  Due to its all-weather and day-and-night capabilities, Synthetic Aperture
Radar imagery is essential for various applications such as disaster
management, earth monitoring, change detection and target recognition. However,
the scarcity of labeled SAR data limits the performance of most deep learning
algorithms. To address this issue, we propose a novel self-supervised learning
framework based on masked Siamese Vision Transformers to create a General SAR
Feature Extractor coined SAFE. Our method leverages contrastive learning
principles to train a model on unlabeled SAR data, extracting robust and
generalizable features. SAFE is applicable across multiple SAR acquisition
modes and resolutions. We introduce tailored data augmentation techniques
specific to SAR imagery, such as sub-aperture decomposition and despeckling.
Comprehensive evaluations on various downstream tasks, including few-shot
classification, segmentation, visualization, and pattern detection, demonstrate
the effectiveness and versatility of the proposed approach. Our network
competes with or surpasses other state-of-the-art methods in few-shot
classification and segmentation tasks, even without being trained on the
sensors used for the evaluation.
