Synthetic Aperture Radar (SAR) provides robust all-weather imaging capabilities; however, translating SAR observations into photo-realistic optical images remains a fundamentally ill-posed problem. Current approaches are often hindered by the inherent speckle noise and geometric distortions of SAR data, which frequently result in semantic misinterpretation, ambiguous texture synthesis, and structural hallucinations. To address these limitations, a novel SAR-to-Optical (S2O) translation framework is proposed, integrating three core technical contributions: (i) Cross-Modal Semantic Alignment, which establishes an Optical-Aware SAR Encoder by distilling robust semantic priors from an Optical Teacher into a SAR Student (ii) Semantically-Grounded Generative Guidance, realized by a Semantically-Grounded ControlNet that integrates class-aware text prompts for global context with hierarchical visual prompts for local spatial guidance; and (iii) an Uncertainty-Aware Objective, which explicitly models aleatoric uncertainty to dynamically modulate the reconstruction focus, effectively mitigating artifacts caused by speckle-induced ambiguity. Extensive experiments demonstrate that the proposed method achieves superior perceptual quality and semantic consistency compared to state-of-the-art approaches.

翻译：合成孔径雷达（SAR）具备全天候稳健成像能力；然而，将SAR观测数据转换为逼真光学图像仍是一个本质上的不适定问题。现有方法常受SAR数据固有散斑噪声与几何畸变的制约，导致语义误判、纹理合成模糊及结构伪影等问题。为突破这些局限，本文提出一种新型SAR至光学（S2O）转换框架，集成三项核心技术贡献：（i）跨模态语义对齐：通过从光学教师网络向SAR学生网络蒸馏鲁棒语义先验，构建光学感知SAR编码器；（ii）语义接地生成引导：采用语义接地ControlNet实现，该网络融合面向全局上下文的类别感知文本提示与面向局部空间引导的层次化视觉提示；（iii）不确定性感知目标函数：显式建模随机不确定性以动态调节重建焦点，有效抑制散斑歧义导致的伪影。大量实验表明，相较于现有先进方法，所提方案在感知质量与语义一致性方面均取得显著提升。