Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914909429694464 |
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| author | Zhang, Hannuo Li, Huihui Lin, Jiarui Zhang, Yujie Fan, Jianghua Liu, Hang |
| author_facet | Zhang, Hannuo Li, Huihui Lin, Jiarui Zhang, Yujie Fan, Jianghua Liu, Hang |
| contents | 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 will be available at https://github.com/NPULHH/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05777 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task Zhang, Hannuo Li, Huihui Lin, Jiarui Zhang, Yujie Fan, Jianghua Liu, Hang Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing 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 will be available at https://github.com/NPULHH/ |
| title | Seg-CycleGAN : SAR-to-optical image translation guided by a downstream task |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Image and Video Processing |
| url | https://arxiv.org/abs/2408.05777 |