Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches, IEEE J. Sel. Topics Appl. Earth Observ. in Remote Sens, vol.5, pp.354-379, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00760787
Recent advances in techniques for hyperspectral image processing, Remote Sens. Environ, vol.113, pp.110-122, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00178888
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery, IEEE Trans. Signal Process, vol.57, pp.4355-4368, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00455585
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE Trans. Geosci. Remote Sens, vol.39, pp.529-545, 2001. ,
Advances in hyperspectral image classification: Earth monitoring with statistical learning methods, IEEE Signal Process. Mag, vol.31, pp.45-54, 2014. ,
Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis, IEEE Geosci. Remote Sens. Lett, vol.8, pp.542-546, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00578886
Advances in spectral-spatial classification of hyperspectral images, Proc. IEEE, vol.101, pp.652-675, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00737075
Hyperspectral image classification with independent component discriminant analysis, IEEE Trans. Geosci. Remote Sens, vol.49, pp.4865-4876, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00607195
A new semi-supervised algorithm for hyperspectral image classification based on spectral unmixing concepts, Proc. IEEE GRSS Workshop Hyperspectral Image SIgnal Process.: Evolution in Remote Sens. (WHISPERS), pp.1-4, 2011. ,
Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images, IEEE Trans. Geosci. Remote Sens, vol.53, pp.2899-2912, 2015. ,
Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral images, IEEE Trans. Image Process, vol.22, pp.5-16, 2013. ,
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing, IEEE Trans. Geosci. Remote Sens, vol.54, issue.8, pp.4775-4789, 2016. ,
A study of the effect of different types of noise on the precision of supervised learning techniques, Artif. Intell. Rev, vol.33, pp.275-306, 2010. ,
Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series, Remote Sens, vol.9, p.173, 2017. ,
Robust supervised classification with mixture models: Learning from data with uncertain labels, Pattern Recognit, vol.42, pp.2649-2658, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-00325263
, Markov Random Field Modeling in Image Analysis, 2009.
The potts model, Rev. Mod. Phys, vol.54, p.235, 1982. ,
URL : https://hal.archives-ouvertes.fr/hal-02340560
Monte Carlo Statistical Methods, ser. Springer Texts in Statistics, 2004. ,
Hierarchical Bayesian image analysis: From low-level modeling to robust supervised learning, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01545393
Discriminant Analysis by Gaussian Mixtures, J. Roy. Stat. Soc. Ser. B, vol.58, pp.155-176, 1996. ,
Vertex component analysis: A fast algorithm to unmix hyperspectral data, IEEE Trans. Geosci. Remote Sens, vol.43, pp.898-910, 2005. ,
Identifying learners robust to low quality data, Proc. IEEE Int. Conf. on Inf. Reuse and Integr, pp.190-195, 2008. ,