Hyperspectral image data analysis, IEEE Signal Processing Magazine, vol.19, issue.1, pp.17-28, 2002. ,
DOI : 10.1109/79.974718
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
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
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
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
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
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
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
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
The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell, vol.20, issue.8, pp.832-844, 1998. ,
A dynamic subspace method for hyperspectral image classification, IEEE Trans ,
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
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
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
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
Ensemble pruning via semi-definite programming, J. Mach. Learn. Res, vol.7, pp.1315-1338, 2006. ,
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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. ,
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
MRF-based multiple classifier system for hyperspectral remote sensing image classification, Multiple Classifier Systems, pp.343-351, 2013. ,
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
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
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
On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.226-239, 1998. ,
DOI : 10.1109/34.667881
<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
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
Land-cover mapping by Markov modeling of spatial?contextual information in veryhigh-resolution remote sensing images, Proc. IEEE, pp.631-651, 2013. ,
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
LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-2727, 2011. ,
DOI : 10.1145/1961189.1961199
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