Jocelyn CHANUSSOT
Professeur Grenoble-INP
Equipe SIGnal iMAge PHYsique
Département Images et Signal
En délégation au 01/09/2019 au 31/08/2021
ME CONTACTER / CONTACT ME
Mail : jocelyn.chanussot@gipsa-lab.grenoble-inp.fr

11 rue des mathématiques
Domaine Universitaire
BP 46
38402 Saint Martin d'Hères cedex

Bureau D1136
Tél.33 (0)4 76 82 62 73
Fax : 33 (0)4 76 57 47 90
PUBLICATIONS RECENTES / RECENT PUBLICATIONS
Les derniéres publications de la collection Gipsa dans HAL

C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud: An analysis of the precision achievable when modeling spectral line within the Local Thermodynamic Equilibrium approximation

Antoine Roueff, Maryvonne Gerin, Pierre Gratier, François Levrier, Jérôme Pety, et al.. C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud: An analysis of the precision achievable when modeling spectral line within the Local Thermodynamic Equilibrium approximation. 2020. ⟨ hal-02570214 ⟩

Nonlocal Coupled Tensor CP Decomposition for Hyperspectral and Multispectral Image Fusion

Yang Xu, Zebin Wu, Jocelyn Chanussot, Pierre Comon, Zhihui Wei. Nonlocal Coupled Tensor CP Decomposition for Hyperspectral and Multispectral Image Fusion. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2020, 58 (1), pp.348-362. ⟨ 10.1109/TGRS.2019.2936486 ⟩. ⟨ hal-02123922 ⟩

Spectral Unmixing: A Derivation of the Extended Linear Mixing Model from the Hapke Model

Lucas Drumetz, Jocelyn Chanussot, Christian Jutten. Spectral Unmixing: A Derivation of the Extended Linear Mixing Model from the Hapke Model. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, In press, ⟨ 10.1109/LGRS.2019.2958203 ⟩. ⟨ hal-02434671 ⟩

Braids of partitions for the hierarchical representation and segmentation of multimodal images

Guillaume Tochon, Mauro Dalla Mura, Miguel Angel Veganzones, Thierry Géraud, Jocelyn Chanussot. Braids of partitions for the hierarchical representation and segmentation of multimodal images. Pattern Recognition, Elsevier, 2019, 95, pp.162-172. ⟨ 10.1016/j.patcog.2019.05.029 ⟩. ⟨ hal-02307542 ⟩

Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

Xin Wu, Danfeng Hong, Jocelyn Chanussot, Yang Xu, Ran Tao, et al.. Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection. 2019. ⟨ hal-02307442 ⟩

An Introduction to Deep Morphological Networks

Keiller Nogueira, Jocelyn Chanussot, Mauro Dalla Mura, William Robson Schwartz, Jefersson A. Dos Santos. An Introduction to Deep Morphological Networks. 2019. ⟨ hal-02307437 ⟩

Assessment of Hyperspectral Sharpening Methods for the Monitoring of Natural Areas Using Multiplatform Remote Sensing Imagery

Javier Marcello, Edurne Ibarrola-Ulzurrun, Consuelo Gonzalo-Martin, Jocelyn Chanussot, Gemine Vivone. Assessment of Hyperspectral Sharpening Methods for the Monitoring of Natural Areas Using Multiplatform Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (10), pp.8208-8222. ⟨ 10.1109/TGRS.2019.2918932 ⟩. ⟨ hal-02307510 ⟩

Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks

Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William Robson Schwartz, Jefersson Alex dos Santos. Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (10), pp.7503-7520. ⟨ 10.1109/TGRS.2019.2913861 ⟩. ⟨ hal-02307468 ⟩

An Improved Stationarity Test Based on Surrogates

Douglas Baptista de Souza, Jocelyn Chanussot, Anne-Catherine Favre, Pierre Borgnat. An Improved Stationarity Test Based on Surrogates. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2019, 26 (10), pp.1431-1435. ⟨ 10.1109/LSP.2019.2931150 ⟩. ⟨ hal-02307460 ⟩

A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data

Maxime Vono, Javier Goicoechea, Pierre Gratier, Viviana Guzman, Annie Hughes, et al.. A Fully Bayesian Approach For Inferring Physical Properties With Credibility Intervals From Noisy Astronomical Data. 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS 2019 ), Sep 2019, Amsterdam, Netherlands. pp.1-5, ⟨ 10.1109/WHISPERS.2019.8920859 ⟩. ⟨ hal-02569471 ⟩

ENCADREMENT DE THESES / PhD THESIS SUPERVISED

Grenoble Images Parole Signal Automatique laboratoire

UMR 5216 CNRS - Grenoble INP - Université Joseph Fourier - Université Stendhal