Self-Supervised Contrastive Learning for Satellite Cloud & Shadow Removal
Multi-modal model reconstructing cloud-obscured optical satellite pixels by fusing contemporaneous Synthetic Aperture Radar (SAR) signals.
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Project Technical Overview
Solves perpetual cloud cover issues in Earth observation. Fuses cloud-penetrating Sentinel-1 SAR microwave radar with clouded optical Sentinel-2 imagery using self-supervised contrastive representations, synthesizing seamless cloud-free terrain maps.
Read the foundational IEEE conference research paper, citations, and algorithmic architecture for this project.