基于等高线特征分析的LiDAR建筑物与道路提取
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摘要
新兴的LiDAR系统可以直接获取地球表面或其上目标三维数据,其“点云”数据形式具有不规则和离散特点,用户需要采取专业的数据处理方法才能获得所需的3-D信息,不同的用途导致各式各样的数据处理方法。
利用LiDAR '‘点云”数据提取建筑物和道路是目前学界研究的热点,滤波-分割算法(Filtering-Segment, F-S)是通常的提取算法。F-S法分两步进行,第一步是滤波,先采用形态学滤波法或表面估计滤波法,提取数字地面模型(Digital Terrain Model, DTM),然后将LiDAR“点云”数据直接获取的数字表面模型(Digital Surface Model, DSM)与DTM做差,通过预设的高差阈值,将DSM点群分为地面点群和非地面点群；第二步是分割,包括建筑物分割和道路分割。建筑物分割是对非地面点群进行分割,利用建筑物的几何特性或是融合其他信息,如多光谱信息,将植被点群去掉,提取出建筑物；道路分割则是对地面点群进行分割,通常是借助LiDAR所获得的强度信息,将非道路点群,主要是绿地等点群去除,提取出道路。
目前的F-S算法的弱点主要有三个方面：适用范围、正确率以及实用性。F-S算法大多只适用于平坦的城区,在地形起伏的山区效果不太理想；无论是建筑物分割,还是道路分割,F-S算法的处理结果都会有一些非建筑物或是非道路信息的存在,降低了分割的正确率；实用性主要是针对道路分割,现有的道路分割是以强度信息作为辅助数据,但并非所有的LiDAR系统都可以获得强度信息,当没有强度信息时,算法的实用性就受到限制。
由此提出基于等高线滤波的分割法(Filtering Based on Contour-Segment, Fc-S)。Fc-S算法首先采用等高线滤波法(Filtering Based on Contour, Fc法)进行滤波,即根据DSM等高线的特征,如闭合性、首尾点距离等,采用阈值法自动提取出属于自然地面的等高线(段),获得初始地面点群；内插地面点群生成初始DTM后,使用迭代逼近法生成最终的(精确的)DTM。利用DSM与DTM之差将LiDAR数据分为地面点群和非地面点群后,采用边缘信息、梯度阈值以及面积阈值等方法从非地面点群中去除植被点群,提取出建筑物点群,并采用迭代逼近的方法对建筑物进行精化；利用目标区光谱信息,从地面点群中提取出道路信息,并进一步利用道路的几何特性去除停车场等与道路光谱信息相近的非道路信息。
为进一步提高提取建筑物的效率,利用等高线形状分析技术(Contour Shape Analysis, CSA)改进Fc-S算法。CSA算法根据DSM中建筑物等高线的几何形状特征,采用形状分析法直接提取建筑物等高线；然后规则化处理以获取建筑物轮廓线。
通过美国弗吉尼亚州的Wytheville的实测LiDAR“点云”数据的试验,证明Fc-S法和CSA法都可以适用于地形起伏的山区,且提取的建筑物和道路的正确率较高,新算法的实用性较强。试验对比滤波的代表算法Masaharu表面估计滤波法,证明Fc滤波算法在山区的效率更高；试验对比尤红建、李涛等人的建筑物分割算法,证明Fc-S算法在山区的建筑物分割精度更高；与Fc-S法相比,CSA算法的效率又有进一步的提高,建筑物轮廓信息也更丰富。利用Fc-S法或CSA法提取建筑物存在的不足之处在于,一些小的建筑物没有被提取出来,可能与所取的阈值有关,需要进一步的研究。
In developing LIDAR system, three-dimensional data from earth surface or object can be obtained directly. The LiDAR data, which is shown as point-cloud, has two organizing forms:irregular and dispersed. The three-dimensional (3D) information is obtained by end user with professional methods of data processing. Different purposes require different ways of data processing.
Extracting buildings and roads from LiDAR point cloud is a hot research topic at present. Generally, the Filtering-Segment (F-S) algorithm is a usual extracting algorithm. The F-S algorithm has two steps. First, the LiDAR data is smoothed by a morphological-based filter or a surface estimation filter to generate the Digital Terrain Model (DTM). The difference between the DTM and the Digital Surface Model (DSM) derived from the LiDAR data may then be classified into ground data and non-ground data by an initial height threshold. Second, the buildings are segmented from the non-ground point clouds by geometric characteristics or fusing other information such as multispectral information, in which vegetation points may be removed for the better extraction of buildings. The roads are segmented from the ground point clouds by usually removing the vegetation canopies via the intensity information of LiDAR.
At present, there are three weak points in the F-S method, i.e., application scope, accuracy rate and practicality. The F-S method is mostly suitable for the flat area, but not for the mountainous area. In addition, there still is some non-building or non-road data after building or road segmentation using the F-S method, which reduces segmentation accuracy rate. In the existing road-segment algorithms, intensity information is used as accessorial information. But not all Lidar systems can obtain the intensity information. Therefore, when without intensity information, the practicality is strained.
This paper presents a new segment method which is called the Filtering Algorithm Based on Contour-Segment (Fc-S). First, the Fc-S algorithm is implemented mainly by filtering based on the characteristics of DSM contour, that is, on the basis of the characteristics of DSM contour, such as the closure feature or the distance between the start and end points of the contour line, the contour line which belongs to natural ground is extracted by threshold method automatically, then the initialized ground point cloud is acquired and interpolated to obtain initialized DTM. The refined DTM is generated by an iterative approximation method, i.e., Filtering Based on Contour (Fc). After the point cloud has been classified into the ground-and non-ground point sets, the height threshold, the edge information, the gradient and area threshold value are adopted to remove the vegetation points from the non-ground point cloud, and thus the building point cloud is extracted. The refined building points are obtained by such iterative approximation method. As the object area's spectrum information can be acquired by digital camera, the roads may be extracted from the spectrum information. The parking lot and other areas that have a spectrum similar to that of the road could be removed by the geometric characteristics of road.
To improve the efficiency of building extraction by the Fc-S algorithm, the Contour Shape Analysis (CSA) is used. Based on the geometric configuration feature of building, the building contours are extracted by the CSA method, and then the building outlines are derived with regular processing.
The method of Fc-S and CSA are tested with Lidar point cloud from Wytheville County, Virginia of America. The results show that the both methods are suitable for mountainous areas, and a higher accuracy can be achived for building and road extraction. Therefore these methods have better practicability. In the mountainous area, Fc algorithm has more efficiency than the Masaharu's algorithm, Fc-S algorithm for building extraction has more accuracy rate than You Hongjian's and LiTao's algorithm, CSA algorithm has more efficiency and details of building outlines than Fc-S algorithm. However, some small buildings are rejected, which may be related to the chosen threshold. This is an insufficient of Fc-S algorithm and CSA algorithm, which needs farther research.
利用LiDAR '‘点云”数据提取建筑物和道路是目前学界研究的热点,滤波-分割算法(Filtering-Segment, F-S)是通常的提取算法。F-S法分两步进行,第一步是滤波,先采用形态学滤波法或表面估计滤波法,提取数字地面模型(Digital Terrain Model, DTM),然后将LiDAR“点云”数据直接获取的数字表面模型(Digital Surface Model, DSM)与DTM做差,通过预设的高差阈值,将DSM点群分为地面点群和非地面点群；第二步是分割,包括建筑物分割和道路分割。建筑物分割是对非地面点群进行分割,利用建筑物的几何特性或是融合其他信息,如多光谱信息,将植被点群去掉,提取出建筑物；道路分割则是对地面点群进行分割,通常是借助LiDAR所获得的强度信息,将非道路点群,主要是绿地等点群去除,提取出道路。
目前的F-S算法的弱点主要有三个方面：适用范围、正确率以及实用性。F-S算法大多只适用于平坦的城区,在地形起伏的山区效果不太理想；无论是建筑物分割,还是道路分割,F-S算法的处理结果都会有一些非建筑物或是非道路信息的存在,降低了分割的正确率；实用性主要是针对道路分割,现有的道路分割是以强度信息作为辅助数据,但并非所有的LiDAR系统都可以获得强度信息,当没有强度信息时,算法的实用性就受到限制。
由此提出基于等高线滤波的分割法(Filtering Based on Contour-Segment, Fc-S)。Fc-S算法首先采用等高线滤波法(Filtering Based on Contour, Fc法)进行滤波,即根据DSM等高线的特征,如闭合性、首尾点距离等,采用阈值法自动提取出属于自然地面的等高线(段),获得初始地面点群；内插地面点群生成初始DTM后,使用迭代逼近法生成最终的(精确的)DTM。利用DSM与DTM之差将LiDAR数据分为地面点群和非地面点群后,采用边缘信息、梯度阈值以及面积阈值等方法从非地面点群中去除植被点群,提取出建筑物点群,并采用迭代逼近的方法对建筑物进行精化；利用目标区光谱信息,从地面点群中提取出道路信息,并进一步利用道路的几何特性去除停车场等与道路光谱信息相近的非道路信息。
为进一步提高提取建筑物的效率,利用等高线形状分析技术(Contour Shape Analysis, CSA)改进Fc-S算法。CSA算法根据DSM中建筑物等高线的几何形状特征,采用形状分析法直接提取建筑物等高线；然后规则化处理以获取建筑物轮廓线。
通过美国弗吉尼亚州的Wytheville的实测LiDAR“点云”数据的试验,证明Fc-S法和CSA法都可以适用于地形起伏的山区,且提取的建筑物和道路的正确率较高,新算法的实用性较强。试验对比滤波的代表算法Masaharu表面估计滤波法,证明Fc滤波算法在山区的效率更高；试验对比尤红建、李涛等人的建筑物分割算法,证明Fc-S算法在山区的建筑物分割精度更高；与Fc-S法相比,CSA算法的效率又有进一步的提高,建筑物轮廓信息也更丰富。利用Fc-S法或CSA法提取建筑物存在的不足之处在于,一些小的建筑物没有被提取出来,可能与所取的阈值有关,需要进一步的研究。
In developing LIDAR system, three-dimensional data from earth surface or object can be obtained directly. The LiDAR data, which is shown as point-cloud, has two organizing forms:irregular and dispersed. The three-dimensional (3D) information is obtained by end user with professional methods of data processing. Different purposes require different ways of data processing.
Extracting buildings and roads from LiDAR point cloud is a hot research topic at present. Generally, the Filtering-Segment (F-S) algorithm is a usual extracting algorithm. The F-S algorithm has two steps. First, the LiDAR data is smoothed by a morphological-based filter or a surface estimation filter to generate the Digital Terrain Model (DTM). The difference between the DTM and the Digital Surface Model (DSM) derived from the LiDAR data may then be classified into ground data and non-ground data by an initial height threshold. Second, the buildings are segmented from the non-ground point clouds by geometric characteristics or fusing other information such as multispectral information, in which vegetation points may be removed for the better extraction of buildings. The roads are segmented from the ground point clouds by usually removing the vegetation canopies via the intensity information of LiDAR.
At present, there are three weak points in the F-S method, i.e., application scope, accuracy rate and practicality. The F-S method is mostly suitable for the flat area, but not for the mountainous area. In addition, there still is some non-building or non-road data after building or road segmentation using the F-S method, which reduces segmentation accuracy rate. In the existing road-segment algorithms, intensity information is used as accessorial information. But not all Lidar systems can obtain the intensity information. Therefore, when without intensity information, the practicality is strained.
This paper presents a new segment method which is called the Filtering Algorithm Based on Contour-Segment (Fc-S). First, the Fc-S algorithm is implemented mainly by filtering based on the characteristics of DSM contour, that is, on the basis of the characteristics of DSM contour, such as the closure feature or the distance between the start and end points of the contour line, the contour line which belongs to natural ground is extracted by threshold method automatically, then the initialized ground point cloud is acquired and interpolated to obtain initialized DTM. The refined DTM is generated by an iterative approximation method, i.e., Filtering Based on Contour (Fc). After the point cloud has been classified into the ground-and non-ground point sets, the height threshold, the edge information, the gradient and area threshold value are adopted to remove the vegetation points from the non-ground point cloud, and thus the building point cloud is extracted. The refined building points are obtained by such iterative approximation method. As the object area's spectrum information can be acquired by digital camera, the roads may be extracted from the spectrum information. The parking lot and other areas that have a spectrum similar to that of the road could be removed by the geometric characteristics of road.
To improve the efficiency of building extraction by the Fc-S algorithm, the Contour Shape Analysis (CSA) is used. Based on the geometric configuration feature of building, the building contours are extracted by the CSA method, and then the building outlines are derived with regular processing.
The method of Fc-S and CSA are tested with Lidar point cloud from Wytheville County, Virginia of America. The results show that the both methods are suitable for mountainous areas, and a higher accuracy can be achived for building and road extraction. Therefore these methods have better practicability. In the mountainous area, Fc algorithm has more efficiency than the Masaharu's algorithm, Fc-S algorithm for building extraction has more accuracy rate than You Hongjian's and LiTao's algorithm, CSA algorithm has more efficiency and details of building outlines than Fc-S algorithm. However, some small buildings are rejected, which may be related to the chosen threshold. This is an insufficient of Fc-S algorithm and CSA algorithm, which needs farther research.
引文
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