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
URL : http://arxiv.org/abs/1310.5107
Dynamic Ensemble Selection Approach for Hyperspectral Image Classification With Joint Spectral and Spatial Information, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, pp.2405-2417, 2015. ,
DOI : 10.1109/JSTARS.2015.2407493
URL : https://hal.archives-ouvertes.fr/hal-01184338
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
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
Feature Selection for Classification of Hyperspectral Data by SVM, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.5, pp.2297-2307, 2010. ,
DOI : 10.1109/TGRS.2009.2039484
Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, vol.93, pp.112-122, 2014. ,
DOI : 10.1016/j.isprsjprs.2014.04.006
Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.4, pp.1185-1198, 2012. ,
DOI : 10.1109/TGRS.2011.2165957
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.643.7399
An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection, IEEE Transactions on Geoscience and Remote Sensing, vol.33, issue.6, pp.1318-1321, 1995. ,
DOI : 10.1109/36.477187
Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, pp.2824-2831, 2015. ,
DOI : 10.1109/JSTARS.2015.2441771
A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.1, pp.1-15, 2015. ,
DOI : 10.1109/TGRS.2015.2450759
Band Selection Using Improved Sparse Subspace Clustering for Hyperspectral Imagery Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, pp.2784-2797, 2015. ,
DOI : 10.1109/JSTARS.2015.2417156
Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.1, pp.544-557, 2016. ,
DOI : 10.1109/TGRS.2015.2461653
A fast separability-based feature-selection method for high-dimensional remotely sensed image classification, Pattern Recognition, vol.41, issue.5, pp.1653-1662, 2008. ,
DOI : 10.1016/j.patcog.2007.11.007
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.300.6734
Feature selection via dependence maximization, J. Mach. Learn. Res, vol.13, issue.1, pp.1393-1434, 2012. ,
Unsupervised Hyperspectral Image Band Selection via Column Subset Selection, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.7, pp.1411-1415, 2015. ,
DOI : 10.1109/LGRS.2015.2404772
Constrained band selection for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.6, pp.1575-1585, 2006. ,
DOI : 10.1109/TGRS.2006.864389
Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.7, pp.1552-1565, 2004. ,
DOI : 10.1109/TGRS.2004.830549
Genetic algorithms and Linear Discriminant Analysis based dimensionality reduction for remotely sensed image analysis, 2011 IEEE International Geoscience and Remote Sensing Symposium ,
DOI : 10.1109/IGARSS.2011.6049687
Remote Sensing Feature Selection by Kernel Dependence Measures, IEEE Geoscience and Remote Sensing Letters, vol.7, issue.3, pp.587-591, 2010. ,
DOI : 10.1109/LGRS.2010.2041896
The Jeffries?Matusita distance for the case of complex Wishart distribution as a separability criterion for fully polarimetric SAR data, Int. J. Remote Sens, vol.35, issue.19, pp.6859-6873, 2014. ,
Visual Method for Spectral Band Selection, IEEE Geoscience and Remote Sensing Letters, vol.1, issue.2, pp.101-106, 2004. ,
DOI : 10.1109/LGRS.2003.822879
The role of feature selection in artificial neural network applications, International Journal of Remote Sensing, vol.56, issue.15, pp.2919-2937, 2002. ,
DOI : 10.1080/014311699212119
Introductory digital image processing: A remote sensing perspective, Geocarto International, vol.2, issue.1, 1996. ,
DOI : 10.1080/10106048709354084
PerTurbo Manifold Learning Algorithm for Weakly Labeled Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, issue.4, pp.1070-1078, 2014. ,
DOI : 10.1109/JSTARS.2014.2304304
Kernel-Based Domain-Invariant Feature Selection in Hyperspectral Images for Transfer Learning, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.5, pp.2615-2626, 2016. ,
DOI : 10.1109/TGRS.2015.2503885
High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso, Neural Computation, vol.12, issue.1, pp.185-207, 2014. ,
DOI : 10.1111/j.1467-9868.2005.00503.x
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.1121
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001. ,
Covariate shift in Hilbert space: A solution via surrogate kernels, Proc. 30th Int. Conf, pp.1-8, 2013. ,
Measuring Statistical Dependence with Hilbert-Schmidt Norms, Algorithmic Learning Theory (Lecture Notes in Computer Science), pp.63-77, 2005. ,
DOI : 10.1007/11564089_7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.477
A Hilbert Space Embedding for Distributions, Algorithmic Learning Theory, pp.13-31, 2007. ,
DOI : 10.1073/pnas.0601231103
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.8341
Non-parametric mixture models for clustering, " in Structural, Syntactic, and Statistical Pattern Recognition, Lecture Notes in Computer Science), pp.334-343, 2010. ,
DOI : 10.1007/978-3-642-14980-1_32
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.6782
On the Sample Complexity of Random Fourier Features for Online Learning, ACM Transactions on Knowledge Discovery from Data, vol.8, issue.3, pp.13-14, 2014. ,
DOI : 10.1145/2611378
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
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.8134
Extreme Learning Machine for Regression and Multiclass Classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.2, pp.513-529, 2012. ,
DOI : 10.1109/TSMCB.2011.2168604
Extreme Learning Machine With Composite Kernels for Hyperspectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, pp.2351-2360, 2015. ,
DOI : 10.1109/JSTARS.2014.2359965
<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
Bayesian network classifiers, Mach. Learn, vol.29, pp.2-3, 1997. ,
Extreme learning machine: Theory and applications, Neurocomputing, vol.70, issue.1-3, pp.1-3, 2006. ,
DOI : 10.1016/j.neucom.2005.12.126
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.3692
Extended profiles with morphological attribute filters for the analysis of hyperspectral data, International Journal of Remote Sensing, vol.2, issue.22, pp.5975-5991, 2010. ,
DOI : 10.1080/01431160500300354
Morphological Attribute Profiles for the Analysis of Very High Resolution Images, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.10, pp.3747-3762, 2010. ,
DOI : 10.1109/TGRS.2010.2048116
Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis, IEEE Geoscience and Remote Sensing Letters, vol.8, issue.3, pp.542-546, 2011. ,
DOI : 10.1109/LGRS.2010.2091253
URL : https://hal.archives-ouvertes.fr/hal-00578886
How to scale up kernel methods to be as good as deep neural nets Available: https://arxiv, p.4000, 1411. ,
The Probabilistic Basis of Jaccard's Index of Similarity, Systematic Biology, vol.45, issue.3, pp.380-385, 1996. ,
DOI : 10.1093/sysbio/45.3.380
A stability index for feature selection, Artificial Intelligence and Applications, pp.390-395, 2007. ,
Stable and Accurate Feature Selection, Machine Learning and Knowledge Discovery in Databases, pp.455-468, 2009. ,
DOI : 10.1109/TSA.2005.860352
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.4739
degree in remote sensing and wireless sensor networks from Amrita Vishwa Vidyapeetham, Coimbatore, in 2010, and the Ph.D. degree in earth and space sciences from the Indian Institute of Space Science and Technology He is currently a Post-Doctoral Researcher with the OBELIX Team His research interests include feature selection, large scale kernel learning, multiple classifier system, hyperspectral/multispectral image analysis, machine learning, and image processing, Dr. Damodaran has been awarded the Prestige and Marie Curie Post-Doctoral Fellowship by the campus France, 2008. ,