AI RESEARCH
SARMAE: Masked Autoencoder for SAR Representation Learning
arXiv CS.LG
•
ArXi:2512.16635v2 Announce Type: replace-cross Synthetic Aperture Radar (SAR) imagery plays a critical role in all-weather, day-and-night remote sensing applications. However, existing SAR-oriented deep learning is constrained by data scarcity, while the physically grounded speckle noise in SAR imagery further hampers fine-grained semantic representation learning. To address these challenges, we propose SARMAE, a Noise-Aware Masked Autoencoder for self-supervised SAR representation learning.