Generative models for SAR–optical image translation: A systematic review

Growing demands in sustainable development and resource management are driving increasing reliance on remote sensing-based Earth observation and image interpretation. In parallel, multimodal collaborative processing is attracting research attention. Synthetic aperture radar (SAR) and optical images offer complementary advantages but pose challenges for simultaneous use due to platform constraints and environmental conditions, often leaving only one modality available and impeding joint analysis. Generative models, particularly generative adversarial networks (GANs) and diffusion models (DMs), address this by learning cross-modal mappings. Translated images preserve structure and semantics while adopting target characteristics, thereby facilitating collaborative use. This review systematically categorizes translation frameworks spanning GANs, DMs, and other generative models. It then details downstream tasks supported by SAR–optical translation, including cloud removal, change detection, semantic segmentation, registration, and object detection, highlighting how translation bridges data gaps and enhances interpretation robustness. Furthermore, we provide open-source code and public datasets, discuss current challenges, and outline future research directions.