  Satellite image analysis has important implications for land use,
urbanization, and ecosystem monitoring. Deep learning methods can facilitate
the analysis of different satellite modalities, such as electro-optical (EO)
and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer
between the modalities to compensate for individual shortcomings. Recent
progress has shown how distributional alignment of neural network embeddings
can produce powerful transfer learning models by employing a sliced Wasserstein
distance (SWD) loss. We analyze how this method can be applied to Sentinel-1
and -2 satellite imagery and develop several extensions toward making it
effective in practice. In an application to few-shot Local Climate Zone (LCZ)
prediction, we show that these networks outperform multiple common baselines on
datasets with a large number of classes. Further, we provide evidence that
instance normalization can significantly stabilize the training process and
that explicitly shaping the embedding space using supervised contrastive
learning can lead to improved performance.
