  Large, self-supervised vision models have led to substantial advancements for
automatically interpreting natural images. Recent works have begun tailoring
these methods to remote sensing data which has rich structure with
multi-sensor, multi-spectral, and temporal information providing massive
amounts of self-labeled data that can be used for self-supervised pre-training.
In this work, we develop a new encoder architecture called USat that can input
multi-spectral data from multiple sensors for self-supervised pre-training.
USat is a vision transformer with modified patch projection layers and
positional encodings to model spectral bands with varying spatial scales from
multiple sensors. We integrate USat into a Masked Autoencoder (MAE)
self-supervised pre-training procedure and find that a pre-trained USat
outperforms state-of-the-art self-supervised MAE models trained on remote
sensing data on multiple remote sensing benchmark datasets (up to 8%) and leads
to improvements in low data regimes (up to 7%). Code and pre-trained weights
are available at https://github.com/stanfordmlgroup/USat .
