Hyperspectral Remote Sensing Data Analysis and Future Challenges, IEEE Geoscience and Remote Sensing Magazine, vol.1, issue.2, pp.6-36, 2013. ,
DOI : 10.1109/MGRS.2013.2244672
URL : http://www.lx.it.pt/~bioucas/files/ieee_grsm_2013_hyper_rs_data_analysis.pdf
Spectral unmixing, IEEE Signal Processing Magazine, vol.19, issue.1, pp.44-57, 2002. ,
DOI : 10.1109/79.974727
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. ,
DOI : 10.1109/JSTARS.2012.2194696
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. ,
DOI : 10.1109/JSTARS.2014.2320576
Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms, IEEE Signal Processing Magazine, vol.31, issue.1, pp.82-94, 2014. ,
DOI : 10.1109/MSP.2013.2279274
URL : https://hal.archives-ouvertes.fr/hal-00915663
Endmember variability in Spectral Mixture Analysis: A review, Remote Sensing of Environment, vol.115, issue.7, pp.1603-1616, 2011. ,
DOI : 10.1016/j.rse.2011.03.003
Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6190-6194, 2017. ,
DOI : 10.1109/ICASSP.2017.7953346
URL : https://hal.archives-ouvertes.fr/hal-01581521
Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images, IEEE Transactions on Image Processing, vol.25, issue.7, pp.3219-3232, 2016. ,
DOI : 10.1109/TIP.2016.2562562
URL : https://hal.archives-ouvertes.fr/hal-01346918
Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing, IEEE Signal Processing Magazine, vol.31, issue.1, pp.95-104, 2014. ,
DOI : 10.1109/MSP.2013.2279177
Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.2, pp.396-408, 2012. ,
DOI : 10.1109/JSTARS.2011.2181340
URL : http://www.umbc.edu/rssipl/people/aplaza/Papers/Journals/2012.JSTARS.Bundles.pdf
Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability, IEEE Transactions on Image Processing, vol.24, issue.12, pp.4904-4917, 2015. ,
DOI : 10.1109/TIP.2015.2471182
URL : http://arxiv.org/pdf/1406.5071
Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model, IEEE Transactions on Signal Processing, vol.64, issue.2, pp.525-538, 2016. ,
DOI : 10.1109/TSP.2015.2486746
URL : https://hal.archives-ouvertes.fr/hal-01273078
A new extended linear mixing model to address spectral variability, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2014. ,
DOI : 10.1109/WHISPERS.2014.8077595
URL : https://hal.archives-ouvertes.fr/hal-01010424
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. ,
DOI : 10.1109/TIP.2016.2579259
URL : https://hal.archives-ouvertes.fr/hal-01336279
Theory of reflectance and emittance spectroscopy, 2012. ,
Endmember variability in hyperspectral image unmixing, 2016. ,
URL : https://hal.archives-ouvertes.fr/tel-01394809
Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp.2015-2016, 2015. ,
DOI : 10.1109/whispers.2015.8075417
URL : https://hal.archives-ouvertes.fr/hal-01336279
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. ,
DOI : 10.1109/TGRS.2005.844293
URL : http://www.lx.it.pt/~bioucas/files/ieeegrsVca04.pdf
Clustering on the unit hypersphere using von Mises-Fisher distributions, Journal of Machine Learning Research, vol.6, pp.1345-1382, 2005. ,
Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, pp.250-253, 2008. ,
DOI : 10.1109/IGARSS.2008.4779330
URL : http://www.lx.it.pt/~bioucas/files/igarss08.pdf
ICE: a statistical approach to identifying endmembers in hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.10, pp.2085-2095, 2004. ,
DOI : 10.1109/TGRS.2004.835299
URL : http://www.ecel.ufl.edu/~barnes/downloads/berman04ice.pdf
Optimization algorithms on matrix manifolds, 2009. ,
DOI : 10.1515/9781400830244
Fast projection onto the simplex and the L1 ball, Mathematical Programming, pp.1-11, 2014. ,
DOI : 10.1007/s10107-015-0946-6
URL : https://hal.archives-ouvertes.fr/hal-01056171
Manopt, a Matlab toolbox for optimization on manifolds, Journal of Machine Learning Research, vol.15, pp.1455-1459, 2014. ,
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.3, pp.529-545, 2001. ,
DOI : 10.1109/36.911111
Hyperspectral Tree Species Classification of Japanese Complex Mixed Forest With the Aid of Lidar Data, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.5, pp.2177-2187, 2015. ,
DOI : 10.1109/JSTARS.2015.2417859