J
Jocelyn Chanussot
Researcher at University of Grenoble
Publications - 703
Citations - 39402
Jocelyn Chanussot is an academic researcher from University of Grenoble. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 73, co-authored 614 publications receiving 27949 citations. Previous affiliations of Jocelyn Chanussot include German Aerospace Center & University of Savoy.
Papers
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Magnetic field geometry and gas kinematics in ngc2024
P. F. Goldsmith,Maryvonne Gerin,Jan H. Orkisz,C. D. Dowell,François Levrier,Jocelyn Chanussot,Pierre Chainais,Karine Demyk,M. Gaudel,Javier R. Goicoechea,Vincent Guillet,Nicolas Peretto,Antoine Roueff +12 more
Proceedings ArticleDOI
Non-Local Means Low-Rank Approximation for Hyperspectral Denoising
TL;DR: In this paper, a non-local means low-rank approximation (NLMLRA) denoising method for hyperspectral images (HSIs) is presented. But this method is based on Chebyshev polynomials.
Estimation de la Dispersion par une Analyse Multi-Signaux dans le plan Temps-Fréquence
TL;DR: In this paper, the propagation of a dispersive wave from a seismic profile is modeled by a double correction phase shift plus delay around each frequency, and a comparison on a complex synthetic profile between classical methods and the proposed method is presented.
Journal ArticleDOI
Gas kinematics around filamentary structures in the Orion B cloud
M. Gaudel,Jan H. Orkisz,Maryvonne Gerin,Jérôme Pety,Antoine Roueff,Antoine Marchal,F. Levrier,M.-A. Miville-Deschênes,Javier R. Goicoechea,Evelyne Roueff,Franck Petit,Victor de Souza Magalhaes,Pierre Palud,M. G. Santa-Maria,Maxime Vono,Sébastien Bardeau,Emeric Bron,Pierre Chainais,Jocelyn Chanussot,Pierre Gratier,Viviana V. Guzmán,Annie Hughes,Jouni Kainulainen,D. Languignon,J. Le Bourlot,Harvey S. Liszt,Karin Oberg,Nicolas Peretto,A. Sievers,Pascal Tremblin +29 more
TL;DR: In this paper , the authors used the ROHSA algorithm to decompose and de-noise the C18O(1)-0 and 13CO(1-0) signals by taking the spatial coherence of the emission into account.
Journal ArticleDOI
Spatial-spectral manifold embedding of hyperspectral data
Danfeng Hong,Danfeng Hong,Jing Yao,Jing Yao,Jing Yao,Xin Wu,Jocelyn Chanussot,Xiao Xiang Zhu,Xiao Xiang Zhu +8 more
TL;DR: In this paper, the spatial-spectral manifold embedding (SSME) method is proposed to combine spatial and spectral information in a patch-based fashion, which can reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding.