TSGAN：一种基于光学的SAR时间偏移光学至SAR双条件GAN
TSGAN: An Optical-to-SAR Dual Conditional GAN for Optical based SAR Temporal Shifting
AI总结：
提出TSGAN双条件GAN，通过光学数据和变化图修改SAR数据实现时间偏移，解决光学到SAR转换不适定性，提升SSIM和PSNR并消除GAN虚构现象。
摘要：
与已被充分研究的SAR到光学转换领域不同，本研究探索了较少被研究的光学到SAR转换领域，由于该转换的不适定性，这是一个具有挑战性的领域。复杂性源于单一光学数据可基于SAR观测几何产生多种SAR表示。我们提出了一种名为SAR Temporal Shifting的新方法，输入来自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，两者均辅以间隔期间光学数据的变化图。该模型根据光学数据中观察到的变化修改SAR数据，以生成目标时间戳的SAR数据。我们的模型为双条件生成对抗网络（GAN），命名为Temporal Shifting GAN（TSGAN），在生成器和判别器中均采用孪生编码器。为防止模型对输入SAR数据过拟合，我们采用变化加权损失函数。该方法通过消除GAN的虚构现象，尤其在未变化区域，提升了这些区域的SSIM和PSNR，超越传统转换方法。此外，对Pix2Pix架构的修改及注意力机制的引入增强了模型在所有数据区域的性能。本研究为利用最丰富且长期的地球影像数据源——传统光学数据集奠定了基础，扩展其在SAR领域和时间分析中的应用。为促进进一步研究，我们提供了代码、研究使用的数据集及为新感兴趣区域生成配对SAR-光学数据集的框架。这些资源可在github.com/moienr/TemporalGAN获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
