Variational Dual-SimCLR: Probabilistic Self-Supervised Contrastive Learning for Satellite Data
Self-supervised learning has become an important approach for machine learning in remote sensing, where large amounts of unlabeled satellite data exist but only lim- ited labeled datasets are available. Contrastive methods such as SimCLR have been adapted to multimodal Sentinel-1 and Sentinel-2 imagery, but current SimCLR-based approaches rely on deterministic embeddings and therefore lack the abi
