Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.2, pp.354-379, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00760787
A review of nonlinear hyperspectral unmixing methods, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.1844-1868, 2014. ,
Endmember variability in hyperspectral analysis: Addressing spectral variability during spectral unmixing, IEEE Signal Processing Magazine, vol.31, issue.1, pp.95-104, 2014. ,
Endmember variability in spectral unmixing: recent advances, Proc. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp.1-4, 2016. ,
Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability, IEEE Transactions on Image Processing, vol.25, issue.8, pp.3890-3905, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01336279
The enmap spaceborne imaging spectroscopy mission for earth observation, Remote Sensing, vol.7, issue.7, pp.8830-8857, 2015. ,
A hierarchical bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images, IEEE Transactions on Computational Imaging, vol.4, issue.1, pp.32-45, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-02002120
Dynamical spectral unmixing of multitemporal hyperspectral images, IEEE Transactions on Image Processing, vol.25, issue.7, pp.3219-3232, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01346918
Multisensor coupled spectral unmixing for time-series analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.55, issue.5, pp.2842-2857, 2017. ,
Data assimilation: the ensemble Kalman filter, 2009. ,
A review of operational methods of variational and ensemble-variational data assimilation, Quarterly Journal of the Royal Meteorological Society, vol.143, issue.703, pp.607-633, 2017. ,
Learning latent dynamics for partiallyobserved chaotic systems, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02274705
Vertex component analysis: a fast algorithm to unmix hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.4, pp.898-910, 2005. ,
Discovering governing equations from data by sparse identification of nonlinear dynamical systems, Proceedings of the National Academy of Sciences, vol.113, pp.3932-3937, 2016. ,
Bilinear residual neural network for the identification and forecasting of dynamical systems, 2017 European Signal Processing Conference, pp.1-4, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01686766
Residual networks as flows of diffeomorphisms, Journal of Mathematical Imaging and Vision, pp.1-11, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01796729
Hyperspectral and lidar data fusion: Outcome of the 2013 GRSS data fusion contest, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.2405-2418, 2014. ,
, Theory of reflectance and emittance spectroscopy, 2012.
Spectral unmixing: A derivation of the extended linear mixing model from the hapke model, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02434671
Em-like learning chaotic dynamics from noisy and partial observations, 2019. ,