chung], Gao, Y.,
Combining Unmixing and Deep Feature Learning for Hyperspectral Image Classification,
DICTA17(1-8)
IEEE DOI 1804
feature extraction, geophysical image processing, image classification, learning (artificial intelligence), Training BibRef

Yao, J., Meng, D., Zhao, Q., Cao, W., Xu, Z.,
Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing,
IP(28), No. 6, June 2019, pp. 2991-3006.
IEEE DOI 1905
geophysical image processing, hyperspectral imaging, image denoising, iterative methods, matrix decomposition, non-local total variation regularization BibRef

Zou, J.L.[Jin-Lin], Lan, J.H.[Jin-Hui],
A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903
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Qu, Y., Qi, H.,
uDAS: An Untied Denoising Autoencoder With Sparsity for Spectral Unmixing,
GeoRS(57), No. 3, March 2019, pp. 1698-1712.
IEEE DOI 1903
hyperspectral imaging, image coding, image denoising, remote sensing, unsupervised learning, spectral unmixing BibRef

Thouvenin, P., Dobigeon, N., Tourneret, J.,
Partially Asynchronous Distributed Unmixing of Hyperspectral Images,
GeoRS(57), No. 4, April 2019, pp. 2009-2021.
IEEE DOI 1904
hyperspectral imaging, image processing, optimisation, remote sensing, matrix factorization problems, partially asynchronous distributed estimation BibRef

Huang, J.[Jie], Huang, T.Z.[Ting-Zhu], Deng, L.J.[Liang-Jian], Zhao, X.L.[Xi-Le],
Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing,
GeoRS(57), No. 4, April 2019, pp. 2419-2438.
IEEE DOI 1904
hyperspectral imaging, image representation, regression analysis, sparse matrices, low-rank representation, hyperspectral unmixing, spectral unmixing BibRef

Ma, Y.[Yong], Jin, Q.W.[Qi-Wen], Mei, X.G.[Xiao-Guang], Dai, X.B.[Xiao-Bing], Fan, F.[Fan], Li, H.[Hao], Huang, J.[Jun],
Hyperspectral Unmixing with Gaussian Mixture Model and Low-Rank Representation,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
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Jin, Q.W.[Qi-Wen], Ma, Y.[Yong], Pan, E.[Erting], Fan, F.[Fan], Huang, J.[Jun], Li, H.[Hao], Sui, C.[Chenhong], Mei, X.G.[Xiao-Guang],
Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
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Shao, Y.[Yang], Lan, J.H.[Jin-Hui],
A Spectral Unmixing Method by Maximum Margin Criterion and Derivative Weights to Address Spectral Variability in Hyperspectral Imagery,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905
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Feng, R.[Ruyi], Wang, L.[Lizhe], Zhong, Y.F.[Yan-Fei],
Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906
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Ibarrola-Ulzurrun, E., Drumetz, L., Marcello, J., Gonzalo-Martín, C., Chanussot, J.,
Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability,
GeoRS(57), No. 7, July 2019, pp. 4775-4788.
IEEE DOI 1907
Ecosystems, Hyperspectral imaging, Vegetation mapping, Biodiversity, Training, Monitoring, CASI, spectral variability BibRef

Liu, H.Y.[Hong-Yi], Lu, Y.K.[You-Kang], Wu, Z.B.[Ze-Bin], Du, Q.[Qian], Chanussot, J.[Jocelyn], Wei, Z.H.[Zhi-Hui],
Bayesian Unmixing of Hyperspectral Image Sequence With Composite Priors for Abundance and Endmember Variability,
GeoRS(60), 2022, pp. 1-15.
IEEE DOI 2112
Bayes methods, Hyperspectral imaging, Principal component analysis, Monte Carlo methods, Markov chain Monte Carlo (MCMC) method BibRef

Heylen, R., Andrejchenko, V., Zahiri, Z., Parente, M., Scheunders, P.,
Nonlinear Hyperspectral Unmixing With Graphical Models,
GeoRS(57), No. 7, July 2019, pp. 4844-4856.
IEEE DOI 1907
Mathematical model, Optical mixing, Optical imaging, Predictive models, Hyperspectral imaging, Hyperspectral unmixing, spectral mixing models BibRef

Shahid, K.T., Schizas, I.D.,
Unsupervised Kernelized Correlation-Based Hyperspectral Unmixing With Missing Pixels,
GeoRS(57), No. 7, July 2019, pp. 4509-4520.
IEEE DOI 1907
Hyperspectral imaging, Correlation, Kernel, Estimation, Minimization, Atmospheric modeling, Canonical correlations, unsupervised unmixing BibRef

Marinoni, A., Gamba, P.,
Improving Reliability in Nonlinear Hyperspectral Unmixing by Multidimensional Structural Optimization,
GeoRS(57), No. 8, August 2019, pp. 5211-5223.
IEEE DOI 1908
geochemistry, geophysical techniques, hyperspectral imaging, nonlinear hyperspectral unmixing, nonlinear unmixing BibRef

Han, H.W.[Hong-Wei], Guo, K.[Ke], Wang, M.Z.[Mao-Zhi], Zhang, T.B.[Ting-Bin], Zhang, S.[Shuang],
Fast Hyperspectral Unmixing via Reweighted Sparse Regression,
IEICE(E102-D), No. 9, September 2019, pp. 1819-1832.
WWW Link. 1909
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Wang, X., Zhong, Y., Zhang, L., Xu, Y.,
Blind Hyperspectral Unmixing Considering the Adjacency Effect,
GeoRS(57), No. 9, September 2019, pp. 6633-6649.
IEEE DOI 1909
Hyperspectral imaging, Atmospheric modeling, Deconvolution, Kernel, Convex functions, Adjacency effect (AE), deblurring BibRef

Woodbridge, Y., Okun, U., Elidan, G., Wiesel, A.,
Unmixing K-Gaussians With Application to Hyperspectral Imaging,
GeoRS(57), No. 9, September 2019, pp. 7281-7293.
IEEE DOI 1909
Hyperspectral imaging, Stochastic processes, Covariance matrices, Computational modeling, Estimation, Bayes methods, normal compositional model (NCM) BibRef

Koirala, B.[Bikram], Khodadadzadeh, M.[Mahdi], Contreras, C.[Cecilia], Zahiri, Z.[Zohreh], Gloaguen, R.[Richard], Scheunders, P.[Paul],
A Supervised Method for Nonlinear Hyperspectral Unmixing,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
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Alkhatib, M.Q.[Mohammed Q.], Velez-Reyes, M.[Miguel],
Improved Spatial-Spectral Superpixel Hyperspectral Unmixing,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
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Li, M.[Minglei], Zhu, F.[Fei], Guo, A.J.X.[Alan J. X.], Chen, J.[Jie],
A Graph Regularized Multilinear Mixing Model for Nonlinear Hyperspectral Unmixing,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910
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Li, J., Li, Y., Song, R., Mei, S., Du, Q.,
Local Spectral Similarity Preserving Regularized Robust Sparse Hyperspectral Unmixing,
GeoRS(57), No. 10, October 2019, pp. 7756-7769.
IEEE DOI 1910
convex programming, geophysical image processing, hyperspectral imaging, image resolution, spectral fidelity BibRef

Das, S.[Samiran], Routray, A.[Aurobinda], Deb, A.K.[Alok Kanti],
Sparsity measure based library aided unmixing of hyperspectral image,
IET-IPR(13), No. 12, October 2019, pp. 2077-2085.
DOI Link 1911
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Rashwan, S.[Shaheera], Dobigeon, N.[Nicolas], Sheta, W.[Walaa], Hassan, H.[Hanan],
Non-linear unmixing of hyperspectral images using multiple-kernel self-organising maps,
IET-IPR(13), No. 12, October 2019, pp. 2190-2195.
DOI Link 1911
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Zeng, Y.L.[Yi-Liang], Ritz, C.[Christian], Zhao, J.H.[Jia-Hong], Lan, J.H.[Jin-Hui],
Scattering Transform Framework for Unmixing of Hyperspectral Data,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
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Xu, X., Shi, Z., Pan, B., Li, X.,
A Classification-Based Model for Multi-Objective Hyperspectral Sparse Unmixing,
GeoRS(57), No. 12, December 2019, pp. 9612-9625.
IEEE DOI 1912
Optimization, Libraries, Hyperspectral imaging, Reliability, Sociology, Statistics, Classification model, hyperspectral images, sparse unmixing BibRef

Zhuang, L., Lin, C., Figueiredo, M.A.T., Bioucas-Dias, J.M.,
Regularization Parameter Selection in Minimum Volume Hyperspectral Unmixing,
GeoRS(57), No. 12, December 2019, pp. 9858-9877.
IEEE DOI 1912
Hyperspectral imaging, Optimization, TV, Image reconstruction, Europe, Sparse matrices, Craig criterion, spectral unmixing BibRef

Zheng, Y.H.[Yu-Hui], Wu, F.Y.[Fei-Yang], Shim, H.J.[Hiuk Jae], Sun, L.[Le],
Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
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Yang, J.[Jihai], Jia, M.M.[Ming-Mei], Xu, C.[Chang], Li, S.J.[Shi-Jun],
Joint hyperspectral unmixing for urban computing,
GeoInfo(24), No. 1, January 2020, pp. 247-265.
WWW Link. 2002
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Uezato, T., Yokoya, N., He, W.,
Illumination Invariant Hyperspectral Image Unmixing Based on a Digital Surface Model,
IP(29), 2020, pp. 3652-3664.
IEEE DOI 2002
Spectral unmixing, endmember, illumination, shadow, spectral variability BibRef

Borsoi, R.A., Imbiriba, T., Bermudez, J.C.M.,
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability,
IP(29), 2020, pp. 3638-3651.
IEEE DOI 2002
Hyperspectral data, spectral variability, spatial regularization, multiscale, superpixels BibRef

Drumetz, L., Chanussot, J., Jutten, C., Ma, W., Iwasaki, A.,
Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry,
IP(29), 2020, pp. 4568-4582.
IEEE DOI 2003
Geometry, Hyperspectral imaging, Estimation, Robustness, Analytical models, Hyperspectral imaging, nonnegative matrix factorization BibRef

Elrewainy, A.[Ahmed], Sherif, S.S.[Sherif S.],
Kronecker least angle regression for unsupervised unmixing of hyperspectral imaging data,
SIViP(14), No. 2, March 2020, pp. 359-367.
WWW Link. 2003
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Fang, B.[Bei], Bai, Y.P.[Yun-Peng], Li, Y.[Ying],
Combining Spectral Unmixing and 3D/2D Dense Networks with Early-Exiting Strategy for Hyperspectral Image Classification,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link 2003
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Borsoi, R.A., Imbiriba, T., Bermudez, J.C.M., Richard, C.,
A Blind Multiscale Spatial Regularization Framework for Kernel-Based Spectral Unmixing,
IP(29), 2020, pp. 4965-4979.
IEEE DOI 2003
Hyperspectral data, multiscale, spatial regularization, nonlinear unmixing, kernel methods BibRef

Xu, X., Li, J., Li, S., Plaza, A.,
Generalized Morphological Component Analysis for Hyperspectral Unmixing,
GeoRS(58), No. 4, April 2020, pp. 2817-2832.
IEEE DOI 2004
Blind source separation (BSS), generalized morphological component analysis (GMCA), nonnegative matrix factorization (NMF) BibRef

Rasti, B.[Behnood], Koirala, B.[Bikram], Scheunders, P.[Paul], Ghamisi, P.[Pedram],
How Hyperspectral Image Unmixing and Denoising Can Boost Each Other,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
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Dou, Z., Gao, K., Zhang, X., Wang, H., Wang, J.,
Hyperspectral Unmixing Using Orthogonal Sparse Prior-Based Autoencoder With Hyper-Laplacian Loss and Data-Driven Outlier Detection,
GeoRS(58), No. 9, September 2020, pp. 6550-6564.
IEEE DOI 2008
Hyperspectral imaging, Decoding, Image reconstruction, Gaussian distribution, Spatial resolution, Anomaly detection, spectral unmixing BibRef

Rathnayake, B.[Bhathiya], Ekanayake, E.M.M.B., Weerakoon, K., Godaliyadda, G.M.R.I., Ekanayake, M.P.B., Herath, H.M.V.R.,
Graph-Based Blind Hyperspectral Unmixing via Nonnegative Matrix Factorization,
GeoRS(58), No. 9, September 2020, pp. 6391-6409.
IEEE DOI 2008
Hyperspectral imaging, Laplace equations, Correlation, Sparse matrices, Spectral analysis, Signal processing algorithms, nonnegative matrix factorization (NMF) BibRef

Wang, W.H.[Wen-Hong], Liu, H.F.[Hong-Fu],
Deep Nonnegative Dictionary Factorization for Hyperspectral Unmixing,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
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Qian, Y., Xiong, F., Qian, Q., Zhou, J.,
Spectral Mixture Model Inspired Network Architectures for Hyperspectral Unmixing,
GeoRS(58), No. 10, October 2020, pp. 7418-7434.
IEEE DOI 2009
Artificial neural networks, Estimation, Mixture models, Network architecture, Optimization, Iterative algorithms, Training, spectral unmixing BibRef

Benachir, D.[Djaouad], Deville, Y.[Yannick], Hosseini, S.[Shahram], Karoui, M.S.[Moussa Sofiane],
Blind Unmixing of Hyperspectral Remote Sensing Data: A New Geometrical Method Based on a Two-Source Sparsity Constraint,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
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Baskurt, D.O.[Didem Ozisik], Bastanlar, Y.L.[Ya-Lin], Cetin, Y.Y.[Yasemin Yardimci],
Catadioptric hyperspectral imaging, an unmixing approach,
IET-CV(14), No. 7, October 2020, pp. 493-504.
DOI Link 2010
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Vincent, F.[François], Besson, O.[Olivier],
Non Zero Mean Adaptive Cosine Estimator and Application to Hyperspectral Imaging,
SPLetters(27), 2020, pp. 1989-1993.
IEEE DOI 2011
Hyperspectral imaging, Additives, Training, Covariance matrices, Detectors, Adaptation models, Standards, hyperspectral imaging BibRef

Zhang, G.[Guichen], Cerra, D.[Daniele], Müller, R.[Rupert],
Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012
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And: Correction: RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
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Wang, Z.C.[Zhi-Cheng], Zhuang, L.[Lina], Gao, L.[Lianru], Marinoni, A.[Andrea], Zhang, B.[Bing], Ng, M.K.[Michael K.],
Hyperspectral Nonlinear Unmixing by Using Plug-and-Play Prior for Abundance Maps,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012
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Rebeyrol, S.[Simon], Deville, Y.[Yannick], Achard, V.[Véronique], Briottet, X.[Xavier], May, S.[Stephane],
Using a Panchromatic Image to Improve Hyperspectral Unmixing,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
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Li, C., Gu, Y., Chen, X., Zhang, Y., Ruan, L.,
Hyperspectral Unmixing via Latent Multiheterogeneous Subspace,
GeoRS(59), No. 1, January 2021, pp. 563-577.
IEEE DOI 2012
Sparse matrices, Hyperspectral imaging, Feature extraction, Imaging, Image reconstruction, Additive noise, sparse analysis BibRef

Li, H., Feng, R., Wang, L., Zhong, Y., Zhang, L.,
Superpixel-Based Reweighted Low-Rank and Total Variation Sparse Unmixing for Hyperspectral Remote Sensing Imagery,
GeoRS(59), No. 1, January 2021, pp. 629-647.
IEEE DOI 2012
Hyperspectral imaging, Libraries, Correlation, TV, Sparse matrices, Hyperspectral remote sensing, reweighted low-rank constraint, total variation (TV) BibRef

Palsson, B., Ulfarsson, M.O., Sveinsson, J.R.,
Convolutional Autoencoder for Spectral-Spatial Hyperspectral Unmixing,
GeoRS(59), No. 1, January 2021, pp. 535-549.
IEEE DOI 2012
Hyperspectral imaging, Indexes, Convolutional codes, Estimation, Spatial resolution, Data mining, Hyperspectral data unmixing, image processing BibRef

Zheng, P.[Peng], Wu, Z.B.[Ze-Bin], Sun, J.[Jin], Zhang, Y.[Yi], Zhu, Y.Q.[Yao-Qin], Shen, Y.[Yuan], Yang, J.D.[Jian-Dong], Wei, Z.H.[Zhi-Hui], Plaza, A.[Antonio],
A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
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Lv, X.C.[Xiao-Chen], Wang, W.H.[Wen-Hong], Liu, H.F.[Hong-Fu],
Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
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Li, C., Jiang, Y., Chen, X.,
Hyperspectral Unmixing via Noise-Free Model,
GeoRS(59), No. 4, April 2021, pp. 3277-3291.
IEEE DOI 2104
Hyperspectral imaging, Robustness, Loss measurement, Standards, Sparse matrices, Noise measurement, nonnegative matrix tri-factorization (NMTF) BibRef

Qin, J., Lee, H., Chi, J.T., Drumetz, L., Chanussot, J., Lou, Y., Bertozzi, A.L.,
Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization,
GeoRS(59), No. 4, April 2021, pp. 3338-3351.
IEEE DOI 2104
Hyperspectral imaging, TV, Laplace equations, Convex functions, Computational modeling, Computational efficiency, Nyström method BibRef

Xu, X., Pan, B., Chen, Z., Shi, Z., Li, T.,
Simultaneously Multiobjective Sparse Unmixing and Library Pruning for Hyperspectral Imagery,
GeoRS(59), No. 4, April 2021, pp. 3383-3395.
IEEE DOI 2104
Libraries, Hyperspectral imaging, Sparse matrices, Multiple signal classification, Pareto optimization, sparse hyperspectral unmixing BibRef

Dong, L.[Le], Yuan, Y.[Yuan],
Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
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Benhalouche, F.Z.[Fatima Zohra], Deville, Y.[Yannick], Karoui, M.S.[Moussa Sofiane], Ouamri, A.[Abdelaziz],
Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106
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Zhang, J.Y.[Jing-Yan], Zhang, X.R.[Xiang-Rong], Jiao, L.C.[Li-Cheng],
Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
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Wu, K.[Ke], Chen, T.[Tao], Xu, Y.[Ying], Song, D.W.[Dong-Wei], Li, H.S.[Hai-Shan],
A Novel Change Detection Approach Based on Spectral Unmixing from Stacked Multitemporal Remote Sensing Images with a Variability of Endmembers,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
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Huang, J.[Jie], Huang, T.Z.[Ting-Zhu], Zhao, X.L.[Xi-Le], Deng, L.J.[Liang-Jian],
Nonlocal Tensor-Based Sparse Hyperspectral Unmixing,
GeoRS(59), No. 8, August 2021, pp. 6854-6868.
IEEE DOI 2108
Tensors, Hyperspectral imaging, Correlation, Estimation, Sparse matrices, Dictionaries, Hyperspectral unmixing, tensor BibRef

Koirala, B.[Bikram], Zahiri, Z.[Zohreh], Lamberti, A.[Alfredo], Scheunders, P.[Paul],
Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability,
GeoRS(59), No. 9, September 2021, pp. 7434-7448.
IEEE DOI 2109
Hyperspectral imaging, Data models, Minerals, Mathematical model, Manifolds, Training, Hyperspectral, machine learning regression, mixing models BibRef

Hua, Z.Q.[Zi-Qiang], Li, X.R.[Xiao-Run], Jiang, J.F.[Jian-Feng], Zhao, L.Y.[Liao-Ying],
Gated Autoencoder Network for Spectral-Spatial Hyperspectral Unmixing,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
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Liu, W.[Wei], He, C.X.[Cheng-Xun], Sun, L.[Le],
Spectral-Smoothness and Non-Local Self-Similarity Regularized Subspace Low-Rank Learning Method for Hyperspectral Mixed Denoising,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
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Su, Y.C.[Yuan-Chao], Xu, X.[Xiang], Li, J.[Jun], Qi, H.R.[Hai-Rong], Gamba, P.[Paolo], Plaza, A.[Antonio],
Deep Autoencoders With Multitask Learning for Bilinear Hyperspectral Unmixing,
GeoRS(59), No. 10, October 2021, pp. 8615-8629.
IEEE DOI 2109
Hyperspectral imaging, Scattering, Task analysis, Mixture models, Decoding, Photonics, Optimization, Autoencoder, bilinear mixture, multitask learning (MTL) BibRef

Zhao, M.[Min], Wang, X.[Xiuheng], Chen, J.[Jie], Chen, W.[Wei],
A Plug-and-Play Priors Framework for Hyperspectral Unmixing,
GeoRS(60), 2022, pp. 1-13.
IEEE DOI 2112
BibRef
Earlier: A2, A1, A3, Only:
Hyperspectral Unmixing Via Plug-And-Play Priors,
ICIP20(1063-1067)
IEEE DOI 2011
Hyperspectral imaging, Optimization, Task analysis, Noise reduction, Convex functions, TV, Image reconstruction, unmixing. Plugs, Convergence, Image denoising BibRef

Feng, X.X.[Xin-Xi], Han, L.[Le], Dong, L.[Le],
Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201
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Dhaini, M.[Mohamad], Berar, M.[Maxime], Honeine, P.[Paul], van Exem, A.[Antonin],
End-to-End Convolutional Autoencoder for Nonlinear Hyperspectral Unmixing,
RS(14), No. 14, 2022, pp. xx-yy.
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Hadi, F.[Fazal], Yang, J.X.[Jing-Xiang], Ullah, M.[Matee], Ahmad, I.[Irfan], Farooque, G.[Ghulam], Xiao, L.[Liang],
DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Yu, H.Y.[Hao-Yang], Chi, J.X.[Jin-Xue], Shang, X.D.[Xiao-Di], Shen, X.J.[Xue-Ji], Chanussot, J.[Jocelyn], Shi, Y.M.[Yi-Min],
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IET-IPR(16), No. 13, 2022, pp. 3557-3566.
DOI Link 2210
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Tiard, A., Condat, L., Drumetz, L., Chanussot, J., Yin, W., Zhu, X.,
Robust linear unmixing with enhanced sparsity,
ICIP17(3140-3144)
IEEE DOI 1803
Computational modeling, Cost function, Estimation, Hyperspectral imaging, Image reconstruction, Robustness, Tuning, Sparsity BibRef

Li, C.Y.[Chun-Yu], Cai, R.[Rong], Yu, J.C.[Jun-Chuan],
An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
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Sun, L.[Le], Chen, Y.[Ying], Li, B.Z.[Bao-Zhu],
SISLU-Net: Spatial Information-Assisted Spectral Information Learning Unmixing Network for Hyperspectral Images,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
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Wang, P.[Peng], Shen, X.[Xun], Kong, Y.Y.[Ying-Ying], Zhang, X.[Xiwang], Wang, L.G.[Li-Guo],
Blind Hyperspectral Unmixing with Enhanced 2DTV Regularization Term,
RS(15), No. 5, 2023, pp. xx-yy.
DOI Link 2303
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Chen, J.[Jie], Zhao, M.[Min], Wang, X.[Xiuheng], Richard, C.[Cédric], Rahardja, S.[Susanto],
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing: A summary of current methods,
SPMag(40), No. 2, March 2023, pp. 61-74.
IEEE DOI 2303
Deep learning, Analytical models, Source coding, Neural networks, Closed box, Task analysis, Nonlinear systems, Hyperspectral imaging BibRef

Su, L.J.[Li-Juan], Sui, Y.X.[Yu-Xiao], Yuan, Y.[Yan],
An Unmixing-Based Multi-Attention GAN for Unsupervised Hyperspectral and Multispectral Image Fusion,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
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Wang, J.[Jie], Xu, J.D.[Jin-Dong], Chong, Q.[Qianpeng], Liu, Z.W.[Zhao-Wei], Yan, W.Q.[Wei-Qing], Xing, H.H.[Hai-Hua], Xing, Q.[Qianguo], Ni, M.[Mengying],
SSANet: An Adaptive Spectral-Spatial Attention Autoencoder Network for Hyperspectral Unmixing,
RS(15), No. 8, 2023, pp. 2070.
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Borsoi, R.A.[Ricardo A.], Imbiriba, T.[Tales], Closas, P.[Pau],
Dynamical Hyperspectral Unmixing With Variational Recurrent Neural Networks,
IP(32), 2023, pp. 2279-2294.
IEEE DOI 2305
Stochastic processes, Hyperspectral imaging, Libraries, Bayes methods, Atmospheric modeling, Recurrent neural networks, multitemporal BibRef

Duan, S.Y.[Shi-Yao], Li, J.J.[Jiao-Jiao], Song, R.[Rui], Li, Y.S.[Yun-Song], Du, Q.[Qian],
Unmixing-Guided Convolutional Transformer for Spectral Reconstruction,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
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Su, L.J.[Li-Juan], Liu, J.[Jun], Yuan, Y.[Yan], Chen, Q.Y.[Qi-Yue],
A Multi-Attention Autoencoder for Hyperspectral Unmixing Based on the Extended Linear Mixing Model,
RS(15), No. 11, 2023, pp. 2898.
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Meng, L.H.[Ling-Hong], Liu, D.F.[Dan-Feng], Wang, L.[Liguo], Benediktsson, J.A.[Jón Atli], Yue, X.H.[Xiao-Han], Pan, Y.[Yuetao],
Augmented GBM Nonlinear Model to Address Spectral Variability for Hyperspectral Unmixing,
RS(15), No. 12, 2023, pp. xx-yy.
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Zhang, J.Y.[Jing-Yan], Zhang, X.R.[Xiang-Rong], Jiao, L.C.[Li-Cheng],
Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing,
RS(15), No. 13, 2023, pp. 3330.
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Cheng, Y.[Ying], Zhao, L.[Liaoying], Chen, S.H.[Shu-Han], Li, X.R.[Xiao-Run],
Hyperspectral Unmixing Network Accounting for Spectral Variability Based on a Modified Scaled and a Perturbed Linear Mixing Model,
RS(15), No. 15, 2023, pp. xx-yy.
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Zouaoui, A.[Alexandre], Muhawenayo, G.[Gedeon], Rasti, B.[Behnood], Chanussot, J.[Jocelyn], Mairal, J.[Julien],
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing,
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IEEE DOI 2309
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Efficient Blind Hyperspectral Unmixing Framework Based on CUR Decomposition (CUR-HU),
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Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation,
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A Robust Multilinear Mixing Model with l2,1 norm for Unmixing Hyperspectral Images,
VCIP20(193-196)
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Optimization, Robustness, Mathematical model, Signal to noise ratio, Data models, Noise measurement, 1 norm BibRef

Janiczek, J.[John], Thaker, P.[Parth], Dasarathy, G.[Gautam], Edwards, C.S.[Christopher S.], Christensen, P.[Philip], Jayasuriya, S.[Suren],
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Yao, J.[Jing], Hong, D.F.[Dan-Feng], Chanussot, J.[Jocelyn], Meng, D.Y.[De-Yu], Zhu, X.X.[Xiao-Xiang], Xu, Z.B.[Zong-Ben],
Cross-attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-resolution,
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Bauer, S., Winterbauer, E., León, F.P.,
Accelerating spectral unmixing by using clustered images,
ICIP17(3988-3992)
IEEE DOI 1803
hyperspectral imaging, image classification, meta data, pattern clustering, remote sensing, Unmixing of Clustered Image, unmixing BibRef

Zhou, Y., Rangarajan, A., Gader, P.D.[Paul D.],
Nonrigid Registration of Hyperspectral and Color Images with Vastly Different Spatial and Spectral Resolutions for Spectral Unmixing and Pansharpening,
EarthVision17(1571-1579)
IEEE DOI 1709
Color, Hyperspectral imaging, Linear programming, Registers, Spatial resolution. BibRef

Chen, X.M.[Xin-Meng], Liu, J.Y.[Ji-Ying], Zhu, J.[Jubo],
Compressive hyperspectral imaging and unmixing using spectral library,
ICIVC17(516-520)
IEEE DOI 1708
Compressed sensing, Hyperspectral imaging, Image coding, Image reconstruction, Libraries, Matrices, compressive sensing, unmixing BibRef

Li, C., Ma, Y., Gao, Y., Wang, Z., Ma, J.,
Sparse unmixing of hyperspectral data based on robust linear mixing model,
VCIP16(1-4)
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Additives BibRef

Hong, D., Yokoya, N., Chanussot, J., Zhu, X.X.,
Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing,
ICIP17(235-239)
IEEE DOI 1803
Dictionaries, Hyperspectral imaging, Machine learning, Mixture models, Optimization, Remote sensing, spectral variability BibRef

Sun, L., Jeon, B., Zheng, Y., Chen, Y.,
Hyperspectral unmixing based on L1-L2 sparsity and total variation,
ICIP16(4349-4353)
IEEE DOI 1610
Hyperspectral imaging BibRef

Meyer, T.R., Drumetz, L., Chanussot, J., Bertozzi, A.L., Jutten, C.,
Hyperspectral unmixing with material variability using social sparsity,
ICIP16(2187-2191)
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Asphalt 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)
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charcoal BibRef

Lee, W.Y.L.[William Y.L.], Andrews, M.[Mark],
Blind spectral unmixing for compressive hyperspectral imaging of highly mixed data,
ICIP14(1312-1316)
IEEE DOI 1502
Compressed sensing BibRef

Jiang, X.W.[Xin-Wei], Ma, L.[Lei], Yang, Y.P.[Yi-Ping],
Cluster constraint based sparse NMF for hyperspectral imagery unmixing,
ICIP14(5107-5111)
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Clustering algorithms BibRef

Henrot, S.[Simon], Soussen, C.[Charles], Brie, D.[David],
Sequential deconvolution: Unmixing of blurred hyperspectral data,
ICIP14(5152-5156)
IEEE DOI 1502
Convolution BibRef

Tong, L.[Lei], Zhou, J.[Jun], Bai, X.[Xiao], Gao, Y.S.[Yong-Sheng],
Dual Graph Regularized NMF for Hyperspectral Unmixing,
DICTA14(1-8)
IEEE DOI 1502
geophysical image processing BibRef

Akhtar, N.[Naveed], Sahfait, F.[Faisal], Mian, A.[Ajmal],
Repeated constrained sparse coding with partial dictionaries for hyperspectral unmixing,
WACV14(953-960)
IEEE DOI 1406
Coherence BibRef

Bendoumi, M.A.[Mohamed Amine], He, M.Y.[Ming-Yi], Mei, S.H.[Shao-Hui], Zhang, Y.F.[Yi-Fan],
Unmixing approach for hyperspectral data resolution enhancement using high resolution multispectral image,
ICARCV12(1369-1373).
IEEE DOI 1304
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Peng, H.H.[Hong-Hong], Rao, R.[Raghuveer], Dianat, S.A.[Sohail A.],
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ICIP12(2145-2148).
IEEE DOI 1302
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Arberet, S.[Simon],
Hyper-DEMIX: Blind source separation of hyperspectral images using local ML estimates,
ICIP10(1393-1396).
IEEE DOI 1009
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Dowler, S.[Shaun], Andrews, M.[Mark],
Abundance guided endmember selection: An algorithm for unmixing hyperspectral data,
ICIP10(2649-2652).
IEEE DOI 1009
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Jia, S.[Sen], Qian, Y.T.[Yun-Tao], Li, J.M.[Ji-Ming], Li, Y.[Yan], Ming, Z.[Zhong],
Hierarchical alternating least squares algorithm with Sparsity Constraint for hyperspectral unmixing,
ICIP10(2305-2308).
IEEE DOI 1009
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Luo, B.[Bin], Chanussot, J.[Jocelyn],
Unsupervised classification of hyperspectral images by using linear unmixing algorithm,
ICIP09(2877-2880).
IEEE DOI 0911
BibRef

Miao, L.[Lidan], Qi, H.R.[Hai-Rong],
A Constrained Non-Negative Matrix Factorization Approach to Unmix Highly Mixed Hyperspectral Data,
ICIP07(II: 185-188).
IEEE DOI 0709
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Berman, M.,
Some Unmixing Problems and Algorithms in Spectroscopy and Hyperspectral Imaging,
AIPR06(15-15).
IEEE DOI 0610
BibRef

Gu, Y.F.[Yan-Feng], Zhang, Y.[Ye], Liu, Y.[Ying],
Unmixing Component Analysis for Anomaly Detection in Hyperspectral Imagery,
ICIP06(965-968).
IEEE DOI 0610
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Mixed Pixels, Unmixing .


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