H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 37 Citations 8,761 156 World Ranking 5267 National Ranking 121

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Algorithm

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.

His most cited work include:

  • Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (1760 citations)
  • Hyperspectral Pansharpening: A Review (354 citations)
  • Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation (315 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (65.25%)
  • Hyperspectral imaging (53.67%)
  • Pattern recognition (54.44%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (65.25%)
  • Hyperspectral imaging (53.67%)
  • Pattern recognition (54.44%)

In recent papers he was focusing on the following fields of study:

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.

Between 2017 and 2021, his most popular works were:

  • Hyperspectral Unmixing With Spectral Variability Using Adaptive Bundles and Double Sparsity (17 citations)
  • Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: A Fusion-Based Approach (17 citations)
  • Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization (15 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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)

1985 Citations

Hyperspectral Pansharpening: A Review

Laetitia Loncan;Luis B. de Almeida;Jose M. Bioucas-Dias;Xavier Briottet.
IEEE Geoscience and Remote Sensing Magazine (2015)

462 Citations

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)

388 Citations

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)

368 Citations

Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms

Nicolas Dobigeon;Jean-Yves Tourneret;Cedric Richard;Jose Carlos M. Bermudez.
IEEE Signal Processing Magazine (2014)

366 Citations

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)

358 Citations

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)

234 Citations

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)

207 Citations

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)

193 Citations

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)

192 Citations

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