  European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at
highspatial resolution and high revisit time, respectively, radar and optical
imagesthat support a wide range of Earth surface monitoring tasks such as
LandUse/Land Cover mapping. A long-standing challenge in the remote
sensingcommunity is about how to efficiently exploit multiple sources of
information and leverage their complementary. In this particular case, get the
most out ofradar and optical satellite image time series (SITS). Here, we
propose to dealwith land cover mapping through a deep learning framework
especially tailoredto leverage the multi-source complementarity provided by
radar and opticalSITS. The proposed architecture is based on an extension of
Recurrent NeuralNetwork (RNN) enriched via a customized attention mechanism
capable to fitthe specificity of SITS data. In addition, we propose a new
pretraining strategythat exploits domain expert knowledge to guide the model
parameter initial-ization. Thorough experimental evaluations involving several
machine learningcompetitors, on two contrasted study sites, have demonstrated
the suitabilityof our new attention mechanism combined with the extend RNN
model as wellas the benefit/limit to inject domain expert knowledge in the
neural networktraining process.
