  Self-supervised frameworks for representation learning have recently stirred
up interest among the remote sensing community, given their potential to
mitigate the high labeling costs associated with curating large satellite image
datasets. In the realm of multimodal data fusion, while the often used
contrastive learning methods can help bridging the domain gap between different
sensor types, they rely on data augmentations techniques that require expertise
and careful design, especially for multispectral remote sensing data. A
possible but rather scarcely studied way to circumvent these limitations is to
use a masked image modelling based pretraining strategy. In this paper, we
introduce Fus-MAE, a self-supervised learning framework based on masked
autoencoders that uses cross-attention to perform early and feature-level data
fusion between synthetic aperture radar and multispectral optical data - two
modalities with a significant domain gap. Our empirical findings demonstrate
that Fus-MAE can effectively compete with contrastive learning strategies
tailored for SAR-optical data fusion and outperforms other masked-autoencoders
frameworks trained on a larger corpus.
