OSCAR: Optical-aware Semantic Control for Aleatoric Refinement in Sar-to-Optical Translation

TL;DR

This paper introduces a novel SAR-to-Optical translation framework that leverages semantic alignment, grounded generative guidance, and uncertainty modeling to produce more accurate and perceptually realistic optical images from SAR data.

Contribution

The paper presents a new S2O translation method integrating semantic alignment, class-aware guidance, and aleatoric uncertainty modeling, addressing speckle noise and structural distortions in SAR-to-optical translation.

Findings

Achieves superior perceptual quality over existing methods

Maintains high semantic consistency in generated images

Effectively reduces artifacts caused by speckle noise

Abstract

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.