Road Centerline Extraction in Complex Urban Scenes From LiDAR Data Based on Multiple Features
Road Centerline Extraction in Complex Urban Scenes From LiDAR Data Based on Multiple Features
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DOI：
10.1109/tgrs.2014.2312793
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发表时间：
2014-04
影响因子：
8.2
通讯作者：
Xiangyun Hu;Yijing Li;J. Shan;Jianqing Zhang;Yongjun Zhang
中科院分区：
文献类型：
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作者：
Xiangyun Hu;Yijing Li;J. Shan;Jianqing Zhang;Yongjun Zhang
由于上下文对象的遮挡和阴影以及复杂的道路结构，从复杂城市地区的图像中自动提取道路是一项非常困难的任务。由于光检测和测距 (LiDAR) 数据明确包含城市场景的直接 3D 信息，并且受遮挡和阴影的影响较小，因此它们是道路检测的良好数据源。本文提出使用多种特征从滤波后剩余的地面点中检测道路中心线。我们方法的主要思想是有效地检测潜在道路中心线的平滑几何基元，并将连接的非道路特征（停车场和裸地）与道路分开。该方法包括三个主要步骤，即基于多个特征的空间聚类，使用自适应均值平移来检测道路中心点，棒张量投票来增强显着线性特征，以及加权霍夫变换来提取道路中心线的弧基元。简而言之，我们将我们的方法表示为均值平移、张量投票、霍夫变换（MTH）。我们使用国际摄影测量学会和遥感城市分类和 3D 建筑重建测试项目的 Vaihingen 和多伦多数据集对该方法进行了评估。 Vaihingen 数据和多伦多数据上提取的路网完整性分别为 81.7% 和 72.3%，正确性分别为 88.4% 和 89.2%，与模板匹配和相位编码盘方法相比，性能最佳。
Automatic extraction of roads from images of complex urban areas is a very difficult task due to the occlusions and shadows of contextual objects, and complicated road structures. As light detection and ranging (LiDAR) data explicitly contain direct 3-D information of the urban scene and are less affected by occlusions and shadows, they are a good data source for road detection. This paper proposes to use multiple features to detect road centerlines from the remaining ground points after filtering. The main idea of our method is to effectively detect smooth geometric primitives of potential road centerlines and to separate the connected nonroad features (parking lots and bare grounds) from the roads. The method consists of three major steps, i.e., spatial clustering based on multiple features using an adaptive mean shift to detect the center points of roads, stick tensor voting to enhance the salient linear features, and a weighted Hough transform to extract the arc primitives of the road centerlines. In short, we denote our method as Mean shift, Tensor voting, Hough transform (MTH). We evaluated the method using the Vaihingen and Toronto data sets from the International Society for Photogrammetry and Remote Sensing Test Project on Urban Classification and 3-D Building Reconstruction. The completeness of the extracted road network on the Vaihingen data and the Toronto data are 81.7% and 72.3%, respectively, and the correctness are 88.4% and 89.2%, respectively, yielding the best performance compared with template matching and phase-coded disk methods.