Abstract
Synthetic Aperture Radar (SAR) enables global, all-weather earth observation. However, owing to diverse imaging mechanisms, domain shifts across sensors and regions severely hinder its semantic generalization. To address this, we present CrossEarth-SAR, the first billion-scale SAR vision foundation model built upon a novel physics-guided sparse mixture-of-experts (MoE) architecture incorporating physical descriptors, explicitly designed for cross-domain semantic segmentation. To facilitate large-scale pre-training, we develop CrossEarth-SAR-200K, a weakly and fully supervised dataset that unifies public and private SAR imagery. We also introduce a benchmark suite comprising 22 sub-benchmarks across 8 distinct domain gaps, establishing the first unified standard for domain generalization semantic segmentation on SAR imagery. Extensive experiments demonstrate that CrossEarth-SAR achieves state-of-the-art results on 20 benchmarks, surpassing previous methods by over 10\% mIoU on some benchmarks under multi-gap transfer. All code, benchmark and datasets will be publicly available.
Abstract (translated)
合成孔径雷达（SAR）技术能够实现全球范围内的全天候地球观测。然而，由于成像机制的多样性及传感器与区域间的领域变化，这严重阻碍了其语义泛化能力。为解决这一问题，我们提出了CrossEarth-SAR，这是首个基于新颖物理引导稀疏专家混合架构（MoE）并融合物理描述符的大规模SAR视觉基础模型，该模型特别针对跨域语义分割设计。为了支持大规模预训练，我们开发了CrossEarth-SAR-200K数据集，这是一个将公共和私人SAR图像统一起来的弱监督与全监督混合的数据集。 此外，我们还引入了一套基准测试工具，包含涵盖8种不同领域差距共计22个子基准的评估系统，建立了首个针对SAR图像跨域泛化语义分割的标准。广泛的实验表明，在多间隙迁移条件下，CrossEarth-SAR在20个基准上实现了最新的性能表现，并且在某些基准上的mIoU指标比之前的最佳方法高出超过10%。 所有代码、基准和数据集都将公开提供使用。
URL
https://arxiv.org/abs/2603.12008
