论文标题
带有多条件对抗网络的SAR-EO图像翻译
SAR-to-EO Image Translation with Multi-Conditional Adversarial Networks
论文作者
论文摘要
本文探讨了多条件对抗网络用于SAR-EO图像翻译。以前的方法仅在输入SAR上条件对抗网络。我们表明，结合多种互补方式，例如Google Maps和IR可以进一步改善SAR-EO图像翻译，尤其是在保留人造物体的锋利边缘方面。我们证明了我们的方法在包括SEN12MS，DFC2020和SpaceNet6在内的各种数据集中的有效性。我们的实验结果表明，与仅在配对的SAR和EO数据中训练的模型相比，互补方式提供的其他信息可改善SAR-EO图像翻译的性能。据我们所知，我们的方法是第一个利用多种方式来改善SAR-EO图像翻译性能。
This paper explores the use of multi-conditional adversarial networks for SAR-to-EO image translation. Previous methods condition adversarial networks only on the input SAR. We show that incorporating multiple complementary modalities such as Google maps and IR can further improve SAR-to-EO image translation especially on preserving sharp edges of manmade objects. We demonstrate effectiveness of our approach on a diverse set of datasets including SEN12MS, DFC2020, and SpaceNet6. Our experimental results suggest that additional information provided by complementary modalities improves the performance of SAR-to-EO image translation compared to the models trained on paired SAR and EO data only. To best of our knowledge, our approach is the first to leverage multiple modalities for improving SAR-to-EO image translation performance.
