Automatic road extraction from high-resolution remote sensing imagery has various applications like urban planning and automatic navigation. Existing methods for automatic road extraction however, focus on regional accuracy but not on the boundary quality; and most of these road extraction methods yield discontinuous results due to noise and occlusions. To address these two problems, a Boundary and Topological-aware Road extraction Network (BT-RoadNet) is proposed. BT-RoadNet is a coarse-to-fine architecture composed of two encoder-to-decoder networks, a Coarse Map Predicting Module (CMPM) and Fine Map Predicting Module (FMPM). The CMPM learns to predict coarse road segmentation maps, in which a Spatial Context Module (SCM) is employed as a bridge to solve discontinuous problems. The FMPM is used to refine the coarse road maps by learning the difference between the coarse road extraction result and the ground truth. Experiments were conducted on the open Massachusetts Road Dataset, a newly annotated Wuhan University (WHU) Road Dataset, and three large satellite images. Quantitative and qualitative analysis demonstrate that the proposed BT-RoadNet can enhance road network extraction to deal with interruptions caused by shadows and occlusions, extract roads with different scales and materials, and handle roads under construction that have incomplete spectral and geometric properties.

从高分辨率遥感影像中自动提取道路具有各种应用，例如城市规划和自动导航。但是，现有的自动道路提取方法只关注区域精度，而不关注边界质量。由于噪音和遮挡，大多数道路提取方法均会产生不连续的结果。为了解决这两个问题，提出了一种边界和拓扑感知道路提取网络（BT-RoadNet）。BT-RoadNet是由两个编码器到解码器网络，一个粗略地图预测模块（CMPM）和精细地图预测模块（FMPM）组成的从粗到细的体系结构。CMPM学习预测粗略的道路分割图，其中使用空间上下文模块（SCM）作为解决不连续问题的桥梁。FMPM用于通过学习粗略道路提取结果与地面真实情况之间的差异来细化粗略道路地图。实验是在开放的马萨诸塞州道路数据集，新注释的武汉大学（WHU）道路数据集和三个大型卫星图像上进行的。定量和定性分析表明，所提出的BT-RoadNet可以增强道路网络的提取能力，以应对由阴影和遮挡引起的中断，提取具有不同比例和材质的道路，并处理具有不完整光谱和几何特性的在建道路。