论文标题
Nightvision：从红外观察产生夜间卫星图像
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
论文作者
论文摘要
机器学习到卫星图像的应用程序最近的爆炸通常依赖于可见图像，因此夜间缺乏数据。可以通过使用可用的红外观测来产生可见图像来填补差距。这项工作介绍了如何通过使用基于U-NET的体系结构成功地应用深度学习来创建这些图像。所提出的方法显示出令人鼓舞的结果，在独立的测试集中达到了最高86％的结构相似性指数（SSIM），并提供了可令人信服的令人信服的输出图像，这是由红外观测产生的。
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night. The gap can be filled by employing available infra-red observations to generate visible images. This work presents how deep learning can be applied successfully to create those images by using U-Net based architectures. The proposed methods show promising results, achieving a structural similarity index (SSIM) up to 86\% on an independent test set and providing visually convincing output images, generated from infra-red observations.
