School of Electronic Science, National University of Defense Technology (NUDT), Changsha 410073, China.
School of Business, University of Leeds, Leeds LS2 9JT, UK.
Sensors (Basel). 2019 Mar 7;19(5):1162. doi: 10.3390/s19051162.
With the rapid development of intelligent transportation, there comes huge demands for high-precision road network maps. However, due to the complex road spectral performance, it is very challenging to extract road networks with complete topologies. Based on the topological networks produced by previous road extraction methods, in this paper, we propose a Multi-conditional Generative Adversarial Network (McGAN) to obtain complete road networks by refining the imperfect road topology. The proposed McGAN, which is composed of two discriminators and a generator, takes both original remote sensing image and the initial road network produced by existing road extraction methods as input. The first discriminator employs the original spectral information to instruct the reconstruction, and the other discriminator aims to refine the road network topology. Such a structure makes the generator capable of receiving both spectral and topological information of the road region, thus producing more complete road networks compared with the initial road network. Three different datasets were used to compare McGan with several recent approaches, which showed that the proposed method significantly improved the precision and recall of the road networks, and also worked well for those road regions where previous methods could hardly obtain complete structures.
随着智能交通的快速发展，对高精度道路网络地图的需求也越来越大。然而，由于道路光谱性能复杂，提取具有完整拓扑结构的道路网络非常具有挑战性。基于之前道路提取方法生成的拓扑网络，本文提出了一种多条件生成对抗网络（McGAN），通过细化不完整的道路拓扑结构来获取完整的道路网络。所提出的 McGAN 由两个鉴别器和一个生成器组成，它将原始遥感图像和现有道路提取方法生成的初始道路网络作为输入。第一个鉴别器使用原始光谱信息来指导重建，另一个鉴别器旨在细化道路网络拓扑结构。这种结构使生成器能够接收道路区域的光谱和拓扑信息，从而与初始道路网络相比生成更完整的道路网络。我们使用了三个不同的数据集来比较 McGan 与几个最近的方法，结果表明，所提出的方法显著提高了道路网络的精度和召回率，并且对于那些以前的方法很难获得完整结构的道路区域也能很好地工作。
