鉴于激光点云和影像数据成像机理的差异以及现有配准基元的可获取性特点，通常采用基于特征的配准算法修正两者之间的转换关系，其中建筑物的边缘及角点为最常用的特征。针对城区建筑物分布密集、形状相似的问题，提出了一种基于道路线的机载激光雷达数据和高分辨率航空影像自动配准方法。该方法充分利用点云数据提供的高程与强度信息，提取出高精度的规则化道路矢量线；根据初始外方位元素建立点云数据和航空影像的近似变换关系，以道路矢量线在航空影像的投影位置为先验知识，采用改进的道路矩形整体匹配算法得到影像中的道路中心线，获取同名线特征；以同名道路线段的首末端点作为控制信息，利用基于欧拉角的空间后方交会算法解算新的影像外方位元素，实现航空影像和激光雷达数据的配准。实验结果表明，该方法利用道路特征实现航空影像与激光雷达点云的配准，提取特征少且配准精度较高，大大提高了工作效率。

In view of the imaging mechanism differences between the laser point cloud and image data, as well as existing registration primitives accessibility features, the registration based on features is the mainstream algorithm to refine the transformation of the two data sets. The corner points and edges of buildings are frequently used as characteristics. In order to deal with the weakness of building edge detection and reduce matching- related computation, a new automatic registration method based on airborne LiDAR data and high-resolution aerial image of the road information is proposed. Vector road centerlines are extracted from raw LiDAR data and projected onto related aerial images with the use of coarse exterior orientation parameters (EOPs). The corresponding image road features of each LiDAR vector road are determined with the improved total rectangle matching approach. The endpoints of the conjugate road features obtained from the LiDAR data and aerial images are used as ground control points in space resection adjustment to refine the EOPs. Experimental results show that this method characterized by the road features can not only extract fewer features, but also improve the efficiency of data processing in autoregistration of aerial imagery with airborne LiDAR data.