Yujie Zhang

Synthetic Aperture Radar (SAR), an active remote sensing technology that can be deployed on various aerial plat forms, generates images by emitting microwaves and analyzing the intensity of backscattered signals. The penetration capability of microwaves enables observation of Earth at all weather, day and night, broadening the application of SAR. However, SAR image interpretation remains challenging for non-experts. In contrast, optical images provide intuitive visual features but are weather-sensitive. To synergize these modalities, SAR-to-Optical Translation (S2OT) has gained research attention, but most studies prioritize visual quality or similarity metrics over practical applicability to downstream tasks. This study proposes a high level vision task-coordinated S2OT framework to address this gap. Semantic segmentation, emulating land cover classification, is integrated as the downstream task. Semantic segmentation loss guides the network to generate optical images that enhance task-relevant features. To preserve structural information in SAR images, we introduce SSIM loss and incorporate SAR derived semantic segmentation maps as auxiliary inputs. An identity loss further aligns the distributions of generated and real optical images, mitigating domain discrepancies. Extensive experiments confirm that S2OT improves downstream land cover classification. The inclusion of task-specific losses elevates translation quality: our HVTC-GAN surpasses the baseline methods in SSIM and PSNR metrics. Ablation studies validate the effectiveness of co-training S2OT with high-level vision tasks, demonstrating that task-oriented constraints enhance both translation fidelity and downstream utility. Code will be available at https://github.com/NWPU-LHH/HVTC-GAN

Optical remote sensing and Synthetic Aperture Radar(SAR) remote sensing are crucial for earth observation, offering complementary capabilities. While optical sensors provide high-quality images, they are limited by weather and lighting conditions. In contrast, SAR sensors can operate effectively under adverse conditions. This letter proposes a GAN-based SAR-to-optical image translation method named Seg-CycleGAN, designed to enhance the accuracy of ship target translation by leveraging semantic information from a pre-trained semantic segmentation model. Our method utilizes the downstream task of ship target semantic segmentation to guide the training of image translation network, improving the quality of output optical-styled images. The potential of foundation-model-annotated datasets in SAR-to-optical translation tasks is revealed. This work suggests broader research and applications for downstream-task-guided frameworks. The code and link to download proposed HRSID-DIOR dataset will be available at https://github.com/NPULHH/.