BUsing vegetation reflectance variability for species level classification of hyperspectral data, Int. J ,
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
Ash decline assessment in emerald ash borer-infested regions: A test of tree-level, hyperspectral technologies, Remote Sensing of Environment, vol.112, issue.5, pp.2665-2676, 2008. ,
DOI : 10.1016/j.rse.2007.12.011
Review Article Hyperspectral geological remote sensing: evaluation of analytical techniques, International Journal of Remote Sensing, vol.26, issue.12, pp.2215-2242, 1996. ,
DOI : 10.1029/JB092iB02p01441
Multisensor approach to determine changes of wetland characteristics in semiarid environments (central Spain), IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.11, pp.2516-2525, 2005. ,
DOI : 10.1109/TGRS.2005.852082
BHyperspectral data segmentation and classification in precision agriculture: A multi-scale analysis, Proc. IEEE Geosci. Remote Sens. Symp, pp.585-588, 2008. ,
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
BHyperspectral image processing for automatic target detection applications,[ Lincoln Lab, J, vol.14, issue.1, pp.79-116, 2003. ,
Multispectral land sensing: where from, where to?, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.3, pp.414-421, 2005. ,
DOI : 10.1109/TGRS.2004.837327
Hyperspectral Imaging Techniques for Spectral Detection and Classification, 2003. ,
Signal Theory Methods in Multispectral Remote Sensing, 2003. ,
DOI : 10.1002/0471723800
A Course in the Geometry of n-Dimensions, 1961. ,
BSupervised classification in high-dimensional space: Geometrical, statistical, and asymptotical properties of multivariate data, IEEE Trans. Syst ,
BHigh-dimensional data analysis: The curses and blessing of dimensionality, AMS Math. Challenges 21st Century, pp.1-32, 2000. ,
BOn the mean accuracy of statistical pattern recognizers ,
Dimension Reduction: A Guided Tour, Foundations and Trends?? in Machine Learning, vol.2, issue.4, pp.275-365, 2010. ,
DOI : 10.1561/2200000002
Investigation of the random forest framework for classification of hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.3, pp.492-501, 2005. ,
DOI : 10.1109/TGRS.2004.842481
Semisupervised Neural Networks for Efficient Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.5, pp.2271-2282, 2010. ,
DOI : 10.1109/TGRS.2009.2037898
Kernel Methods for Remote Sensing Data Analysis, 2009. ,
DOI : 10.1002/9780470748992
BEvaluation of kernels for multiclass classification of hyperspectral remote sensing data, Proc. IEEE Int, pp.10-1109, 2006. ,
Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.8, pp.1778-1790, 2004. ,
DOI : 10.1109/TGRS.2004.831865
BClassification of high dimensional data with limited training samples, Electr. Comput. Eng, 1998. ,
Classification of Remote Sensing Images From Urban Areas Using a Fuzzy Possibilistic Model, IEEE Geoscience and Remote Sensing Letters, vol.3, issue.1, pp.40-44, 2006. ,
DOI : 10.1109/LGRS.2005.856117
URL : https://hal.archives-ouvertes.fr/hal-00096363
BSpatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data, IEEE J. Sel. Top. Appl. Earth Observat. Remote Sens, vol.52, pp.380-395, 2012. ,
Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects, IEEE Transactions on Geoscience Electronics, vol.14, issue.1, pp.19-26, 1976. ,
DOI : 10.1109/TGE.1976.294460
BAdaptive Bayesian contextual classification based on Markov random fields ,
DOI : 10.1109/tgrs.2002.805087
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.7590
BStochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. Pattern Anal. Mach. Intell, vol.6, issue.6, pp.721-741, 1984. ,
Estimating Gaussian Markov random field parameters in a nonstationary framework: application to remote sensing imaging, IEEE Transactions on Image Processing, vol.8, issue.4, pp.490-503, 1999. ,
DOI : 10.1109/83.753737
BManaging the spectral-spatial mix in context classification using Markov random fields ,
Advances in Spectral?Spatial Classification of Hyperspectral Images [31] Y. Boykov and G. Funka-Lea, BGraph cuts and efficient ND image segmentation, Int. J. Comput. Vis, vol.70, pp.109-131, 2006. ,
What energy functions can be minimized via graph cuts?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.147-159, 2004. ,
DOI : 10.1109/TPAMI.2004.1262177
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.1823
SAR Image Regularization With Fast Approximate Discrete Minimization, IEEE Transactions on Image Processing, vol.18, issue.7, pp.1588-1600, 2009. ,
DOI : 10.1109/TIP.2009.2019302
URL : https://hal.archives-ouvertes.fr/ujm-00380535
BAnt colony optimization for image regularization based on a nonstationary Markov modeling, IEEE Trans. Image Process, vol.16, issue.3, pp.865-878, 2007. ,
BAdaptive Markov random field approach for classification of hyperspectral imagery, IEEE Geosci. Remote Sens. Lett, vol.8, issue.5, pp.973-977, 2011. ,
BSpatial techniques for image classification,[ in Signal and Image Processing for Remote Sensing, pp.491-513, 2006. ,
BSpectral-spatial based super pixel remote sensing image classification, Proc. 4th Int. Congr. Image Signal Process, pp.1680-1684, 2011. ,
BClassification and feature extraction for remote sensing images from urban areas based on morphological transformations, IEEE Trans. Geosci ,
Classification of hyperspectral data from urban areas based on extended morphological profiles, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.3, pp.480-491, 2005. ,
DOI : 10.1109/TGRS.2004.842478
A Dempster–Shafer Relaxation Approach to Context Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.5, pp.1422-1431, 2007. ,
DOI : 10.1109/TGRS.2007.893821
A patch???based image classification by integrating hyperspectral data with GIS, International Journal of Remote Sensing, vol.11, issue.15, pp.3337-3346, 2006. ,
DOI : 10.1117/12.317797
Segmentation and classification of hyperspectral images using watershed transformation, Pattern Recognition, vol.43, issue.7, pp.2367-2379, 2010. ,
DOI : 10.1016/j.patcog.2010.01.016
URL : https://hal.archives-ouvertes.fr/hal-00578860
BSpectral-spatial classification of hyperspectral imagery based on partitional clustering techniques, IEEE Trans. Geosci ,
BMultiple spectral-spatial classification approach for hyperspectral data, IEEE Trans. Geosci. Remote Sens, vol.48, issue.11, pp.4122-4132, 2010. ,
BBest merge region growing segmentation with integrated non-adjacent region object aggregation ,
DOI : 10.1109/tgrs.2012.2190079
Morphological Image Analysis, Principles and Applications, 2003. ,
Segmentation and Classification of Hyperspectral Images Using Minimum Spanning Forest Grown From Automatically Selected Markers, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.40, issue.5, pp.1267-1279, 2010. ,
DOI : 10.1109/TSMCB.2009.2037132
URL : https://hal.archives-ouvertes.fr/hal-00578848
Covariance estimation with limited training samples, IEEE Transactions on Geoscience and Remote Sensing, vol.37, issue.4, pp.2113-2118, 1999. ,
DOI : 10.1109/36.774728
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.81.906
Advances in mathematical morphology applied to geoscience and remote sensing, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.9, pp.2042-2055, 2002. ,
DOI : 10.1109/TGRS.2002.804618
BRecent developments in morphological image processing for remote sensing, Proc. SPIEVInt. Soc. Opt. Eng, vol.7477, pp.747702-747703, 2009. ,
BTheoretical aspects of morphological filters by reconstruction,[ Signal Process, pp.201-225, 1995. ,
A new approach for the morphological segmentation of high-resolution satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.2, pp.309-320, 2001. ,
DOI : 10.1109/36.905239
Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.11, pp.3866-3879, 2009. ,
DOI : 10.1109/TGRS.2009.2027895
Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.10, pp.2803-2813, 2008. ,
DOI : 10.1109/TGRS.2008.2000628
Classification of Hyperspectral Data Over Urban Areas Using Directional Morphological Profiles and Semi-Supervised Feature Extraction, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.4, pp.1177-1190 ,
DOI : 10.1109/JSTARS.2012.2190045
A comparative study on multivariate mathematical morphology, Pattern Recognition, vol.40, issue.11, pp.2914-2929, 2007. ,
DOI : 10.1016/j.patcog.2007.02.004
URL : https://hal.archives-ouvertes.fr/hal-00512660
A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles, Pattern Recognition, vol.37, issue.6, pp.1097-1116, 2004. ,
DOI : 10.1016/j.patcog.2004.01.006
Remote Sensing Digital Image Analysis, 2006. ,
BClassification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis, Proc. IEEE Int. Geosci. Remote Sens. Symp, pp.176-179, 2005. ,
BKernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas, EURASIP J. Adv. Signal Process, pp.1-14, 2009. ,
BOn the influence of feature reduction for the classification of hyperspectral images based on the extended morphological profile, Int. J ,
Beyond self-duality in morphological image analysis, Image and Vision Computing, vol.23, issue.2, pp.249-257, 2005. ,
DOI : 10.1016/j.imavis.2004.06.002
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
BOn the use of morphological alternated sequential filters for the classification of remote sensing images from urban areas, Proc. IEEE Geosci ,
General Adaptive Neighborhood Image Processing:, Journal of Mathematical Imaging and Vision, vol.13, issue.6, pp.245-266, 2006. ,
DOI : 10.1007/s10851-006-7451-8
URL : https://hal.archives-ouvertes.fr/hal-00128123
General Adaptive Neighborhood Image Processing, Journal of Mathematical Imaging and Vision, vol.13, issue.6, pp.267-284, 2006. ,
DOI : 10.1007/s10851-006-7452-7
URL : https://hal.archives-ouvertes.fr/hal-00128123
BVector median filters, Proc. IEEE, pp.678-689, 1990. ,
The Nature of Statistical Learning Theory, 1999. ,
Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.8, pp.1778-1790, 2004. ,
DOI : 10.1109/TGRS.2004.831865
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
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.11, pp.3804-3814, 2008. ,
DOI : 10.1109/TGRS.2008.922034
URL : https://hal.archives-ouvertes.fr/hal-00283769
BFeature selection for support vector machines,[ Advances in Neural Information Processing Systems 13, pp.668-674, 2001. ,
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
BAdaptive pixel neighborhood definition for the classification of hyperspectral images with support vector machines and composite kernel, Proc. 15th IEEE Int. Conf. Image Process, pp.1884-1887, 2008. ,
Advances in Spectral?Spatial Classification of Hyperspectral Images The Elements of Statistical Learning: Data Mining, Inference, and Prediction: With 200 Full-Color Illustrations, 2001. ,
Antiextensive connected operators for image and sequence processing, IEEE Transactions on Image Processing, vol.7, issue.4, pp.555-570, 1998. ,
DOI : 10.1109/83.663500
BMorphological attribute profiles for the analysis of very high resolution images, IEEE Trans. Geosci ,
BThe evolution of the morphological profile: From panchromatic to hyperspectral images, Optical Remote Sensing, pp.123-146, 2011. ,
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
BHyperspectral image segmentation using binary partition trees, Proc. IEEE Int. Conf. Image Process, pp.1273-1276, 2011. ,
Iterative area filtering of multichannel images, Image and Vision Computing, vol.25, issue.8, pp.1352-1364, 2007. ,
DOI : 10.1016/j.imavis.2006.09.002
Learning Relevant Image Features With Multiple-Kernel Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.10, pp.3780-3791, 2010. ,
DOI : 10.1109/TGRS.2010.2049496
URL : http://my.unil.ch/serval/document/BIB_2289989BB4C6.pdf
Urban Image Classification With Semisupervised Multiscale Cluster Kernels, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.4, issue.1, pp.65-74, 2011. ,
DOI : 10.1109/JSTARS.2010.2069085
A survey on image segmentation, Pattern Recognition, vol.13, issue.1, pp.3-16, 1981. ,
DOI : 10.1016/0031-3203(81)90028-5
BImage segmentation by region growing and spectral clustering with a natural convergence criterion, Proc. Int ,
A classification EM algorithm for clustering and two stochastic versions, Computational Statistics & Data Analysis, vol.14, issue.3, pp.315-332, 1992. ,
DOI : 10.1016/0167-9473(92)90042-E
URL : https://hal.archives-ouvertes.fr/inria-00075196
SEM algorithm and unsupervised statistical segmentation of satellite images, IEEE Transactions on Geoscience and Remote Sensing, vol.31, issue.3, pp.618-633, 1993. ,
DOI : 10.1109/36.225529
BUse of watersheds in contour detection,[ presented at the Int. Workshop Image Process. Real-Time Edge Motion Detection/ Estimation, 1979. ,
MORPHOLOGICAL SEGMENTATION OF HYPERSPECTRAL IMAGES, Image Analysis & Stereology, vol.26, issue.3, pp.101-109, 2007. ,
DOI : 10.5566/ias.v26.p101-109
URL : https://hal.archives-ouvertes.fr/hal-01220414
A morphological gradient approach to color edge detection, IEEE Transactions on Image Processing, vol.15, issue.6, pp.1454-1463, 2006. ,
DOI : 10.1109/TIP.2005.864164
Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.6, pp.583-598, 1991. ,
DOI : 10.1109/34.87344
Computer Vision, 2002. ,
Fast Hyperspectral Feature Reduction Using Piecewise Constant Function Approximations, IEEE Geoscience and Remote Sensing Letters, vol.4, issue.4, pp.547-551, 2007. ,
DOI : 10.1109/LGRS.2007.896331
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.3602
Hierarchy in picture segmentation: a stepwise optimization approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.2, pp.150-163, 1989. ,
DOI : 10.1109/34.16711
RHSeg User's Manual: Including HSWO, 2012. ,
BAutomated selection of results in hierarchical segmentations of remotely sensed hyperspectral images, Proc. Int. Geosci ,
BMarker-based hierarchical segmentation and classification approach for hyperspectral imagery, Proc. Int. Conf. Acoust. Speech Signal Process, pp.1089-1092, 2011. ,
A Marker-Based Approach for the Automated Selection of a Single Segmentation From a Hierarchical Set of Image Segmentations, Proc. 23rd Asian Conf. Remote Sens, pp.262-272, 2002. ,
DOI : 10.1109/JSTARS.2011.2173466
URL : https://hal.archives-ouvertes.fr/hal-00729001
BClassifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines, J. Appl. Remote Sens, vol.1, issue.013543, pp.1-17, 2007. ,
LIBSVMVA Library for Support Vector Machines, 2008. ,
BProbability estimates for multi-class classification by pairwise coupling, J. Mach. Learn. Res, issue.5, pp.975-1005, 2004. ,
Multiple classifiers applied to multisource remote sensing data, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.10, pp.2291-2299, 2002. ,
DOI : 10.1109/TGRS.2002.802476
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.323.8445
On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.226-239, 1998. ,
DOI : 10.1109/34.667881
BMathematical morphology and graphs: Application to interactive medical image segmentation, Ctr. Math. Morphology, Paris School of Mines, 2008. ,
Shortest Connection Networks And Some Generalizations, BShortest connection networks and some generalizations, pp.1389-1401, 1957. ,
DOI : 10.1002/j.1538-7305.1957.tb01515.x
Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach, IEEE Transactions on Image Processing, vol.21, issue.4, pp.2008-2021, 2012. ,
DOI : 10.1109/TIP.2011.2175741
Thematic Map Comparison, Photogrammetric Engineering & Remote Sensing, vol.70, issue.5, pp.627-633, 2004. ,
DOI : 10.14358/PERS.70.5.627
Feature extraction based on decision boundaries, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.4, pp.388-400, 1993. ,
DOI : 10.1109/34.206958
BA robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction, IEEE Trans. Geosci ,
he was a Postdoctoral Research Associate with the MISTIS Team of the National Institute for Research in Computer Science and Control (INRIA) Since 2010, he has been an Assistant Professor with the National Polytechnic Institute of Toulouse (ENSAT-University of Toulouse) within the DYNAFOR lab (University of Toulouse-INRA), Castanet- Tolosan, France. His research interests are remote sensing, data fusion, pattern recognition, multicomponent signal, and image processing, 2008. ,
Advances in Spectral?Spatial Classification of Hyperspectral Images | Proceedings of the, IEEE, vol.23 ,
degree in electrical engineering from Purdue University, West Lafayette , IN, in 1987 and 1990, respectively. Currently, he is a Pro Rector for Academic Affairs and Professor of Electrical and Computer Engineering at the University of Iceland. He is a cofounder of the biomedical startup company Oxymap. His research interests are in remote sensing, biomedical analysis of signals, pattern recognition, image processing, and signal processing, and he has published extensively in those fields, IEEE) received the Cand.Sci. degree in electrical engineering from the University of Iceland Prof. Benediktsson is the 2011?2012 President of the IEEE Geoscience and Remote Sensing Society (GRSS) and has been on the GRSS AdCom since 1999. He was Editor of the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (TGRS) from, 1984. ,
he was with the Geography Imagery Perception Laboratory for the Delegation Generale de l'Armement (DGA-French National Defense Department) Since 1999, he has been with Grenoble INP, where he was an Assistant Professor from 1999 to 2005, an Associate Professor from 2005 to 2007, and is currently a Professor of Signal and Image Processing. He is currently conducting his research at the Grenoble Images Speech Signals and Automatics Laboratory (GIPSA- Lab) His research interests include image analysis, multicomponent image processing, nonlinear filtering, and data fusion in remote sensing. Dr. Chanussot is the founding President of the IEEE Geoscience and Remote Sensing Society French chapter, 1995 and the Ph.D. degree in electrical engineering from Savoie University which received the 2010 IEEE GRS-S Chapter Excellence Award Bfor excellence as a Geoscience and Remote Sensing Society chapter He was the recipient of the NORSIG 2006 Best Student Paper Award, the IEEE GRSS 2011 Symposium Best Paper Award, and the IEEE GRSS 2012 Transactions Prize Paper Award, 1998. ,
he has been an Associate Editor for the Since 2011, he has been the Editor-in- Chief of the, Since IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2007. ,
Currently, he is a Computer Engineer with the Computational and Information Sciences and Technology Office (CISTO) of the Science and Exploration Directorate at the NASA Goddard Space Flight Center As a member of CISTO, he is responsible for designing and developing computer software tools for space and Earth science image analysis, and encouraging the use of these computer tools through interactions with space and Earth scientists. His software development has resulted in two patents and two other patent applications. Dr. Tilton is a Senior Member of the IEEE Geoscience and Remote Sensing Society (GRSS). From, 1976, the M.S. degree in optical sciences from the University of Arizona 1978, and the Ph.D. degree in electrical engineering from Purdue University, 1981. ,
Advances in Spectral-Spatial Classification of Hyperspectral Images, Proceedings of the IEEE, vol.101, issue.3 ,
DOI : 10.1109/JPROC.2012.2197589
URL : https://hal.archives-ouvertes.fr/hal-00737075