Titre :

Estimation of Parsimonious Covariance Models for Gaussian Matrix Valued Random Variables for Multi-Dimensional Spectroscopic Data

Auteur(s) :

Poddar, Asmita [Auteur]
National University of Singapore [NUS]
Iovleff, Serge [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Latimier, Florent [Auteur]
MOdel for Data Analysis and Learning [MODAL]

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

Discipline(s) HAL :

Statistiques [stat]/Applications [stat.AP]
Mathématiques [math]/Statistiques [math.ST]

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, thesampling 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

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL