Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images

Understand

The effective combination of the complementary information provided by the huge amount of unlabeled multi-sensor data (e.g., Synthetic Aperture Radar (SAR) and optical images) is a critical topic in remote sensing.

• Recently, contrastive learning methods have reached remarkable success in obtaining meaningful feature representations from multi-view data.
• However, these methods only focus on image-level features, which may not satisfy the requirement for dense prediction tasks such as land-cover mapping.
• In this work, we propose a self-supervised framework for SAR-optical data fusion and land-cover mapping tasks.