Rémi Flamary

Site web professionel

Projet AMOR

Description

Le but de ce projet est de proposer des méthodes automatiques d’extraction d’information dans des séries d’images de télédétection à haute résolution temporelle. Ces méthodes prendront en compte les propriétés intrinsèques des images multitemporelles : échantillonage temporel irrégulier, image multicomposante et grande masse de données. Elles devront de plus fournir des modèles interprétables qui pourrons être mis à jour itérativement.

Le Projet AMOR est un projet Jeunes Chercheurs soutenu par le GdR 720 ISIS et l’association GRETSI.

Responsables et partenaires du projet

News

Kickoff Meeting

15 Octobre 2013

Nous nous sommes rencontré à Nice à la suite du Workshop MAHI pour amorcer nos collaborations scientifiques.

Publications

R. Flamary, M. Fauvel, M. Dalla Mura, S. Valero, "Analysis of multi-temporal classification techniques for forecasting image times series", Geoscience and Remote Sensing Letters (GRSL), Vol. 12, N. 5, pp 953-957, 2015.
Abstract: The classification of an annual times series by using data from past years is investigated in this paper. Several classification schemes based on data fusion, sparse learning and semi-supervised learning are proposed to address the problem. Numerical experiments are performed on a MODIS image time series and show that while several approaches have statistically equivalent performances, SVM with 1 regularization leads to a better interpretation of the results due to their inherent sparsity in the temporal domain.
BibTeX:
@article{flamary2014analysis,
author = { Flamary, R. and Fauvel, M. and Dalla Mura, M. and Valero, S.},
title = {Analysis of multi-temporal classification techniques for forecasting image times series},
journal = { Geoscience and Remote Sensing Letters (GRSL)},
volume = {12},
number = {5},
pages = {953-957},
year = {2015}
}
R. Flamary, I. Harrane, M. Fauvel, S. Valero, M. Dalla Mura, "Discrimination périodique à partir d’observations multi-temporelles", GRETSI, 2015.
Abstract: In this work, we propose a novel linear classification scheme for non-stationary periodic data. We express the classifier in a temporal basis while regularizing its temporal complexity leading to a convex optimization problem. Numerical experiments show very good results on a simulated example and on real life remote sensing image classification problem.
BibTeX:
@inproceedings{flamary2015discrimination,
author = {Flamary, R. and Harrane, I. and Fauvel, M. and Valero, S. and Dalla Mura, M.},
title = {Discrimination périodique à partir d’observations multi-temporelles},
booktitle = {GRETSI},
year = {2015}
}