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Clément Dechesne
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2020 – today
- 2021
- [j3]Clément Dechesne, Pierre Lassalle, Sébastien Lefèvre:
Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images. Remote. Sens. 13(19): 3836 (2021) - [c3]Clément Dechesne, Pierre Lassalle, Sébastien Lefèvre:
Bayesian Deep Learning with Monte Carlo Dropout for Qualification of Semantic Segmentation. IGARSS 2021: 2536-2539
2010 – 2019
- 2019
- [j2]Clément Dechesne, Sébastien Lefèvre, Rodolphe Vadaine, Guillaume Hajduch, Ronan Fablet:
Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning. Remote. Sens. 11(24): 2997 (2019) - 2018
- [c2]Clément Dechesne, Clément Mallet, Arnaud Le Bris, Valérie Gouet-Brunet:
Forest Stand Extraction: Which Optimal Remote Sensing Data Source(S)? IGARSS 2018: 7279-7282 - 2017
- [b1]Clément Dechesne:
Semantic segmentation of forest stand by join analysis of very high resolution multispectral image and 3D airborne Lidar data. (Segmentation sémantique de peuplement forestiers par analyse conjointe d'imagerie multispectrale très haute résolution et de données 3D Lidar aéroportées). University of Paris-Est, France, 2017 - [c1]Clément Dechesne, Clément Mallet, Arnaud Le Bris, Valérie Gouet-Brunet:
How to combine lidar and very high resolution multispectral images for forest stand segmentation? IGARSS 2017: 2772-2775 - 2015
- [j1]Mathieu Fauvel, Clément Dechesne, Anthony Zullo, Frédéric Ferraty:
Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 8(6): 2824-2831 (2015) - [i1]Mathieu Fauvel, Clément Dechesne, Anthony Zullo, Frédéric Ferraty:
Fast forward feature selection for the nonlinear classification of hyperspectral images. CoRR abs/1501.00857 (2015)
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