Target-Level SAR-to-Optical Image Translation Driven by Semantic Segmentation

Synthetic Aperture Radar (SAR) is widely used in military and civilian applications due to its all-weather, day-and-night imaging capability. However, interpreting SAR images is challenging for both experts and non-experts. Inspired by deep learning-based style transfer, researchers have employed Generative Adversarial Networks (GANs) to convert SAR images into more intuitive optical ones. Yet, current loss functions and evaluation metrics focus mainly on pixel-level differences, overlooking the structural coherence required for target recognition and downstream tasks. To address this, we propose a semantic segmentation-driven framework for target-level SAR-to-optical image translation. Compatible with various supervised models, it incorporates segmentation loss and uses SAR segmentation maps as additional inputs to preserve target structure. Experiments on custom datasets, built from Sentinel 1-2 imagery with road binary segmentation labels, as well as public datasets, confirm the method's effectiveness across different base translation models. The source code and the datasets used will be published at the following URLhttps://github.com/NWPU-LHH/SOIT-Seg-Driven.