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

Maïlys 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 build a model suitable to classify grassland management practices using satellite image time series with high spatial resolution. The study site is located in southern France where 52 parcels with three management types were selected. The NDVI computed from a Formosat 2 intra-annual time series of 17 images was used. To work at the parcel scale while accounting for the spectral variability inside the grasslands, the pixels signal distribution is modeled by a Gaussian distribution. To deal with the small ground sample size compared to the large number of variables, a parsimonious Gaussian model is used. A high dimensional symmetrized Kullback-Leibler divergence (KLD) is introduced to compute the similarity between each pair of grasslands. Our proposed model provides better results than the conventional KLD in terms of classification accuracy using SVM.
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Maïlys Lopes, Mathieu Fauvel, Stéphane Girard, David Sheeren. High dimensional Kullback-Leibler divergence for grassland management practices classification from high resolution satellite image time series. IEEE International Geoscience And Remote Sensing Symposium - IGARSS 2016, Jul 2016, Beijing, China. pp.3342-3345, ⟨10.1109/IGARSS.2016.7729864⟩. ⟨hal-01366208v2⟩

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