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 .
@article{arxiv.2312.02199,
title = {USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery},
author = {Jeremy Irvin and Lucas Tao and Joanne Zhou and Yuntao Ma and Langston Nashold and Benjamin Liu and Andrew Y. Ng},
journal= {arXiv preprint arXiv:2312.02199},
year = {2023}
}