基于标记点过程的机载激光扫描点云建筑物提取
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摘要
经过近二十多年的发展,机载激光扫描系统(Airborne laser scanning system, ALS)已经从最初的实验室研究阶段发展成为成熟的商业产品,具备数据采集速度快、处理周期短、高精度、高密度、获取成本较低等优点,能够直接、快速的采集大面积区域的空间三维信息,正日益成为空间数据采集技术的一个新的发展方向。机载激光扫描数据被广泛的应用于3D城市建模、电力线走廊三维制图、数字高程模型生成、植被检测以及环境研究等领域。建筑物作为人们生活和工作的重要场所,是城市空间中的重要实体,其位置边界信息是地籍图生成、建筑物三维重建、地图更新以及变形监测等应用方面的宝贵数据源。因此,研究机载激光扫描数据中的建筑物目标提取具有非常重要的意义。
从机载激光扫描数据中提取建筑物目标一直是摄影测量与遥感、计算机视觉等领域的研究热点。近年来,国内外对基于机载激光扫描数据的建筑物目标提取进行了广泛的研究。但是由于建筑物及其周围地形的多样性和复杂性,使建筑物的提取具有较多的困难,至今还没有一种适用于任何数据质量以及任何场景的建筑物提取方法。同时由于机载激光扫描数据具有离散随机性以及分布不均匀性等特性,对建筑物的提取也带来了一定的困难。针对上述问题,本文研究了利用标记点过程从机载激光扫描数据中直接提取建筑物的方法。该方法是一种基于目标的、稳健的且能准确从点云数据中提取建筑物目标的方法。该方法首先构建出面向建筑物提取的Gibbs能量模型,然后对该能量模型进行优化求解以获取初步的建筑物区域,最后对初步获取的建筑物区域进行精细处理,从而得到精确的建筑物轮廓。本文的主要研究内容如下：
1、介绍了本文的研究背景和意义以及机载激光扫描系统及其应用。针对机载激光扫描数据的特点以及目前建筑物提取的难点,提出了本文的研究目标。针对机载激光扫描数据中建筑物目标的提取以及标记点过程方法用于几何目标提取这两个方面的研究现状进行了综述,总结了目前从机载激光扫描数据中进行建筑物目标提取的难点和不足及其可能的发展趋势。
2、介绍了利用标记点过程方法进行几何目标提取的研究背景,并简要叙述了点过程和标记点过程的理论。根据建筑物在机载激光扫描数据中的几何形状特点,确定了将长方体作为建筑物的几何标记模型。根据建筑物在散乱点云中的结构特征以及建筑物目标之间的相互关系构建了能直接从点云数据中提取建筑物目标的Gibbs能量模型,有效的将建筑物目标的空间信息和空间关系引入到新构建的Gibbs能量模型中,为建筑物目标的准确提取奠定了基础。
3、针对构建出的Gibbs能量模型的函数属于非线性函数,难以从理论上进行分析并获取对应的解析解的情况,本文采用RJMCMC方法与模拟退火算法相结合的方法获取Gibbs能量模型的全局最优解。虽然该求解方法能够从任何的初始状态下收敛到全局最优解,但运行效率却比较低,故本文对该求解方法进行了优化,以有效的提高算法的运行效率,节省运行时间。
4、求解Gibbs能量模型后可以初步的获取建筑物区域。但是这些区域的边界可能并不精确,也不完整,同一个建筑物可能由多个区域构成,同时其中也可能存在一些错误提取的区域。因此本文对初步获取的建筑物区域进行精细化处理。利用建筑物目标的几何属性特征结合区域生长算法剔除错误提取的地面点、树冠点、噪声点以及树冠等非建筑物目标；然后对邻接区域进行合并并利用改进的凸包算法求取每个建筑物目标的精确轮廓。
5、采用ISPRS机构提供的基准测试数据验证本文提出的算法的准确性和有效性。对本文提出的算法中用到的参数进行分析和探讨,并对实验结果进行精度评价与分析。最后以F1Measure作为度量建筑物目标提取的精度评价标准,将本文提出的方法与其它的建筑物提取方法进行比较。详尽的实验结果及其评价数据证明了本文提出的算法的准确性和有效性。
At the developments of nearly twenty years, airborne laser scanning (ALS) systems have become an accepted means of capturing accurate spatial data in an efficient way because of directly obtain the3D coordinates of objects, short data acquisition and processing times, relatively high accuracy and point density, and reduction in acquisition costs. The ALS data is widely used in numerous applications such as3D city modeling,3D mapping of power lines corridors, digital elevation models generation (DEMs), and environmental studies. Building is an important place for people to live and work. And building is also an important entity in the city space. Data of building outlines are an invaluable data source for automatic generation of cadastral maps,3D building reconstruction, change detection, and so on. Therefore, it is very significant to extract building from ALS data.
Extracting buildings from ALS data has been an active topic in the field of photogrammetry, remote sensing, and computer vision. Different approaches for extracting buildings have been reported in the last decade. But due to the diversity and complexity of building and its surrounding terrain, there are still a lot of difficulties in building extraction. So far there is not a good building extraction method which is suitable for any quality data and any scene. On the other hand, due to the discrete randomness and inhomogeneous distribution of ALS data, building extraction is become more difficult. Aiming at these problems, this paper investigates the method of building extraction directly from ALS data based on marked point process. This method is an object-oriented and robust method. Firstly, the Gibbs energy model of building objects is modeled. Then, an optimized solution strategy was put forward to find a maximum a posteriori estimate of the Gibbs energy model. Finally, a refinement operator is performed to extract building roof outlines. The main research context of this paper as follows:
1. The research background and significance of this paper as well as ALS system and its application are introduced. After the discussion of characteristics of ALS data and existing problems of building extraction, the research objectives and contents of this paper are proposed. Then the related works of building extraction from ALS data and geometric feature extraction by marked point process are reviewed. Moreover, the difficulties and possible prospects of building extraction from ALS data are pointed out.
2. The background and theory of point process and marked point process are introduced. According to the geometric characteristics of buildings, the marked space consists of marked point process of cuboids are defined to describe buildings. Then the Gibbs energy model which can extract building directly from ALS data is build according to the geometric feature and relationship of the objects in the point cloud data. This model contains both a data coherence term which fits the objects to the data and a prior term which incorporates the prior knowledge of the object geometric properties. Spatial relationship and information of buildings are introduced to Gibbs energy model effectively. It laid a foundation for building extraction.
3. For the function of Gibbs energy model is a nonlinear function, it is difficult to analysis and obtain analytical solution in theory. Therefore, Reverse Jump Markov Chain Monte Carlo (RJMCMC) coupled with simulated annealing is used to find a maximum a posteriori estimate of the number, the locations and sizes of building objects described by the Gibbs energy model. This solving algorithm can converging to global optimal solution from any initial state. But just as all the sampling-based approaches, the proposed method has also a heavy time cost because of huge computing burden. To improve the efficiency of method, the solving algorithm has been optimized and improved.
4. Once the optimal solution of the Gibbs energy U(x) is obtained by RJMCMC coupled with simulated annealing, the building objects of ALS data are detected. However, a lot of objects with the similar shapes of cuboid or with low heights might be detected as buildings as well. On the other hand, one building may have attachments or different levels, resulting in incomplete detection. The above cases lead to bad qualities of building footprints extraction. Hence, a refinement operator is performed to extract building roof outlines from the extracted building objects by filtering false buildings and merging the connecting parts of buildings. Finally, the modified convex hull method is applied to extract the outlines of detected building objects,.
5. The proposed model was tested with the ALS dataset of Vaihingen, Germany and Toronto (5datasets in total), in the context of the 'ISPRS Test Project on Urban Classification and3D Building Reconstruction'. This paper analysis and discuss the parameters which were used in the proposed algorithm. And the comprehensive evaluation and comparison on the detected outlines of buildings were performed by ISPRS Workgroup III/4based on the completeness, the correctness, and the quality of the results both on a per-object and on a per-area level. On the other hand, F1measure is used to compare the results of building extraction from the proposed method and other methods. The experimental results show the proposed building extraction method can get good extraction performance in terms of pixel based and object based completeness, correctness, and RMSEs. It also shows that the proposed method provides a functional and effective solution for directly extracting building objects from ALS data of a variety of scenes without resampling or gridding the input data.
从机载激光扫描数据中提取建筑物目标一直是摄影测量与遥感、计算机视觉等领域的研究热点。近年来,国内外对基于机载激光扫描数据的建筑物目标提取进行了广泛的研究。但是由于建筑物及其周围地形的多样性和复杂性,使建筑物的提取具有较多的困难,至今还没有一种适用于任何数据质量以及任何场景的建筑物提取方法。同时由于机载激光扫描数据具有离散随机性以及分布不均匀性等特性,对建筑物的提取也带来了一定的困难。针对上述问题,本文研究了利用标记点过程从机载激光扫描数据中直接提取建筑物的方法。该方法是一种基于目标的、稳健的且能准确从点云数据中提取建筑物目标的方法。该方法首先构建出面向建筑物提取的Gibbs能量模型,然后对该能量模型进行优化求解以获取初步的建筑物区域,最后对初步获取的建筑物区域进行精细处理,从而得到精确的建筑物轮廓。本文的主要研究内容如下：
1、介绍了本文的研究背景和意义以及机载激光扫描系统及其应用。针对机载激光扫描数据的特点以及目前建筑物提取的难点,提出了本文的研究目标。针对机载激光扫描数据中建筑物目标的提取以及标记点过程方法用于几何目标提取这两个方面的研究现状进行了综述,总结了目前从机载激光扫描数据中进行建筑物目标提取的难点和不足及其可能的发展趋势。
2、介绍了利用标记点过程方法进行几何目标提取的研究背景,并简要叙述了点过程和标记点过程的理论。根据建筑物在机载激光扫描数据中的几何形状特点,确定了将长方体作为建筑物的几何标记模型。根据建筑物在散乱点云中的结构特征以及建筑物目标之间的相互关系构建了能直接从点云数据中提取建筑物目标的Gibbs能量模型,有效的将建筑物目标的空间信息和空间关系引入到新构建的Gibbs能量模型中,为建筑物目标的准确提取奠定了基础。
3、针对构建出的Gibbs能量模型的函数属于非线性函数,难以从理论上进行分析并获取对应的解析解的情况,本文采用RJMCMC方法与模拟退火算法相结合的方法获取Gibbs能量模型的全局最优解。虽然该求解方法能够从任何的初始状态下收敛到全局最优解,但运行效率却比较低,故本文对该求解方法进行了优化,以有效的提高算法的运行效率,节省运行时间。
4、求解Gibbs能量模型后可以初步的获取建筑物区域。但是这些区域的边界可能并不精确,也不完整,同一个建筑物可能由多个区域构成,同时其中也可能存在一些错误提取的区域。因此本文对初步获取的建筑物区域进行精细化处理。利用建筑物目标的几何属性特征结合区域生长算法剔除错误提取的地面点、树冠点、噪声点以及树冠等非建筑物目标；然后对邻接区域进行合并并利用改进的凸包算法求取每个建筑物目标的精确轮廓。
5、采用ISPRS机构提供的基准测试数据验证本文提出的算法的准确性和有效性。对本文提出的算法中用到的参数进行分析和探讨,并对实验结果进行精度评价与分析。最后以F1Measure作为度量建筑物目标提取的精度评价标准,将本文提出的方法与其它的建筑物提取方法进行比较。详尽的实验结果及其评价数据证明了本文提出的算法的准确性和有效性。
At the developments of nearly twenty years, airborne laser scanning (ALS) systems have become an accepted means of capturing accurate spatial data in an efficient way because of directly obtain the3D coordinates of objects, short data acquisition and processing times, relatively high accuracy and point density, and reduction in acquisition costs. The ALS data is widely used in numerous applications such as3D city modeling,3D mapping of power lines corridors, digital elevation models generation (DEMs), and environmental studies. Building is an important place for people to live and work. And building is also an important entity in the city space. Data of building outlines are an invaluable data source for automatic generation of cadastral maps,3D building reconstruction, change detection, and so on. Therefore, it is very significant to extract building from ALS data.
Extracting buildings from ALS data has been an active topic in the field of photogrammetry, remote sensing, and computer vision. Different approaches for extracting buildings have been reported in the last decade. But due to the diversity and complexity of building and its surrounding terrain, there are still a lot of difficulties in building extraction. So far there is not a good building extraction method which is suitable for any quality data and any scene. On the other hand, due to the discrete randomness and inhomogeneous distribution of ALS data, building extraction is become more difficult. Aiming at these problems, this paper investigates the method of building extraction directly from ALS data based on marked point process. This method is an object-oriented and robust method. Firstly, the Gibbs energy model of building objects is modeled. Then, an optimized solution strategy was put forward to find a maximum a posteriori estimate of the Gibbs energy model. Finally, a refinement operator is performed to extract building roof outlines. The main research context of this paper as follows:
1. The research background and significance of this paper as well as ALS system and its application are introduced. After the discussion of characteristics of ALS data and existing problems of building extraction, the research objectives and contents of this paper are proposed. Then the related works of building extraction from ALS data and geometric feature extraction by marked point process are reviewed. Moreover, the difficulties and possible prospects of building extraction from ALS data are pointed out.
2. The background and theory of point process and marked point process are introduced. According to the geometric characteristics of buildings, the marked space consists of marked point process of cuboids are defined to describe buildings. Then the Gibbs energy model which can extract building directly from ALS data is build according to the geometric feature and relationship of the objects in the point cloud data. This model contains both a data coherence term which fits the objects to the data and a prior term which incorporates the prior knowledge of the object geometric properties. Spatial relationship and information of buildings are introduced to Gibbs energy model effectively. It laid a foundation for building extraction.
3. For the function of Gibbs energy model is a nonlinear function, it is difficult to analysis and obtain analytical solution in theory. Therefore, Reverse Jump Markov Chain Monte Carlo (RJMCMC) coupled with simulated annealing is used to find a maximum a posteriori estimate of the number, the locations and sizes of building objects described by the Gibbs energy model. This solving algorithm can converging to global optimal solution from any initial state. But just as all the sampling-based approaches, the proposed method has also a heavy time cost because of huge computing burden. To improve the efficiency of method, the solving algorithm has been optimized and improved.
4. Once the optimal solution of the Gibbs energy U(x) is obtained by RJMCMC coupled with simulated annealing, the building objects of ALS data are detected. However, a lot of objects with the similar shapes of cuboid or with low heights might be detected as buildings as well. On the other hand, one building may have attachments or different levels, resulting in incomplete detection. The above cases lead to bad qualities of building footprints extraction. Hence, a refinement operator is performed to extract building roof outlines from the extracted building objects by filtering false buildings and merging the connecting parts of buildings. Finally, the modified convex hull method is applied to extract the outlines of detected building objects,.
5. The proposed model was tested with the ALS dataset of Vaihingen, Germany and Toronto (5datasets in total), in the context of the 'ISPRS Test Project on Urban Classification and3D Building Reconstruction'. This paper analysis and discuss the parameters which were used in the proposed algorithm. And the comprehensive evaluation and comparison on the detected outlines of buildings were performed by ISPRS Workgroup III/4based on the completeness, the correctness, and the quality of the results both on a per-object and on a per-area level. On the other hand, F1measure is used to compare the results of building extraction from the proposed method and other methods. The experimental results show the proposed building extraction method can get good extraction performance in terms of pixel based and object based completeness, correctness, and RMSEs. It also shows that the proposed method provides a functional and effective solution for directly extracting building objects from ALS data of a variety of scenes without resampling or gridding the input data.
引文
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