A
Adrien Lagrange
Researcher at University of Toulouse
Publications - 15
Citations - 270
Adrien Lagrange is an academic researcher from University of Toulouse. The author has contributed to research in topics: Cluster analysis & Supervised learning. The author has an hindex of 4, co-authored 14 publications receiving 224 citations. Previous affiliations of Adrien Lagrange include Office National d'Études et de Recherches Aérospatiales.
Papers
More filters
Journal ArticleDOI
Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part A: 2-D Contest
Manuel Campos-Taberner,Adriana Romero-Soriano,Carlo Gatta,Gustau Camps-Valls,Adrien Lagrange,Bertrand Le Saux,Anne Beaupere,Alexandre Boulch,Adrien Chan-Hon-Tong,Stéphane Herbin,Hicham Randrianarivo,Marin Ferecatu,Michal Shimoni,Gabriele Moser,Devis Tuia +14 more
TL;DR: The scientific results obtained by the winners of the 2-D contest are discussed, which studied either the complementarity of RGB and LiDAR with deep neural networks or provided a comprehensive benchmarking evaluation of new classification strategies for extremely high-resolution multimodal data.
Proceedings ArticleDOI
Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks
Adrien Lagrange,Bertrand Le Saux,Anne Beaupere,Alexandre Boulch,Adrien Chan-Hon-Tong,Stéphane Herbin,Hicham Randrianarivo,Marin Ferecatu +7 more
TL;DR: It is established that combining multisensor features is essential for retrieving some specific classes, in the image domain, deep convolutional networks obtain significantly better overall performances and transfer of learning from large generic-purpose image sets is highly effective to build EO data classifiers.
Journal ArticleDOI
Large-Scale Feature Selection With Gaussian Mixture Models for the Classification of High Dimensional Remote Sensing Images
TL;DR: A large-scale feature selection wrapper is discussed for the classification of high dimensional remote sensing and an efficient implementation based on intrinsic properties of Gaussian mixtures models and block matrix is proposed.
Journal ArticleDOI
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
TL;DR: In this paper, a hierarchical Bayesian model is proposed to perform classification and low-level modeling jointly, where the estimated latent variables are used as features for classification and to incorporate simultaneously supervised information to help latent variable extraction.
Journal ArticleDOI
Matrix Cofactorization for Joint Spatial–Spectral Unmixing of Hyperspectral Images
TL;DR: In this article, instead of considering a simple but limited regularization process, spatial information is directly incorporated through the newly proposed context of spatial unmixing through a cofactorization model used to identify clusters of shared spatial and spectral signatures.