Nicolas Dobigeon mainly investigates Hyperspectral imaging, Artificial intelligence, Pattern recognition, Algorithm and Endmember. His Hyperspectral imaging research integrates issues from Pixel, Bayesian probability and Robustness. His study in Prior probability, Gibbs sampling and Posterior probability is carried out as part of his studies in Artificial intelligence.
His Pattern recognition study combines topics in areas such as Image processing, Multispectral image and Hyperparameter. His Algorithm research incorporates themes from Monte Carlo method, Mathematical optimization, Computer vision and Hybrid Monte Carlo. His Endmember study frequently draws connections to other fields, such as Spectral signature.
Nicolas Dobigeon spends much of his time researching Artificial intelligence, Hyperspectral imaging, Pattern recognition, Algorithm and Pixel. His research on Artificial intelligence frequently connects to adjacent areas such as Computer vision. His Hyperspectral imaging research is multidisciplinary, incorporating elements of Spectral signature and Multispectral image.
His Pattern recognition research focuses on Estimator and how it relates to Applied mathematics. His Algorithm research includes themes of Image processing and Mathematical optimization. His Pixel research incorporates elements of Image and Spatial analysis.
Nicolas Dobigeon mostly deals with Artificial intelligence, Hyperspectral imaging, Pattern recognition, Pixel and Algorithm. His study looks at the intersection of Artificial intelligence and topics like Computer vision with Infrared. His work carried out in the field of Hyperspectral imaging brings together such families of science as Spectral signature, Multispectral image and Mixing.
Nicolas Dobigeon has researched Pattern recognition in several fields, including Sampling, Image and Cluster analysis. The concepts of his Pixel study are interwoven with issues in Spatial analysis, Data cube and Sample. Nicolas Dobigeon has included themes like Image processing and Markov chain Monte Carlo in his Algorithm study.
His primary areas of investigation include Hyperspectral imaging, Bayesian inference, Artificial intelligence, Algorithm and Pattern recognition. His Hyperspectral imaging study combines topics from a wide range of disciplines, such as Pixel and Spectral signature. His work is dedicated to discovering how Bayesian inference, Markov chain Monte Carlo are connected with Gibbs sampling and other disciplines.
Many of his research projects under Artificial intelligence are closely connected to Spectral resolution with Spectral resolution, tying the diverse disciplines of science together. Nicolas Dobigeon combines subjects such as Probability and statistics, Auxiliary variables and Robustness with his study of Algorithm. His studies deal with areas such as Image and Latent variable as well as Pattern recognition.
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
J. M. Bioucas-Dias;A. Plaza;N. Dobigeon;M. Parente.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2012)
Hyperspectral Pansharpening: A Review
Laetitia Loncan;Luis B. de Almeida;Jose M. Bioucas-Dias;Xavier Briottet.
IEEE Geoscience and Remote Sensing Magazine (2015)
Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation
Qi Wei;Jose Bioucas-Dias;Nicolas Dobigeon;Jean-Yves Tourneret.
IEEE Transactions on Geoscience and Remote Sensing (2015)
Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model
A. Halimi;Y. Altmann;N. Dobigeon;J. Tourneret.
IEEE Transactions on Geoscience and Remote Sensing (2011)
Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms
Nicolas Dobigeon;Jean-Yves Tourneret;Cedric Richard;Jose Carlos M. Bermudez.
IEEE Signal Processing Magazine (2014)
Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery
N. Dobigeon;S. Moussaoui;M. Coulon;J.-Y. Tourneret.
IEEE Transactions on Signal Processing (2009)
Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery
N. Dobigeon;J.-Y. Tourneret;Chein-I Chang.
IEEE Transactions on Signal Processing (2008)
Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
Qi Wei;Nicolas Dobigeon;Jean-Yves Tourneret.
IEEE Transactions on Image Processing (2015)
Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery
Y. Altmann;A. Halimi;N. Dobigeon;J. Tourneret.
IEEE Transactions on Image Processing (2012)
Temporal Dynamics of Host Molecular Responses Differentiate Symptomatic and Asymptomatic Influenza A Infection
Yongsheng Huang;Aimee K. Zaas;Arvind Rao;Nicolas Dobigeon.
PLOS Genetics (2011)
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