Fast forward feature selection for the nonlinear classification of hyperspectral images. - Mathieu Fauvel; Clément Dechesne; Anthony Zullo; Frédéric Ferraty (CoRR, 2015)

Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models. - Mathieu Fauvel; Clément Dechesne; Anthony Zullo; Frédéric Ferraty (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2015)

How to combine lidar and very high resolution multispectral images for forest stand segmentation? - Clément Dechesne; Clément Mallet; Arnaud Le Bris; Valérie Gouet-Brunet (IGARSS, 2017)

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). - Clément Dechesne (2017)

Forest Stand Extraction: Which Optimal Remote Sensing Data Source(S)? - Clément Dechesne; Clément Mallet; Arnaud Le Bris; Valérie Gouet-Brunet (IGARSS, 2018)

Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning. - Clément Dechesne; Sébastien Lefèvre; Rodolphe Vadaine; Guillaume Hajduch; Ronan Fablet (Remote. Sens., 2019)

Bayesian Deep Learning with Monte Carlo Dropout for Qualification of Semantic Segmentation. - Clément Dechesne; Pierre Lassalle; Sébastien Lefèvre (IGARSS, 2021)

Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images. - Clément Dechesne; Pierre Lassalle; Sébastien Lefèvre (Remote. Sens., 2021)