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
Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability, IEEE Transactions on Image Processing, vol.24, issue.12, 2014. ,
DOI : 10.1109/TIP.2015.2471182
Vertex Component Analysis, IEEE Transactions on, vol.43, issue.4, pp.898-910, 2005. ,
DOI : 10.1201/9781420003130.ch19
Automated extraction of image-based endmember bundles for improved spectral unmixing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.5, issue.2, pp.396-408, 2012. ,
On Endmember Identification in Hyperspectral Images Without Pure Pixels: A Comparison of Algorithms, Journal of Mathematical Imaging and Vision, vol.42, issue.3, pp.163-175, 2012. ,
DOI : 10.1007/s10851-011-0276-0
On spectral clustering: Analysis and an algorithm Advances in neural information processing systems, pp.849-856, 2002. ,
Does independent component analysis play a role in unmixing hyperspectral data? Geoscience and Remote Sensing, IEEE Transactions on, vol.43, issue.1, pp.175-187, 2005. ,
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 Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.5, issue.2, pp.354-379, 2012. ,
Hyperspectral imaging: techniques for spectral detection and classification Spectral unmixing via data-guided sparsity, Science & Business Media, pp.5412-5427, 2003. ,
A compressive sensing and unmixing scheme for hyperspectral data processing, Image Processing IEEE Transactions on, vol.21, issue.3, pp.1200-1210, 2012. ,
Hyperspectral unmixing via sparsityconstrained nonnegative matrix factorization Geoscience and Remote Sensing, IEEE Transactions on, vol.49, issue.11, pp.4282-4297, 2011. ,
Social Sparsity! Neighborhood Systems Enrich Structured Shrinkage Operators, IEEE Transactions on Signal Processing, vol.61, issue.10, pp.2498-2511, 2013. ,
DOI : 10.1109/TSP.2013.2250967
URL : https://hal.archives-ouvertes.fr/hal-00691774
Distributed optimization and statistical learning via the alternating direction method of multipliers Foundations and Trends Fast algorithms for nonconvex compressive sensing: Mri reconstruction from very few data, Machine Learning Biomedical Imaging: From Nano to Macro ISBI'09. IEEE International Symposium on. IEEE, pp.1-122, 2009. ,
Semi-realistic simulations of natural hyperspectral scenes, Geoscience and Remote Sensing Symposium (IGARSS), pp.1004-1007, 2015. ,
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, p.2015, 2015. ,
DOI : 10.1109/TIP.2016.2579259
URL : https://hal.archives-ouvertes.fr/hal-01165114
Hyperspectral image segmentation using a new spectral unmixing-based binary partition tree representation, Image Processing IEEE Transactions on, vol.23, issue.8, pp.3574-3589, 2014. ,
Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing, IEEE Transactions on Image Processing, vol.24, issue.12, 2014. ,
DOI : 10.1109/TIP.2015.2487862