论文标题
深雪：通过生成对抗网综合遥感图像
Deep Snow: Synthesizing Remote Sensing Imagery with Generative Adversarial Nets
论文作者
论文摘要
在这项工作中，我们证明，即使在未配对的训练环境中，也可以使用生成的对抗网络（GAN）来在遥感图像中产生现实的普遍变化。我们根据生成的和真实图像的深层嵌入来研究一些转换质量指标，这些指标能够可视化和理解GAN的训练动力学，并且可以在量化量化生成图像与真实图像的区分方面提供有用的衡量标准。我们还确定了GAN在生成的图像中引入的一些伪影，这些伪影在生成的图像中可能有助于即使在真实和生成的样品看起来相似的情况下，即使在深层嵌入特征空间中，真实和生成的样品之间看到的差异。
In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in remote sensing imagery, even in an unpaired training setting. We investigate some transformation quality metrics based on deep embedding of the generated and real images which enable visualization and understanding of the training dynamics of the GAN, and may provide a useful measure in terms of quantifying how distinguishable the generated images are from real images. We also identify some artifacts introduced by the GAN in the generated images, which are likely to contribute to the differences seen between the real and generated samples in the deep embedding feature space even in cases where the real and generated samples appear perceptually similar.
