In contrast to the well-investigated field of SAR-to-Optical translation, this study explores the lesser-investigated domain of Optical-to-SAR translation, a challenging field due to the ill-posed nature of this translation. The complexity arises as a single optical data can have multiple SAR representations based on the SAR viewing geometry. We propose a novel approach, termed SAR Temporal Shifting, which inputs an optical data from the desired timestamp along with a SAR data from a different temporal point but with a consistent viewing geometry as the expected SAR data, both complemented with a change map of optical data during the intervening period. This model modifies the SAR data based on the changes observed in optical data to generate the SAR data for the desired timestamp. Our model, a dual conditional Generative Adversarial Network (GAN), named Temporal Shifting GAN (TSGAN), incorporates a siamese encoder in both the Generator and the Discriminator. To prevent the model from overfitting on the input SAR data, we employed a change weighted loss function. Our approach surpasses traditional translation methods by eliminating the GAN's fiction phenomenon, particularly in unchanged regions, resulting in higher SSIM and PSNR in these areas. Additionally, modifications to the Pix2Pix architecture and the inclusion of attention mechanisms have enhanced the model's performance on all regions of the data. This research paves the way for leveraging legacy optical datasets, the most abundant and longstanding source of Earth imagery data, extending their use to SAR domains and temporal analyses. To foster further research, we provide the code, datasets used in our study, and a framework for generating paired SAR-Optical datasets for new regions of interest. These resources are available on github.com/moienr/TemporalGAN

翻译：与已深入研究的SAR到光学影像翻译不同，本研究探索了较少涉及的光学到SAR影像翻译领域，该领域因翻译问题的不适定性而极具挑战性。其复杂性源于单一光学数据可对应多种基于SAR观测几何的表示形式。我们提出了一种名为"SAR时序偏移"的新方法：该方法输入目标时间戳的光学数据，以及来自不同时间点但具有与预期SAR数据一致观测几何的SAR数据，两者辅以间隔期内光学数据的变化图。该模型根据光学数据中观测到的变化调整SAR数据，从而生成目标时间戳的SAR数据。我们的模型是一个名为"时序偏移生成对抗网络"（TSGAN）的双条件GAN，其生成器和判别器中均嵌入了孪生编码器。为防止模型对输入SAR数据过拟合，我们采用了变化加权损失函数。该方法通过消除GAN的虚构现象（尤其是在未变化区域），在SSIM和PSNR指标上优于传统翻译方法。此外，对Pix2Pix架构的改进及注意力机制的引入，提升了模型在数据所有区域上的性能。本研究为利用最丰富且历史最悠久的地球影像数据源——光学历史数据集——开辟了新途径，将其应用扩展至SAR领域及时序分析。为促进进一步研究，我们提供了代码、数据集，以及用于为新感兴趣区域生成配对SAR-光学数据的框架。这些资源可在github.com/moienr/TemporalGAN获取。