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摘要: 面向机载LiDAR数据的道路提取算法的常用数据结构存在局限: 2D格网及TIN表达多次回波数据时存在的信息损失会影响提取结果的完整性且提取结果为2D形式; 点云的空间结构及拓扑信息难以利用, 由此导致算法设计的困难.为此, 提出了一种基于灰度体元模型的3D道路提取算法.算法首先将LiDAR数据规则化为灰度体元模型(灰度为体元内LiDAR点的平均强度值的量化表示); 然后选取道路种子体元进而搜寻并标记种子及其3D连通区域为道路体元; 最后利用数学形态学优化提取结果.基于ISPRS提供的包含不同复杂程度的城区路网LiDAR数据测试"邻域尺度"和"灰度差阈值"参数的敏感性及提出的算法的精度.实验结果表明: 56邻域为最佳邻域尺度、2为最佳灰度差阈值; 道路提取的平均质量、完整度及正确率分别为70%、86.77%及81.13%;对相对平坦的单层路网及起伏较大的复杂路网均可成功提取.Abstract: 2D grid, TIN and point cloud, which are the commonly used methods to represent LiDAR data for road extraction, have defects, for example, it is difficult for 2D grid and TIN to represent multiple return LiDAR data and thus influences the integrity of grid and TIN-based road extraction results and their extraction results are 2D, it is difficult for point cloud to use its topological and adjacent information and thus leads to the difficulty in the design of point-based road extraction algorithm. To overcome these restrictions, a grayscale voxel model (GVM) based 3D road extraction algorithm is presented. LiDAR data are regularized into GVM in which the grayscale of a voxel corresponds to the quantized mean intensity of the LIDAR points within the voxel. Road seed voxels are selected and then seeds and their 3D connected regions are labeled as road voxels. The extracted road result is optimized using mathematical morphology. ISPRS urban LiDAR datasets, which are representative of road networks of different complexities, are used to analyze the sensitivity of "adjacency size" and "intensity difference threshold" parameters and assess the accuracy of the proposed algorithm quantitatively. The experiment results indicate that: 1) 56-adjacency is the optimal adjacency size and 2 is the optimal intensity difference threshold; 2) The average quality, completeness and correctness of road extraction were 70%, 86.77% and 81.13%, respectively; 3) Roads in the relatively flat single layer road network and the undulating complex road network can both be successfully extracted.
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Key words:
- LiDAR /
- road extraction /
- grayscale voxel model /
- intensity /
- 3D
1) 本文责任编委 吴毅红 -
表 1 各不同邻域尺度及$T_{i}$下的道路提取算法总误差
Table 1 Total errors of the proposed algorithm with different adjacent sizes and $T_{i}$
CSite2/CSite3提取结果总误差(%) $T_{i}$ 6邻域 18邻域 26邻域 56邻域 64邻域 1 46.30/40.64 32.26/31.87 25.89/24.15 20.47/17.94 17.98/12.44 2 39.57/32.32 28.34/25.02 19.07/18.87 12.81/8.29 18.73/15.65 3 34.25/30.06 26.16/19.21 20.83/10.33 15.58/13.84 23.16/20.45 4 37.29/33.12 30.21/20.04 24.56/12.87 26.74/16.51 29.86/25.31 
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表 2 提出的算法的精度
Table 2 The accuracy of the proposed algorithm
数据 $R_{com}$(%) $R_{cor}$(%) $R_{q}$(%) CSite2 84.83 80.76 70.57 CSite3 88.71 81.50 73.84
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表 3 Terrasolid道路提取精度
Table 3 The road extraction accuracy of Terrasolid
数据 $R_{com}$ (%) $R_{cor}$ (%) $R_{q}$ (%) CSite2 88.2 64.1 59.1 CSite3 93.8 56.9 54.8
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