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)

1 DYNAFOR - Dynamiques Forestières dans l'Espace Rural
2 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems

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.

Domaines: Statistiques [math.ST]

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. ⟨hal-01366208v1⟩