Estimation of Parsimonious Covariance ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès sans actes
URL permanente :
Titre :
Estimation of Parsimonious Covariance Models for Gaussian Matrix Valued Random Variables for Multi-Dimensional Spectroscopic Data
Auteur(s) :
Poddar, Asmita [Auteur]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Latimier, Florent [Auteur]
Iovleff, Serge [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Latimier, Florent [Auteur]
Titre de la manifestation scientifique :
WiML 2018 - 13th Women in Machine Learning workshop
Ville :
Montreal
Pays :
Canada
Date de début de la manifestation scientifique :
2018-12-03
Date de publication :
2018-12-03
Discipline(s) HAL :
Statistiques [stat]/Applications [stat.AP]
Résumé en anglais : [en]
Satellite remote sensing makes it possible to observe landscapes on large spatial scales. The Sentinel-1 and Sentinel-2 satellites currently provide full coverage of the national territory of France every 5 days. Due to ...
Lire la suite >Satellite remote sensing makes it possible to observe landscapes on large spatial scales. The Sentinel-1 and Sentinel-2 satellites currently provide full coverage of the national territory of France every 5 days. Due to the orbit of the satellites, coupled with the presence of clouds, the sampling of the pixels are temporally irregular. The project aims to develop, study and implement supervised and unsupervised classification methods when the data are of different natures (heterogeneous) and have missing and\/or aberrant data. The methods implemented are developed to process satellite and aerial data for ecology and cartography.Lire moins >
Lire la suite >Satellite remote sensing makes it possible to observe landscapes on large spatial scales. The Sentinel-1 and Sentinel-2 satellites currently provide full coverage of the national territory of France every 5 days. Due to the orbit of the satellites, coupled with the presence of clouds, the sampling of the pixels are temporally irregular. The project aims to develop, study and implement supervised and unsupervised classification methods when the data are of different natures (heterogeneous) and have missing and\/or aberrant data. The methods implemented are developed to process satellite and aerial data for ecology and cartography.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CNRS
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:10:19Z