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

Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation

Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, Xiuping Jia, Jocelyn Chanussot, et al.. Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation. Submitted for Journal (Version 2). 2018. 〈hal-01687733〉

4DCAF: A temporal approach for denoising hyperspectral image sequences

Blanca Priego, Richard Duro, Jocelyn Chanussot. 4DCAF: A temporal approach for denoising hyperspectral image sequences. Pattern Recognition, Elsevier, 2017, 72, pp.433 - 445. 〈10.1016/j.patcog.2017.07.023〉. 〈hal-01687059〉

Multimorphological Superpixel Model for Hyperspectral Image Classification

Tianzhu Liu, Yanfeng Gu, Jocelyn Chanussot, Mauro Dalla Mura. Multimorphological Superpixel Model for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (12), pp.6950 - 6963. 〈10.1109/TGRS.2017.2737037〉. 〈hal-01665290〉

Multiple Kernel Learning for Hyperspectral Image Classification: A Review

Yanfeng Gu, Jocelyn Chanussot, Xiuping Jia, Jon Atli Benediktsson. Multiple Kernel Learning for Hyperspectral Image Classification: A Review. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (11), pp.6547 - 6565. 〈10.1109/TGRS.2017.2729882〉. 〈hal-01687770〉

A Regression-Based High-Pass Modulation Pansharpening Approach

Gemine Vivone, Rocco Restaino, Jocelyn Chanussot. A Regression-Based High-Pass Modulation Pansharpening Approach. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, pp.1 - 13. 〈10.1109/TGRS.2017.2757508〉. 〈hal-01687064〉

Class-Oriented Weighted Kernel Sparse Representation With Region-Level Kernel for Hyperspectral Imagery Classification

Le Gan, Junshi Xia, Peijun Du, Jocelyn Chanussot. Class-Oriented Weighted Kernel Sparse Representation With Region-Level Kernel for Hyperspectral Imagery Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2017, pp.1 - 13. 〈10.1109/JSTARS.2017.2757475〉. 〈hal-01687085〉

Robust linear unmixing with enhanced sparsity

Alexandre Tiard, Laurent Condat, Lucas Drumetz, Jocelyn Chanussot, Wotao Yin, et al.. Robust linear unmixing with enhanced sparsity. IEEE International Conference on Image Processing (ICIP), Sep 2017, Beijing, China. 〈hal-01656640〉

Keynote 2: Opportunities and challenges in hyperspectral remote sensing

Jocelyn Chanussot. Keynote 2: Opportunities and challenges in hyperspectral remote sensing. 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Sep 2017, Kuching, France. IEEE, 〈10.1109/ICSIPA.2017.8120566〉. 〈hal-01687749〉

Object Tracking by Hierarchical Decomposition of Hyperspectral Video Sequences: Application to Chemical Gas Plume Tracking

Guillaume Tochon, Jocelyn Chanussot, Mauro Dalla Mura, Andrea L. Bertozzi. Object Tracking by Hierarchical Decomposition of Hyperspectral Video Sequences: Application to Chemical Gas Plume Tracking. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (8), pp.4567 - 4585. 〈10.1109/TGRS.2017.2694159〉. 〈hal-01665299〉

Cellular Automata-Based Image Sequence Denoising Algorithm for Signal Dependent Noise

Blanca Priego, Richard Duro, Jocelyn Chanussot. Cellular Automata-Based Image Sequence Denoising Algorithm for Signal Dependent Noise. International Work-Conference on the Interplay Between Natural and Artificial Computation, Jun 2017, Coruna, Spain. 2, pp.333 - 342, 2017, 〈10.1007/978-3-319-59773-7_34〉. 〈hal-01687082〉

ENCADREMENT DE THESES / PhD THESIS SUPERVISED

Grenoble Images Parole Signal Automatique laboratoire

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