论文标题
跑道提取和改进了太空图像的映射
Runway Extraction and Improved Mapping from Space Imagery
论文作者
论文摘要
用于监视关键基础设施（例如机场跑道）的更改检测方法代表了救灾和城市规划的重要功能。目前的工作确定了两个生成的对抗网络（GAN）体系结构，它们在合理的跑道图和卫星图像之间可逆地翻译。我们从相同的角度使用配对图像（卫星映射）说明了训练能力，并使用Pix2Pix架构或条件gan。在没有可用对的情况下，我们同样表明，具有四个网络头的Cyclegan体系结构（歧视器生成器对）也可以提供从原始图像像素到轮廓或特征图的有效风格转移。为了强调跑道和柏油碎石的边界，我们通过实验表明，传统的灰色棕褐色地图不是必需的训练输入，但可以通过更高的对比度映射调色板（Red-Black）来增强Sharper Runway Borgaries。我们预览了一种潜在的新颖用例（称为“ Sketch2satellite”），其中人粗略地绘制了当前的跑道边界，并自动化了合理的卫星图像的机器输出。最后，我们确定了错误的跑道图的示例，其中已发布的卫星和映射跑道不同意，但自动化更新可使用gans呈现正确的地图。
Change detection methods applied to monitoring key infrastructure like airport runways represent an important capability for disaster relief and urban planning. The present work identifies two generative adversarial networks (GAN) architectures that translate reversibly between plausible runway maps and satellite imagery. We illustrate the training capability using paired images (satellite-map) from the same point of view and using the Pix2Pix architecture or conditional GANs. In the absence of available pairs, we likewise show that CycleGAN architectures with four network heads (discriminator-generator pairs) can also provide effective style transfer from raw image pixels to outline or feature maps. To emphasize the runway and tarmac boundaries, we experimentally show that the traditional grey-tan map palette is not a required training input but can be augmented by higher contrast mapping palettes (red-black) for sharper runway boundaries. We preview a potentially novel use case (called "sketch2satellite") where a human roughly draws the current runway boundaries and automates the machine output of plausible satellite images. Finally, we identify examples of faulty runway maps where the published satellite and mapped runways disagree but an automated update renders the correct map using GANs.
