SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
This work addresses the need for better pre-training methods in remote sensing, where unlabeled data is abundant, offering incremental advances over existing techniques.
The paper tackles the problem of unsupervised pre-training for satellite imagery by introducing SatMAE, a framework based on Masked Autoencoder that leverages temporal and multi-spectral data, resulting in improvements of up to 7% on benchmark datasets and up to 14% on downstream tasks like land cover classification.
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery presents significant opportunities as unlabelled data is plentiful and the inherent temporal and multi-spectral structure provides avenues to further improve existing pre-training strategies. In this paper, we present SatMAE, a pre-training framework for temporal or multi-spectral satellite imagery based on Masked Autoencoder (MAE). To leverage temporal information, we include a temporal embedding along with independently masking image patches across time. In addition, we demonstrate that encoding multi-spectral data as groups of bands with distinct spectral positional encodings is beneficial. Our approach yields strong improvements over previous state-of-the-art techniques, both in terms of supervised learning performance on benchmark datasets (up to $\uparrow$ 7%), and transfer learning performance on downstream remote sensing tasks, including land cover classification (up to $\uparrow$ 14%) and semantic segmentation. Code and data are available on the project website: https://sustainlab-group.github.io/SatMAE/