Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference
Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference
复制标题
基于几何特征推断的高分辨率遥感图像乡村道路提取
DOI：
10.3390/ijgi6100314
复制
发表时间：
2017-10-01
影响因子：
3.4
通讯作者：
Li, Yunpeng
中科院分区：
文献类型：
--
作者：
Liu, Jian;Qin, Qiming;Li, Yunpeng
道路信息作为基础地理信息的一种，对于城市规划、交通导航等服务是非常重要的，因此迫切需要及时更新道路信息。学者们提出了各种从遥感图像中提取道路的方法，但大多数方法不适用于材料多样、曲率变化大、遮挡问题严重的农村道路。针对这些问题，本文提出了一种基于几何特征推理的道路提取方法。在该方法中，我们充分利用道路的线性特征，利用选定的样本路段信息构建农村道路的几何知识库。在知识库的基础上，对图像中的平行线对进行识别，并在知识推理的指导下进行分组和连接，最终得到完整的农村公路。以中国所在的湖南省湘潭市为例，验证了该方法的有效性。
Road information as a type of basic geographic information is very important for services such as city planning and traffic navigation, as such there is an urgent need for updating road information in a timely manner. Scholars have proposed various methods of extracting roads from remote sensing images, but most of them are not applicable to rural roads with diverse materials, large curvature changes, and a severe shelter problem. In view of these problems, we propose a road extraction method based on geometric feature inference. In this method, we make full use of the linear characteristics of roads, and construct a geometric knowledge base of rural roads using information on selected sample road segments. Based on the knowledge base, we identify the parallel line pairs in images, and further conduct grouping and connection instructed by knowledge reasoning, and finally obtain complete rural roads. The case study in Xiangtan City of China’s Hunan Province validates the performance of the proposed method.