用于配对遥感图像的语义鲁棒无监督图像翻译
Semantically Robust Unsupervised Image Translation for Paired Remote Sensing Images
AI总结：
针对双时相遥感图像无监督翻译需语义保留和确定性输出的问题，提出SRUIT方法，通过共享生成器高层网络层、引入跨周期一致对抗网络，实现语义鲁棒的确定性翻译，提升感知质量与语义保留能力。
摘要：
双时相遥感图像中用于变化检测或分类的图像翻译具有独特性。尽管可获取配对图像，但仍为无监督方式；且翻译需严格保留语义而非生成多模态输出。针对这些问题，本文提出新方法SRUIT（语义鲁棒无监督图像到图像翻译），确保语义鲁棒翻译并产生确定性输出。受先前工作启发，该方法探索双时相遥感图像的潜在特征并设计相应网络：首先假设双时相遥感图像共享同一潜在空间（因通常来自同一陆地区域），故让生成器共享高层网络层，此约束迫使两个域映射落入同一潜在空间；其次考虑双时相图像的土地覆盖可相互演变，利用跨周期一致对抗网络实现双向翻译与恢复。实验结果表明，共享权重和跨周期一致性约束使翻译图像在显著差异下兼具良好感知图像质量和语义保留能力。
英文摘要：
Image translation for change detection or classification in bi-temporal remote sensing images is unique. Although it can acquire paired images, it is still unsupervised. Moreover, strict semantic preservation in translation is always needed instead of multimodal outputs. In response to these problems, this paper proposes a new method, SRUIT (Semantically Robust Unsupervised Image-to-image Translation), which ensures semantically robust translation and produces deterministic output. Inspired by previous works, the method explores the underlying characteristics of bi-temporal Remote Sensing images and designs the corresponding networks. Firstly, we assume that bi-temporal Remote Sensing images share the same latent space, for they are always acquired from the same land location. So SRUIT makes the generators share their high-level layers, and this constraint will compel two domain mapping to fall into the same latent space. Secondly, considering land covers of bi-temporal images could evolve into each other, SRUIT exploits the cross-cycle-consistent adversarial networks to translate from one to the other and recover them. Experimental results show that constraints of sharing weights and cross-cycle consistency enable translated images with both good perceptual image quality and semantic preservation for significant differences.
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。
