论文标题
通过集成沟通神经网络与特征成wisecontitional随机场（FPCRF）进行建筑足迹生成
Building Footprint Generation by IntegratingConvolution Neural Network with Feature PairwiseConditional Random Field (FPCRF)
论文作者
论文摘要
建筑足迹图对于许多遥感应用程序至关重要，例如3D建筑建模，城市规划和灾难管理。由于建筑物的复杂性，遥感图像中建筑物足迹的准确而可靠的生成仍然是一项艰巨的任务。在这项工作中，提出了整合卷积神经网络（CNN）和图形模型的端到端建筑足迹生成方法。 CNN用作特征提取器，而图模型可以考虑空间相关性。此外，我们建议将特征成对条件随机场（FPCRF）作为图形模型实现，以保留尖锐的边界和细粒度的分割。实验是在四个不同的数据集上进行的：（1）慕尼黑，巴黎，罗马和苏黎世城市的行星赛卫星图像； （2）来自Potsdam市的ISPRS基准数据，（3）DSTL Kaggle数据集； （4）奥斯丁，芝加哥，基萨普县，蒂罗尔和维也纳的Inria空中图像标记数据。发现使用FPCRF的拟议的端到端建筑足迹生成框架作为图形模型可以进一步提高构建足迹生成的准确性，仅使用CNN，即当前的最新艺术品。
Building footprint maps are vital to many remote sensing applications, such as 3D building modeling, urban planning, and disaster management. Due to the complexity of buildings, the accurate and reliable generation of the building footprint from remote sensing imagery is still a challenging task. In this work, an end-to-end building footprint generation approach that integrates convolution neural network (CNN) and graph model is proposed. CNN serves as the feature extractor, while the graph model can take spatial correlation into consideration. Moreover, we propose to implement the feature pairwise conditional random field (FPCRF) as a graph model to preserve sharp boundaries and fine-grained segmentation. Experiments are conducted on four different datasets: (1) Planetscope satellite imagery of the cities of Munich, Paris, Rome, and Zurich; (2) ISPRS benchmark data from the city of Potsdam, (3) Dstl Kaggle dataset; and (4) Inria Aerial Image Labeling data of Austin, Chicago, Kitsap County, Western Tyrol, and Vienna. It is found that the proposed end-to-end building footprint generation framework with the FPCRF as the graph model can further improve the accuracy of building footprint generation by using only CNN, which is the current state-of-the-art.
