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This paper deals with the classification of grasslands using high resolution satellite imagetime series. Grasslands considered in this work are semi-natural elements in fragmented landscapes,i.e., they are heterogeneous and small elements. The first contribution of this study is to accountfor grassland heterogeneity while working at the object level by modeling its pixels distributionsby a Gaussian distribution. To measure the similarity between two grasslands, a new kernelis proposed as a second contribution: the a-Gaussian mean kernel. It allows one to weight theinfluence of the covariance matrix when comparing two Gaussian distributions. This kernel isintroduced in support vector machines for the supervised classification of grasslands from southwestFrance. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for theclassification of grasslands’ management practices, while an inter-annual NDVI time series ofFormosat-2 is used for old and young grasslands’ discrimination. Results are compared to otherexisting pixel- and object-based approaches in terms of classification accuracy and processing time.The proposed method is shown to be a good compromise between processing speed and classificationaccuracy. It can adapt to the classification constraints, and it encompasses several similarity measuresknown in the literature. It is appropriate for the classification of small and heterogeneous objectssuch as grasslands

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels