论文标题
卫星图像生成的条件性渐进生成对抗网络
Conditional Progressive Generative Adversarial Network for satellite image generation
论文作者
论文摘要
由于机器学习算法能够现实地替换缺失的像素，因此图像生成和图像完成是迅速发展的领域。但是，产生大量的高分辨率图像，具有大量的细节，提出了重要的计算挑战。在这项工作中，我们将图像生成任务制定为完成图像的完成，其中三个角落中缺少一个。然后，我们将这种方法扩展到具有相同细节级别的迭代构建较大图像。我们的目标是获得可扩展的方法，以生成通常在卫星图像数据集中发现的高分辨率样本。我们引入了有条件的渐进生成对抗网络（GAN），该网络（GAN）在图像中生成丢失的瓷砖，使用AS INPUT由Wasserstein Auto-编码器编码在潜在矢量中的三个初始相邻图块。我们专注于联合国卫星中心（UNOSAT）使用的一组图像，以训练洪水检测工具，并在现实设置中验证合成图像的质量。
Image generation and image completion are rapidly evolving fields, thanks to machine learning algorithms that are able to realistically replace missing pixels. However, generating large high resolution images, with a large level of details, presents important computational challenges. In this work, we formulate the image generation task as completion of an image where one out of three corners is missing. We then extend this approach to iteratively build larger images with the same level of detail. Our goal is to obtain a scalable methodology to generate high resolution samples typically found in satellite imagery data sets. We introduce a conditional progressive Generative Adversarial Networks (GAN), that generates the missing tile in an image, using as input three initial adjacent tiles encoded in a latent vector by a Wasserstein auto-encoder. We focus on a set of images used by the United Nations Satellite Centre (UNOSAT) to train flood detection tools, and validate the quality of synthetic images in a realistic setup.
