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Unmixing multitemporal hyperspectral images accounting for endmember variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Toumeret, Steve McLaughlin, Paul Honeine
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability of the endmembers.  ...  A prior enforcing a smooth temporal variation of both endmembers and abundances is considered.  ...  CONCLUSIONS This paper proposed an unsupervised Bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability.  ... 
doi:10.1109/eusipco.2015.7362665 dblp:conf/eusipco/HalimiDTMH15 fatcat:wm2gb4whgvc3faehftta3b2lai

Unmixing Multitemporal Hyperspectral Images Accounting For Endmember Variability

Nicolas Dobigeon, Abderrahim Halimi, Paul Honeine, Steve McLaughlin, J.-Y. Tourneret
2015 Zenodo  
Publication in the conference proceedings of EUSIPCO, Nice, France, 2015  ...  CONCLUSIONS This paper proposed an unsupervised Bayesian algorithm for unmixing successive hyperspectral images while accounting for temporal and spatial variability.  ...  INTRODUCTION Spectral unmixing (SU) consists of identifying the macroscopic materials present in an hyperspectral image (HI) (called endmembers) and their proportions (called abundances).  ... 
doi:10.5281/zenodo.38913 fatcat:6jtavstgybatrao57dxlfaamwq

Hyperspectral unmixing accounting for spatial correlations and endmember variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret, Paul Honeine
2015 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)  
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability.  ...  Simulations conducted on a real dataset show the potential of the proposed model in terms of unmixing performance for the analysis of hyperspectral images.  ...  INTRODUCTION Unmixing hyperspectral (HS) images consists of decomposing a pixel spectrum into a combination of pure constituent spectra, or endmembers, and a set of corresponding fractions, or abundances  ... 
doi:10.1109/whispers.2015.8075442 dblp:conf/whispers/HalimiDTH15 fatcat:6s6sxw6o4fdjnhjxma3hfvhwx4

Enhancing Hyperspectral Image Unmixing With Spatial Correlations

Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourneret
2011 IEEE Transactions on Geoscience and Remote Sensing  
This paper describes a new algorithm for hyperspectral image unmixing.  ...  These parameters include the abundances for each pixel, the means and variances of the abundances for each class, as well as a classification map indicating the classes of all pixels in the image.  ...  ACKNOWLEDGMENTS The authors would like to thank one of the reviewers for pointing out the relevant paper [12] and for his valuable suggestions that helped to improve the manuscript.  ... 
doi:10.1109/tgrs.2011.2140119 fatcat:cc77sjcmt5a7za4dtacaez3sby

A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret, Paul Honeine
2015 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability.  ...  Simulations conducted with synthetic and real data show the potential of the proposed model and the unmixing performance for the analysis of hyperspectral images.  ...  INTRODUCTION Spectral unmixing (SU) consists of decomposing a pixel spectrum as a linear combination of physical materials contained in a hyperspectral (HS) image, known as endmembers, and of estimating  ... 
doi:10.1109/icassp.2015.7178415 dblp:conf/icassp/HalimiDTH15 fatcat:53mhh3pxvneptertdawxv53fqm

A Hierarchical Bayesian Model Accounting for Endmember Variability and Abrupt Spectral Changes to Unmix Multitemporal Hyperspectral Images [article]

Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
2017 arXiv   pre-print
More specifically, multi-temporal hyperspectral images, i.e., sequences of hyperspectral images acquired over the same area at different time instants, are likely to simultaneously exhibit moderate endmember  ...  In this context, we propose a new unmixing model for multitemporal hyperspectral images accounting for smooth temporal variations, construed as spectral variability, and abrupt spectral changes interpreted  ...  Mailhes, and J.-Y. Tourneret, "Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery," IEEE Trans.  ... 
arXiv:1609.07792v4 fatcat:oomwwrpthfg3nn6uz4qz2awriq

Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret
2015 IEEE Transactions on Image Processing  
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability.  ...  The proposed algorithm exploits the whole image to provide spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image.  ...  CONCLUSIONS This paper introduced a Bayesian model for unsupervised unmixing of hyperspectral images accounting for spectral variability.  ... 
doi:10.1109/tip.2015.2471182 pmid:26302517 fatcat:lwgecnrmlje5jch56fgwgysyny

A Hierarchical Bayesian Model Accounting for Endmember Variability and Abrupt Spectral Changes to Unmix Multitemporal Hyperspectral Images

Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
2018 IEEE Transactions on Computational Imaging  
More specifically, multitemporal hyperspectral images, i.e., sequences of hyperspectral images acquired over the same area at different time instants, are likely to simultaneously exhibit moderate endmember  ...  In this context, we propose a new unmixing model for multitemporal hyperspectral images accounting for smooth temporal variations, construed as spectral variability, and abrupt spectral changes interpreted  ...  This paper studies a new Bayesian model allowing both spectral variability and presence of outliers to be considered in the unmixing of MTHS images.  ... 
doi:10.1109/tci.2017.2777484 fatcat:eo7orklctjenbjb5u5efcjcwia

Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity, or Mismodeling Effects

Abderrahim Halimi, Paul Honeine, Jose M. Bioucas-Dias
2016 IEEE Transactions on Image Processing  
This paper presents three hyperspectral mixture models jointly with Bayesian algorithms for supervised hyperspectral unmixing.  ...  The ME formulation takes into account the effect of outliers and copes with some types of EV and NL. The known constraints on the parameter of each observation model are modeled via suitable priors.  ...  CONCLUSIONS This paper introduced three hyperspectral mixture models and their associated Bayesian algorithms for supervised hyperspectral unmixing.  ... 
doi:10.1109/tip.2016.2590324 pmid:27416597 fatcat:hudaq2zifbbk3i2ttjjvkl45au

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

José M. Bioucas-Dias, Antonio Plaza, Nicolas Dobigeon, Mario Parente, Qian Du, Paul Gader, Jocelyn Chanussot
2012 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel.  ...  Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size.  ...  Green and the AVIRIS team for making the Rcuprite hyperspectral data set available to the community, and the United States Geological Survey (USGS) for their publicly available library of mineral signatures  ... 
doi:10.1109/jstars.2012.2194696 fatcat:s66a35xjd5dqzkw5wwihq6ux64

Toward a Sparse Bayesian Markov Random Field Approach to Hyperspectral Unmixing and Classification

Peng Chen, James D. B. Nelson, Jean-Yves Tourneret
2017 IEEE Transactions on Image Processing  
We here propose a new Bayesian approach to joint hyperspectral unmixing and image classification such that the previous assumption of stochastic abundance vectors is relaxed to a formulation whereby a  ...  This allows us to avoid stochastic reparameterizations and, instead, we propose a symmetric Dirichlet distribution model with adjustable parameters for the common abundance vector of each class.  ...  Olivier Eches for sharing code and implementation advice.  ... 
doi:10.1109/tip.2016.2622401 pmid:27810822 fatcat:agwx4zun5ng6xfpumwm6jmuc5y

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [article]

José M. Bioucas-Dias, Antonio Plaza, Nicolas Dobigeon, Mario Parente, Qian Du, Paul Gader, Jocelyn Chanussot
2012 arXiv   pre-print
Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel.  ...  Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size.  ...  Green and the AVIRIS team for making the Rcuprite hyperspectral data set available to the community, and the United States Geological Survey (USGS) for their publicly available library of mineral signatures  ... 
arXiv:1202.6294v2 fatcat:4vxq62jxvzfynpb75wvvhw4phq

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment  ...  The paper is comprehensive in coverage of both hyperspectral image analysis tasks and machine learning algorithms.  ...  This method used sticky hierarchical DP as spatial prior for the abundances in a Bayesian linear unmixing framework and Gibbs sampling to infer the posterior distributions of the endmembers and the abundances  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression

L. Drumetz, G. Tochon, M.A. Veganzones, J. Chanussot, C. Jutten
2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Local Spectral Unmixing (LSU) methods perform the unmixing of hyperspectral data locally in regions of the image.  ...  The endmembers and their abundances in each pixel are extracted region-wise, instead of globally to mitigate spectral variability effects, which are less severe locally.  ...  INTRODUCTION Spectral Unmixing (SU) is one of the most important applications in hyperspectral imaging for remote sensing [1] .  ... 
doi:10.1109/icassp.2017.7953346 dblp:conf/icassp/DrumetzTVCJ17 fatcat:twkrewsp2jav5epek62sh5qsu4

Exploration of Planetary Hyperspectral Images with Unsupervised Spectral Unmixing: A Case Study of Planet Mars

Jun Liu, Bin Luo, Sylvain Douté, Jocelyn Chanussot
2018 Remote Sensing  
We propose to replace traditional spectral index methods by unsupervised spectral unmixing methods for the exploration of large datasets of planetary hyperspectral images.  ...  We examine the sensitivity of a series of state-of-the-art methods of unmixing to the intrinsic spectral variability of the species in the image and to intimate assemblages of compounds.  ...  Three hyperspectral mixture models jointly with Bayesian algorithms for supervised hyperspectral unmixing have been introduced in [28] .  ... 
doi:10.3390/rs10050737 fatcat:3rglcjng6jdyrk3ko3smgdzugq
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