论文标题
卫星图像中基于CNN的语义变化检测
CNN-Based Semantic Change Detection in Satellite Imagery
论文作者
论文摘要
及时的灾难风险管理需要准确的路线图和迅速损害评估。目前，这是由手动标记受影响区域的卫星图像的志愿者完成的，但是此过程缓慢且通常容易出错。分割算法可以应用于卫星图像以检测道路网络。但是，现有的方法不适合灾难造成的区域，因为它们对道路网络拓扑做出了假设，在这些情况下可能不再有效。本文中，我们提出了一个基于CNN的框架，用于通过检测前降沙斯特图像中的变化来识别disasaster图像中的可访问道路。图理论与CNN输出结合使用，用于通过OpenStreetMap数据检测道路网络的语义变化。我们的结果通过从DigitalGlobe获得的印度尼西亚帕鲁市受海啸影响地区的数据进行了验证。
Timely disaster risk management requires accurate road maps and prompt damage assessment. Currently, this is done by volunteers manually marking satellite imagery of affected areas but this process is slow and often error-prone. Segmentation algorithms can be applied to satellite images to detect road networks. However, existing methods are unsuitable for disaster-struck areas as they make assumptions about the road network topology which may no longer be valid in these scenarios. Herein, we propose a CNN-based framework for identifying accessible roads in post-disaster imagery by detecting changes from pre-disaster imagery. Graph theory is combined with the CNN output for detecting semantic changes in road networks with OpenStreetMap data. Our results are validated with data of a tsunami-affected region in Palu, Indonesia acquired from DigitalGlobe.
