MMATR-X: High-Resolution Optical-SAR Joint Pretraining via Conditioned Alignment and Degraded-Target Prediction
CoDe-MAE tackles representation degradation in high-resolution optical and SAR pretraining by using Conditioned Contrastive Learning (CCL) and Cross-Modal Degraded Reconstruction (CDR). This method avoids rigid alignment by using dynamic buffering to align shared consensus while stripping non-homologous features, leading to robust representations that achieve state-of-the-art performance on diverse optical and SAR tasks.