D. Landgrebe, Hyperspectral image data analysis, IEEE Signal Processing Magazine, vol.19, issue.1, pp.17-28, 2002.
DOI : 10.1109/79.974718

A. Ghiyamat and H. Z. Shafri, A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment, International Journal of Remote Sensing, vol.1, issue.7, pp.1837-1856, 2010.
DOI : 10.1016/j.rse.2006.06.010

J. L. Boggs, T. D. Tsegaye, T. L. Coleman, K. C. Reddy, and A. Fahsi, Relationship Between Hyperspectral Reflectance, Soil Nitrate-Nitrogen, Cotton Leaf Chlorophyll, and Cotton Yield: A Step Toward Precision Agriculture, Journal of Sustainable Agriculture, vol.45, issue.3, pp.5-16, 2003.
DOI : 10.1080/00103629409369002

L. O. Jimenez and D. A. Landgrebe, Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.28, issue.1, pp.39-54, 1998.
DOI : 10.1109/5326.661089

G. Camps-valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods, IEEE Signal Processing Magazine, vol.31, issue.1, pp.45-54, 2014.
DOI : 10.1109/MSP.2013.2279179

J. A. Benediktsson and I. Kanellopoulos, Classification of multisource and hyperspectral data based on decision fusion, IEEE Transactions on Geoscience and Remote Sensing, vol.37, issue.3, pp.1367-1377, 1999.
DOI : 10.1109/36.763301

A. B. Santos, A. De, A. Araujo, and D. Menotti, Combining Multiple Classification Methods for Hyperspectral Data Interpretation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.3, pp.1450-1459, 2013.
DOI : 10.1109/JSTARS.2013.2251969

S. Samiappan, S. Prasad, and L. M. Bruce, Non-Uniform Random Feature Selection and Kernel Density Scoring With SVM Based Ensemble Classification for Hyperspectral Image Analysis, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.2, pp.792-800, 2013.
DOI : 10.1109/JSTARS.2013.2237757

L. I. Kuncheva and C. J. Whitaker, Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine Learning, vol.51, issue.2, pp.181-207, 2003.
DOI : 10.1023/A:1022859003006

T. K. Ho, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell, vol.20, issue.8, pp.832-844, 1998.

J. M. Yang, B. C. Kuo, P. T. Yu, and C. H. Chuang, A dynamic subspace method for hyperspectral image classification, IEEE Trans

X. Ceamanos, A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data, International Journal of Image and Data Fusion, vol.5, issue.4, pp.293-307, 2010.
DOI : 10.1109/TGRS.2008.916089

URL : https://hal.archives-ouvertes.fr/hal-00578897

K. L. Bakos and P. Gamba, Combining Hyperspectral Data Processing Chains for Robust Mapping Using Hierarchical Trees and Class Memberships, IEEE Geoscience and Remote Sensing Letters, vol.8, issue.5, pp.968-972, 2011.
DOI : 10.1109/LGRS.2011.2141651

B. B. Damodaran and R. R. Nidamanuri, Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system, Advances in Space Research, vol.53, issue.12, pp.1720-1734, 2014.
DOI : 10.1016/j.asr.2013.11.027

L. Zhang and W. Zhou, Sparse ensembles using weighted combination methods based on linear programming, Pattern Recognition, vol.44, issue.1, pp.97-106, 2011.
DOI : 10.1016/j.patcog.2010.07.021

Z. Yi, B. Samuel, and W. N. Street, Ensemble pruning via semi-definite programming, J. Mach. Learn. Res, vol.7, pp.1315-1338, 2006.

Z. H. Zhou, J. Wu, and W. Tang, Ensembling neural networks: Many could be better than all, Artificial Intelligence, vol.137, issue.1-2, pp.239-263, 2002.
DOI : 10.1016/S0004-3702(02)00190-X

B. B. Damodaran and R. R. Nidamanuri, Dynamic Linear Classifier System for Hyperspectral Image Classification for Land Cover Mapping, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.6, pp.2080-2093, 2014.
DOI : 10.1109/JSTARS.2013.2294857

P. Gurram and H. Kwon, Sparse Kernel-Based Ensemble Learning With Fully Optimized Kernel Parameters for Hyperspectral Classification Problems, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.2, pp.787-802, 2013.
DOI : 10.1109/TGRS.2012.2203603

K. Kirchhoff and J. A. Bilmes, Dynamic classifier combination in hybrid speech recognition systems using utterance-level confidence values, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), pp.693-696, 1999.
DOI : 10.1109/ICASSP.1999.759761

R. Singh, M. Vatsa, and A. Noore, Multiclass mv-granular soft support vector machine: A case study in dynamic classifier selection for multispectral face recognition, 2008 19th International Conference on Pattern Recognition, pp.1-4, 2008.
DOI : 10.1109/ICPR.2008.4761877

L. Didaci, G. Giacinto, F. Roli, and G. L. Marcialis, A study on the performances of dynamic classifier selection based on local accuracy estimation, Pattern Recognition, vol.38, issue.11, pp.2188-2191, 2005.
DOI : 10.1016/j.patcog.2005.02.010

K. Woods, W. P. Kegelmeyer, and K. Bowyer, Combination of multiple classifiers using local accuracy estimates, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.4, pp.405-410, 1997.
DOI : 10.1109/34.588027

P. C. Smits, Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.4, pp.801-813, 2002.
DOI : 10.1109/TGRS.2002.1006354

T. Woloszynski and M. Kurzynski, A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recognition, vol.44, issue.10-11, pp.2656-2668, 2011.
DOI : 10.1016/j.patcog.2011.03.020

P. Du, J. Xia, J. Chanussot, and X. He, Hyperspectral remote sensing image classification based on the integration of support vector machine and random forest, 2012 IEEE International Geoscience and Remote Sensing Symposium, pp.174-177, 2012.
DOI : 10.1109/IGARSS.2012.6351609

URL : https://hal.archives-ouvertes.fr/hal-00799693

G. B. Huang, Q. Y. Zhu, and C. K. Siew, Extreme learning machine: Theory and applications, Neurocomputing, vol.70, issue.1-3, pp.489-501, 2006.
DOI : 10.1016/j.neucom.2005.12.126

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.3692

Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.8, pp.2973-2987, 2009.
DOI : 10.1109/TGRS.2009.2016214

M. Fauvel, J. Chanussot, and J. A. Benediktsson, A spatial???spectral kernel-based approach for the classification of remote-sensing images, Pattern Recognition, vol.45, issue.1, pp.381-392, 2012.
DOI : 10.1016/j.patcog.2011.03.035

URL : https://hal.archives-ouvertes.fr/hal-00798504

G. Camps-valls, L. Gomez-chova, J. Munoz-mari, J. Vila-frances, and J. Calpe-maravilla, Composite Kernels for Hyperspectral Image Classification, IEEE Geoscience and Remote Sensing Letters, vol.3, issue.1, pp.93-97, 2006.
DOI : 10.1109/LGRS.2005.857031

M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, Advances in Spectral-Spatial Classification of Hyperspectral Images, Proc. IEEE, pp.652-675, 2013.
DOI : 10.1109/JPROC.2012.2197589

URL : https://hal.archives-ouvertes.fr/hal-00737075

Y. Tarabalka, M. Fauvel, J. Chanussot, and J. A. Benediktsson, SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, vol.7, issue.4, pp.736-740, 2010.
DOI : 10.1109/LGRS.2010.2047711

URL : https://hal.archives-ouvertes.fr/hal-00578864

P. Ghamisi, J. A. Benediktsson, and M. O. Ulfarsson, Spectral–Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.5, pp.2565-2574, 2014.
DOI : 10.1109/TGRS.2013.2263282

L. Xu and J. Li, Bayesian classification of hyperspectral imagery based on probabilistic sparse representation and Markov random field, IEEE Geosci. Remote Sens. Lett, vol.11, issue.4, pp.823-827, 2014.

J. Bai, S. Xiang, and C. Pan, A Graph-Based Classification Method for Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.2, pp.803-817, 2013.
DOI : 10.1109/TGRS.2012.2205002

X. He, J. Xia, and P. Du, MRF-based multiple classifier system for hyperspectral remote sensing image classification, Multiple Classifier Systems, pp.343-351, 2013.

M. Khodadadzadeh, Spectral–Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.10, pp.6298-6314, 2014.
DOI : 10.1109/TGRS.2013.2296031

A. Plaza, Recent advances in techniques for hyperspectral image processing, Remote Sensing of Environment, vol.113, pp.110-122, 2009.
DOI : 10.1016/j.rse.2007.07.028

URL : https://hal.archives-ouvertes.fr/hal-00178888

G. Camps-valls and L. Bruzzone, Kernel-based methods for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.6, pp.1351-1362, 2005.
DOI : 10.1109/TGRS.2005.846154

J. Kittler, M. Hatef, R. P. Duin, and J. Matas, On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.226-239, 1998.
DOI : 10.1109/34.667881

A. Samat, P. Du, S. Liu, J. Li, and L. Cheng, <formula formulatype="inline"><tex Notation="TeX">${{\rm E}^{2}}{\rm LMs}$</tex> </formula>: Ensemble Extreme Learning Machines for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.4, pp.1060-1069, 2014.
DOI : 10.1109/JSTARS.2014.2301775

M. A. Bencherif, Fusion of Extreme Learning Machine and Graph-Based Optimization Methods for Active Classification of Remote Sensing Images, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.3, pp.527-531, 2015.
DOI : 10.1109/LGRS.2014.2349538

G. Moser, S. B. Serpico, and J. A. Benediktsson, Land-cover mapping by Markov modeling of spatial?contextual information in veryhigh-resolution remote sensing images, Proc. IEEE, pp.631-651, 2013.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-2727, 2011.
DOI : 10.1145/1961189.1961199

G. Thoonen, Z. Mahmood, S. Peeters, and P. Scheunders, Multisource Classification of Color and Hyperspectral Images Using Color Attribute Profiles and Composite Decision Fusion, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.2, pp.510-521, 2012.
DOI : 10.1109/JSTARS.2011.2168317