Abstract—

The paper investigates the use of generative adversarial networks (GAN) for intentional modification of Earth remote sensing data. A generative neural network that includes a special subnet for object boundary inpainting is considered. The network comprises two GAN: the first completes the object boundary, and the second repaints blank areas. Actual remote sensing data are used to test the generative network under consideration. The exemplar-based Patch-Match algorithm is taken as a reference for comparison purposes. The experimental results allow the conclusion that the approach is an effective tool for the intentional modification of large terrestrial area images in falsification of Earth remote sensing data.

中文翻译：

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使用生成对抗网络更改遥感数据

摘要—

本文研究了使用生成对抗网络（GAN）进行地球遥感数据的有意修改。考虑了一个生成神经网络，该网络包括用于对象边界修复的特殊子网。该网络包括两个GAN：第一个完成对象边界，第二个重新绘制空白区域。实际的遥感数据用于测试所考虑的生成网络。基于示例的Patch-Match算法被用作比较的参考。实验结果可以得出这样的结论：该方法是在伪造地球遥感数据时有意修改大陆地图像的有效工具。