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
BibRef
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
BibRef
Wang, S.[Si],
Huang, T.Z.[Ting-Zhu],
Zhao, X.L.[Xi-Le],
Liu, G.[Gang],
Cheng, Y.[Yougan],
Double Reweighted Sparse Regression and Graph Regularization for
Hyperspectral Unmixing,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link
1808
BibRef
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.
WWW Link.
1809
BibRef
Swarna, M.,
Sowmya, V.,
Soman, K.P.,
Band selection using variational mode decomposition applied in
sparsity-based hyperspectral unmixing algorithms,
SIViP(12), No. 8, November 2018, pp. 1463-1470.
WWW Link.
1809
BibRef
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
BibRef
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
BibRef
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
Alakian, A.[Alexandre],
Achard, V.[Véronique],
Classification of Hyperspectral Reflectance Images With Physical and
Statistical Criteria,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link
2007
BibRef
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
BibRef
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.[Fahim Irfan],
Zhou, J.[Jun],
Liew, A.W.C.[Alan Wee-Chung],
Jia, X.,
Chanussot, J.,
Gao, Y.S.[Yong-Sheng],
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.[Fahim Irfan],
Zhou, J.[Jun],
Tong, L.[Lei],
Liew, A.W.C.[Alan Wee-Chung],
Gao, Y.S.[Yong-Sheng],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
And:
Correction:
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.
DOI Link
2208
BibRef
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],
Robust linear unmixing with enhanced constraint of classification for
hyperspectral remote sensing imagery,
IET-IPR(16), No. 13, 2022, pp. 3557-3566.
DOI Link
2210
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.
DOI Link
2305
BibRef
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
BibRef
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.
DOI Link
2306
BibRef
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.
DOI Link
2307
BibRef
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.
DOI Link
2307
BibRef
Cheng, Y.[Ying],
Zhao, L.Y.[Liao-Ying],
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.
DOI Link
2308
BibRef
Zouaoui, A.[Alexandre],
Muhawenayo, G.[Gedeon],
Rasti, B.[Behnood],
Chanussot, J.[Jocelyn],
Mairal, J.[Julien],
Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing,
IP(32), 2023, pp. 4649-4663.
IEEE DOI
2309
BibRef
Deville, Y.[Yannick],
Brezini, S.E.[Salah-Eddine],
Benhalouche, F.Z.[Fatima Zohra],
Karoui, M.S.[Moussa Sofiane],
Guillaume, M.[Mireille],
Lenot, X.[Xavier],
Lafrance, B.[Bruno],
Chami, M.[Malik],
Jay, S.[Sylvain],
Minghelli, A.[Audrey],
Briottet, X.[Xavier],
Serfaty, V.[Véronique],
Modeling and Unsupervised Unmixing Based on Spectral Variability for
Hyperspectral Oceanic Remote Sensing Data with Adjacency Effects,
RS(15), No. 18, 2023, pp. 4583.
DOI Link
2310
BibRef
Huang, J.X.[Jia-Xiang],
Zhang, P.Z.[Pu-Zhao],
Beyond Pixel-Wise Unmixing: Spatial-Spectral Attention Fully
Convolutional Networks for Abundance Estimation,
RS(15), No. 24, 2023, pp. 5694.
DOI Link
2401
BibRef
Abdelgawad, M.A.A.[Muhammad A. A.],
Cheung, R.C.C.[Ray C. C.],
Yan, H.[Hong],
Efficient Blind Hyperspectral Unmixing Framework Based on CUR
Decomposition (CUR-HU),
RS(16), No. 5, 2024, pp. 766.
DOI Link
2403
BibRef
Andrés-Anaya, P.[Paula],
Hernández-Herráez, G.[Gustavo],
del Pozo, S.[Susana],
Lagüela, S.[Susana],
Advanced Unmixing Methodologies for Satellite Thermal Imagery: Matrix
Changing and Classification Insights from ASTER and Landsat 8-9,
RS(16), No. 16, 2024, pp. 3067.
DOI Link
2408
BibRef
Ducasse, E.[Etienne],
Adeline, K.[Karine],
Hohmann, A.[Audrey],
Achard, V.[Véronique],
Bourguignon, A.[Anne],
Grandjean, G.[Gilles],
Briottet, X.[Xavier],
Mapping of Clay Montmorillonite Abundance in Agricultural Fields
Using Unmixing Methods at Centimeter Scale Hyperspectral Images,
RS(16), No. 17, 2024, pp. 3211.
DOI Link
2409
BibRef
Zhang, M.L.[Ming-Le],
Yang, M.Y.[Ming-Yu],
Xie, H.Y.[Hong-Yu],
Yue, P.L.[Pin-Liang],
Zhang, W.[Wei],
Jiao, Q.B.[Qing-Bin],
Xu, L.[Liang],
Tan, X.[Xin],
A Global Spatial-Spectral Feature Fused Autoencoder for Nonlinear
Hyperspectral Unmixing,
RS(16), No. 17, 2024, pp. 3149.
DOI Link
2409
BibRef
Lei, C.[Cong],
Liu, R.[Rong],
Kuang, Z.Y.[Zhi-Yuan],
Deng, R.[Ruru],
An Adaptive Unmixing Method Based on Iterative Multi-Objective
Optimization for Surface Water Fraction Mapping (IMOSWFM) Using
Zhuhai-1 Hyperspectral Images,
RS(16), No. 21, 2024, pp. 4038.
DOI Link
2411
BibRef
Li, M.,
Zhu, F.,
Guo, A.J.X.,
A Robust Multilinear Mixing Model with l2,1 norm for Unmixing
Hyperspectral Images,
VCIP20(193-196)
IEEE DOI
2102
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],
Differentiable Programming for Hyperspectral Unmixing Using a
Physics-based Dispersion Model,
ECCV20(XXVII:649-666).
Springer DOI
2011
BibRef
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,
ECCV20(XXIX: 208-224).
Springer DOI
2010
BibRef
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)
IEEE DOI
1701
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)
IEEE DOI
1610
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)
IEEE DOI
1402
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)
IEEE DOI
1502
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
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
Arberet, S.[Simon],
Hyper-DEMIX: Blind source separation of hyperspectral images using
local ML estimates,
ICIP10(1393-1396).
IEEE DOI
1009
BibRef
Dowler, S.[Shaun],
Andrews, M.[Mark],
Abundance guided endmember selection:
An algorithm for unmixing hyperspectral data,
ICIP10(2649-2652).
IEEE DOI
1009
BibRef
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
BibRef
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
BibRef
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 .