Jocelyn CHANUSSOT
Professeur Grenoble-INP
Equipe SIGnal iMAge PHYsique
Département Images et Signal
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

Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms

Lucas Drumetz, Travis Meyer, Jocelyn Chanussot, Andrea Bertozzi, Christian Jutten. Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 28 (7), pp.3435-3450. ⟨10.1109/TIP.2019.2897254⟩. ⟨hal-02138874⟩

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. 2019. ⟨hal-02123922⟩

Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability

Edurne Ibarrola-Ulzurrun, Lucas Drumetz, Javier Marcello, Consuelo Gonzalo-Martin, Jocelyn Chanussot. Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (7), pp.4775-4788. ⟨10.1109/TGRS.2019.2892903⟩. ⟨hal-02138883⟩

An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2019, 28 (4), pp.1923-1938. ⟨https://ieeexplore.ieee.org/abstract/document/8528557⟩. ⟨10.1109/TIP.2018.2878958⟩. ⟨hal-01961090⟩

Scene Classification With Recurrent Attention of VHR Remote Sensing Images

Qi Wang, Shaoteng Liu, Jocelyn Chanussot, Xuelong Li. Scene Classification With Recurrent Attention of VHR Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2019, 57 (2), pp.1155-1167. ⟨10.1109/TGRS.2018.2864987⟩. ⟨hal-01960710⟩

Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification

Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu. Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2019, 147, pp.193-205. ⟨10.1016/j.isprsjprs.2018.10.006⟩. ⟨hal-01961357⟩

Dynamic Multi-Scale Segmentation of Remote Sensing Images based on Convolutional Networks

Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William Robson Schwartz, Jefersson Alex Dos Santos. Dynamic Multi-Scale Segmentation of Remote Sensing Images based on Convolutional Networks. 2018. ⟨hal-01961078⟩

Target Recognition in SAR Image via Keypoint based Local Descriptor—Foundation

Ganggang Dong, Jocelyn Chanussot. Target Recognition in SAR Image via Keypoint based Local Descriptor—Foundation. 2018. ⟨hal-01961409⟩

Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion

Wenzhi Liao, Jocelyn Chanussot, Wilfried Philips. Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion. Mathematical Models for Remote Sensing Image Processing, 8 (3), pp.243-275, 2018. ⟨hal-01961387⟩

Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping

Wenzhi Liao, Frieke Van Coillie, Lianru Gao, Liwei Li, Bing Zhang, et al.. Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping. IEEE Access, IEEE, 2018, 6, pp.68716-68729. ⟨10.1109/ACCESS.2018.2880083⟩. ⟨hal-01960716⟩

ENCADREMENT DE THESES / PhD THESIS SUPERVISED

Grenoble Images Parole Signal Automatique laboratoire

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