Multi-Scale Classification of Sentinel-2 Images for Land Cover Mapping Using Two-Branch Convolutional Neural Network

International audience Effectively characterize the current land cover status is crucial for assessing agricultural production, monitoring natural resources, and making informed land management decisions. Satellite Image Time Series (SITS) data, capturing spatio-temporal information, are the primary source of information to support the general task of Land Use Land Cover (LULC) mapping. With the aim to effectively exploit the spatio-temporal information carried out by SITS data for the underlying task of land cover mapping, here we introduce a multi-scale classification framework that combines together pixel-level and object-level multivariate SITS information with the objective to ameliorate the pixel-level time series analysis. To assess the behaviour of the proposed framework, we provide a comparative analysis with several SITS-based land cover mapping strategies on a challenging study area, namely Koumbia, located in the Burkina Faso. The obtained results reveal that the joint use of pixel-level and object-level information clearly ameliorates the classification performances.