影像与LiDAR数据信息融合复杂场景下的道路自动提取

城区的道路自动提取受场景复杂度的影响一直是极具挑战的任务，尤其是阴影和遮挡较严重地区的道路提取难度较大。结合LiDAR数据和高分辨率遥感影像，提出一种自动道路提取方法。该方法首先对滤波后的点云强度信息获取初始道路中线及道路关键点；将地面点云强度，离散度及高分辨率遥感影像光谱数据多重信息融合建立道路模型，并以优化后的道路关键点作为种子点利用动态规划计算模型最优解，进一步提取道路网。试验表明，该方法在城市复杂场景下的自动提取主要道路是有效的。

Automatic Road Extraction in Complex Scenes Based on Information Fusion from LiDAR Data and Remote Sensing Imagery

Automatic road extraction from remote sensing images in urban area has been a very challenging task due to the complexity of the scene, especially in the occluded or shadowed areas. This paper proposes an integrated method to fuse LiDAR data and high resolution imagery for automatic extraction of road centrelines. Firstly the LiDAR point cloud is filtered to get the ground points whose intensity data is used to detect initial road centrelines and key points of the roads. A road model is then built on the intensity and dispersion of the ground points as well as spectral information obtained from the high resolution image. Based on the model, the dynamic programming algorithm is applied to find the optimal road centrelines linking the key points which are selected by evaluation. The experimental results indicate its effectiveness in automatic road extraction in urban and complex scenes.