结论
我们揭示了以下结论：CyeleGAN用于遥感图像生成是可行的，尤其是给没有雪的地面覆盖雪。尽管这个生成结果并不能骗过人的眼睛，但通过对某些区域的详细观察，可以找到一些植入的伪像：这就提示我们做任何操作时都要小心它对后面过程的影响。我们还介绍了一些质量评估的方法，可以用来指导CycleGAN这种非配对训练应该何时停止——虽然只研究了一下同域翻译（RGBRGB），我们预感到，以后可能要用CycleGAN或pix2pix在跨域之间做实验，但就像我们已经说了的那些一样：我们要对这些模型引入的潜在的artifacts做潜在的分析。
致谢
本文得到了洛斯阿拉莫斯实验室研究与开发计划和空间与地球中心的支持。还要感谢笛卡尔实验室的图像和技术支持。最后，还要感谢同志们的建设性的讨论。
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