# SAR-Optical Fusion

SAR and optical remote sensing images are often treated as largely redundant views, and earlier cross-modal self-supervised methods aligned them only at the whole-image level, leaving fine-grained complementary structure unused. CroCoMAE combines image-level contrastive learning with patch-level masked reconstruction inside a ViT, splitting each patch embedding into a shared subspace aligned across sensors and an unconstrained unshared subspace. On the EuroSAT-SAR scene-classification and DFC2020 semantic-segmentation benchmarks it beats uni-modal self-supervised baselines and retrained cross-modal frameworks in most configurations, with the largest gains on segmentation.

SAR and optical remote sensing images are often treated as largely redundant views, and earlier cross-modal self-supervised methods aligned them only at the whole-image level, leaving fine-grained complementary structure unused. CroCoMAE combines image-level contrastive learning with patch-level masked reconstruction inside a ViT, splitting each patch embedding into a shared subspace aligned across sensors and an unconstrained unshared subspace. On the EuroSAT-SAR scene-classification and DFC2020 semantic-segmentation benchmarks it beats uni-modal self-supervised baselines and retrained cross-modal frameworks in most configurations, with the largest gains on segmentation.