2012 - IEEE Fellow For contributions to data fusion and image processing for remote sensing
Her primary areas of investigation include Artificial intelligence, Hyperspectral imaging, Pattern recognition, Contextual image classification and Image resolution. Her Artificial intelligence course of study focuses on Computer vision and Matching. The concepts of her Hyperspectral imaging study are interwoven with issues in Image processing, Subspace topology and Support vector machine.
Her Pattern recognition research integrates issues from Mathematical morphology and Spatial analysis. Her Contextual image classification research includes themes of Full spectral imaging, Feature vector and Linear discriminant analysis. Her Multispectral image research incorporates elements of Multiresolution analysis, Feature learning and Sensor fusion.
Jocelyn Chanussot mainly focuses on Artificial intelligence, Hyperspectral imaging, Pattern recognition, Computer vision and Remote sensing. Artificial intelligence is represented through her Image resolution, Pixel, Multispectral image, Feature extraction and Contextual image classification research. Her research integrates issues of Spectral resolution, Image fusion, Convolutional neural network and Superresolution in her study of Image resolution.
Her Multispectral image research is multidisciplinary, relying on both Subspace topology and Sensor fusion. Her biological study spans a wide range of topics, including Image processing, Algorithm, Spectral signature and Spatial analysis. Pattern recognition is closely attributed to Image in her research.
The scientist’s investigation covers issues in Artificial intelligence, Hyperspectral imaging, Pattern recognition, Image resolution and Multispectral image. In Artificial intelligence, Jocelyn Chanussot works on issues like Graph, which are connected to Laplacian matrix. Her Hyperspectral imaging study necessitates a more in-depth grasp of Remote sensing.
Her Remote sensing research focuses on subjects like Earth observation, which are linked to Transfer of learning. Her Pattern recognition study combines topics from a wide range of disciplines, such as Matrix decomposition, Embedding, Feature and Graph. She combines subjects such as Iterative reconstruction and Superresolution with her study of Image resolution.
Jocelyn Chanussot mostly deals with Hyperspectral imaging, Artificial intelligence, Pattern recognition, Image resolution and Multispectral image. Her Hyperspectral imaging research incorporates themes from Pixel, Algorithm, Spectral signature, Iterative reconstruction and Sensor fusion. Her Artificial intelligence study frequently draws connections to other fields, such as Graph.
Her Pattern recognition study incorporates themes from Image processing and Feature. Her Image resolution study integrates concerns from other disciplines, such as Regularization, Sparse matrix and Superresolution. In her study, Earth observation and Transfer of learning is inextricably linked to Remote sensing, which falls within the broad field of Deep learning.
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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)
Recent Advances in Techniques for Hyperspectral Image Processing
Antonio Plaza;Jon Atli Benediktsson;Joseph W. Boardman;Jason Brazile.
Remote Sensing of Environment (2009)
Hyperspectral Remote Sensing Data Analysis and Future Challenges
J. M. Bioucas-Dias;A. Plaza;G. Camps-Valls;P. Scheunders.
IEEE Geoscience and Remote Sensing Magazine (2013)
Advances in Spectral-Spatial Classification of Hyperspectral Images
M. Fauvel;Y. Tarabalka;J. A. Benediktsson;J. Chanussot.
Proceedings of the IEEE (2013)
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
M. Fauvel;J.A. Benediktsson;J. Chanussot;J.R. Sveinsson.
IEEE Transactions on Geoscience and Remote Sensing (2008)
Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest
L.. Alparone;L.. Wald;J.. Chanussot;C.. Thomas.
IEEE Transactions on Geoscience and Remote Sensing (2007)
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques
Y. Tarabalka;J.A. Benediktsson;J. Chanussot.
IEEE Transactions on Geoscience and Remote Sensing (2009)
A Critical Comparison Among Pansharpening Algorithms
Gemine Vivone;Luciano Alparone;Jocelyn Chanussot;Mauro Dalla Mura.
IEEE Transactions on Geoscience and Remote Sensing (2015)
SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images
Y Tarabalka;M Fauvel;J Chanussot;J A Benediktsson.
IEEE Geoscience and Remote Sensing Letters (2010)
Segmentation and classification of hyperspectral images using watershed transformation
Y. Tarabalka;J. Chanussot;J. A. Benediktsson.
Pattern Recognition (2010)
University of Iceland
Grenoble Alpes University
University of Tokyo
University of Extremadura
Ministry of Natural Resources and Forestry
Instituto Superior Técnico
German Aerospace Center
University of Pavia
University of Iceland
Universitat Politècnica de Catalunya
Profile was last updated on December 6th, 2021.
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