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High Dimensional Kullback-Leibler Divergence for grassland object-oriented classification from high resolution satellite image time series

Maïlys Lopes 1, * Mathieu Fauvel 1, 2 Stéphane Girard 3 David Sheeren 1, 2
* Auteur correspondant
3 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
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.
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https://hal.inrae.fr/hal-02798633
Déposant : Migration Prodinra <>
Soumis le : vendredi 5 juin 2020 - 16:17:09
Dernière modification le : jeudi 19 novembre 2020 - 13:04:02

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  • HAL Id : hal-02798633, version 1
  • PRODINRA : 354465

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Maïlys Lopes, Mathieu Fauvel, Stéphane Girard, David Sheeren. High Dimensional Kullback-Leibler Divergence for grassland object-oriented classification from high resolution satellite image time series. 2016 European Space Agency Living Planet Symposium, May 2016, Prague, Czech Republic. 1 p., 2016. ⟨hal-02798633⟩

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