High dimensional Kullback-Leibler divergence for grassland management practices classification from high resolution satellite image time series

Mailys Lopes 1 Mathieu Fauvel 1 Stéphane Girard 2 David Sheeren 1
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : The aim of this study is to classify grassland management practices using satellite image time series with high spatial resolution. The study area is located in southern France where 52 parcels with 3 management types were selected. The spectral variability inside the grasslands was taken into account considering that the pixels signal can be modeled by a Gaussian distribution. A parsimonious model is discussed to deal with the high dimension of the data and the small sample size. A high dimensional symmetrized Kullback-Leibler divergence (KLD) is introduced to compute the similarity between each pair of grasslands. The model is positively compared to the conventional KLD to construct a positive definite kernel used in SVM for supervised classification.
Type de document :
Communication dans un congrès
IGARSS 2016 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Bejing, China. 2016
Liste complète des métadonnées

Littérature citée [18 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01366208
Contributeur : Stephane Girard <>
Soumis le : mercredi 14 septembre 2016 - 11:22:09
Dernière modification le : lundi 9 avril 2018 - 12:22:26
Document(s) archivé(s) le : jeudi 15 décembre 2016 - 14:25:54

Fichier

abstract_igarss_2016_final_ver...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01366208, version 1

Collections

Citation

Mailys Lopes, Mathieu Fauvel, Stéphane Girard, David Sheeren. High dimensional Kullback-Leibler divergence for grassland management practices classification from high resolution satellite image time series. IGARSS 2016 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Bejing, China. 2016. 〈hal-01366208v1〉

Partager

Métriques

Consultations de la notice

757

Téléchargements de fichiers

119