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 ⟩

Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry

Lucas Drumetz, Jocelyn Chanussot, Christian Jutten, Wing-Kin Ma, Akira Iwasaki. Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨ 10.1109/TIP.2020.2974062 ⟩. ⟨ hal-02490602 ⟩

Fusion of hyperspectral and panchromatic data by spectral unmixing in the reflective domainFusion de données hyperspectrales et panchromatique par démélange spectral dans le domaine réflectif

Yohann Constans, Sophie Fabre, Henry Brunet, Michael Seymour, Vincent Crombez, et al.. Fusion of hyperspectral and panchromatic data by spectral unmixing in the reflective domain. 2020. ⟨ hal-02443504 ⟩

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 ⟩

Chapter 3.1 - Applications in remote sensing—natural landscapes

Touria Bajjouk, Florian de Boissieu, Jocelyn Chanussot, Sylvain Dout, Marie Dumont, et al.. Chapter 3.1 - Applications in remote sensing—natural landscapes. Data Handling in Science and Technology, 32, Elsevier Ltd, pp.371-410, 2020, ⟨ 10.1016/B978-0-444-63977-6.00016-X ⟩. ⟨ hal-02477896 ⟩

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 ⟩

Applications in remote sensing—natural landscapes

Touria Bajjouk, Florian de Boissieu, Jocelyn Chanussot, Sylvain Douté, Marie Dumont, et al.. Applications in remote sensing—natural landscapes. Hyperspectral Imaging, pp.371-410, 2020, ⟨ 10.1016/b978-0-444-63977-6.00016-x ⟩. ⟨ hal-02592498 ⟩

Fusion of hyperspectral imaging and LiDAR for forest monitoring

Eduardo Tusa, Anthony Laybros, Jean-Matthieu Monnet, Mauro Dalla Mura, Jean-Baptiste Barré, et al.. Fusion of hyperspectral imaging and LiDAR for forest monitoring. Data Handling in Science and Technology, 32, Elsevier, pp.281-303, 2020, Data Handling in Science and Technology, 978-0-444-63977-6. ⟨ 10.1016/B978-0-444-63977-6.00013-4 ⟩. ⟨ hal-02443395 ⟩

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 ⟩

ENCADREMENT DE THESES / PhD THESIS SUPERVISED

Grenoble Images Parole Signal Automatique laboratoire

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