LiDAR与影像结合的地物分类及房屋重建研究
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摘要
机载激光扫描通常也被称为LiDAR, LiDAR技术的发展为获取高时空分辨率的地球信息提供了一种全新的手段。LiDAR提高了观测的精度和速度,使数据获取和处理朝着自动化方向发展。自从1990中期后,LiDAR技术已广泛应用于地物目标和表面三维提取与重建。由于LiDAR数据分布离散、不规则以及数据分布不均匀,同时缺少光谱、纹理和形状等物体信息,加之LiDAR数据存在数据缝隙,使得仅从激光扫描数据中进行地物提取与建筑物重建仍然存在很多问题。然而随着硬件技术的提高,一般激光扫描系统都包含高分辨率的数码相机,在获取激光扫描数据的同时可以获取高分辨率航空影像,甚至有些LiDAR系统可以提供近红外影像。由于航空影像可以提供丰富的光谱、纹理信息,可有效弥补LiDAR数据的不足。通过融合不同数据源,使得一个数据源的缺点可以有其他数据源加以弥补,这也是摄影测量和遥感领域的一个发展趋势。本文主要涉及LiDAR与航空影像相结合的地物分类,以及建筑物提取过程中的几个问题。
1)探讨了快速查询离散LiDAR点云数据的几种方法,主要包括二维分块索引、利用KD-树实现在三维空间中激光点的k个最邻近点和区域的快速查询、以及不规则三角网(TIN)。并且研究了在利用三角网加密迭代方法对LiDAR数据提取DTM过程中需要考虑的几个关键问题：主要是初始种子点的选取,激光点云划分的格网尺寸,极低局外点的去除以及高程阈值的选择。
2)根据激光扫描数据按照扫描线排列的特点,设计了基于扫描线的两步法滤波算法。算法首先根据地形连续性的特征,利用激光数据点之间的坡度、高程差以及扫描区域最大地形坡度进行一维地形点特征提取；其次在假设城市局部地形表面是平坦的前提下,将前一步骤获得的地形点作为候选地形点,采用局部参数化表面拟合进一步将候选地形点中非地形点去除。通过实验验证了该方法对城区地形的可行性。
3)分析了航空影像和LiDAR数据分别所提供的光谱信息和几何信息,提出了LiDAR数据和航空影像融合的地物提取算法。算法首先对LiDAR距离影像与航空影像进行基于Brovey比值法的融合、采用金字塔分裂合并算法进行融合影像的分割、边界跟踪获取若干个同质区域作为分类对象；然后提取航空影像所提供的直线特征,以及LiDAR数据提供的几何信息(LiDAR数据的高程信息、激光点的空间离散度、首末次回波)。最后对每个分类对象统计该区域内光谱信息和几何信息,根据实验所确定分类规则和分类条件,对LiDAR数据进行分类。
4)研究了建筑物区域多边形简化、最小二乘模板和直角约束条件提取简单直角建筑物区域的方法,以及建筑物屋顶面片自动分割算法。提出了在利用RANSAC算法检测建筑物屋顶面片的基础上,建立屋顶面片之间的邻域关系。并分别介绍了利用检测出来的屋顶面片重建平顶、人字型以及四坡型三类简单建筑物过程。由于LiDAR数据的特性,从点云中重建的建筑物轮廓并不是真正的建筑物边界,因此最后需利用航空影像对从LiDAR数据中重建的平顶、人字型以及四坡型建筑物进行精确定位。
用多组LiDAR实验数据来对滤波技术进行了大量的实验,探讨了其中的技术问题。也通过多组融合数据进行了分类实验,并分析了各种可能性以及实践中遇到的问题。由于受所研究影像数据分辨率问题的限制,对建筑物的提取只进行三类简单建筑物重建的研究。基于LiDAR数据和航空影像的分类和三维重建是一项复杂的任务,如何提高地物分类精度、模型三维重建的自动化水平和模型精度,仍然需要大量和深入的研究。
LiDAR, Light Detection and Ranging, provides a new technological means for attaining high-resolution and high temporal-spatial geo-information, which makes data acquisition are being changed from the traditional photogrammetric mode to the continuous automatic mode. It enhances the accuracy and speed of survey, and automate acquisition and processing of the data. Since 1990s, LiDAR technology has been widely used in the fields of object extraction and surface feature extraction and 3D reconstruction. There are still many difficulties in object extraction and building reconstruction only from point cloud, because data provided by LiDAR system is discrete and regular distribution, and lacks spectrum, texture and shape information. in addition, the data gap also exists in LiDAR data. However, with the improvement of hardware techniques, LiDAR system generally includes high-resolution digital camera, from which the high-resolution aerial images, even infrared images, can be attained when accessing the point cloud. The aerial image provides aboundant spectrum and texture information that can compensate the disadvantages of the data of LiDAR. It is a tendency to the field of Photogrametry and Remote Sensing that the shortcomings of one data source can be made up other data sources through the integration of different data sources. This paper studies a few of problems and key techniques in the object classification from integrating LiDAR and aerial images, as well as in the process of building extraction from the following aspects:
The several techniques of quick inquiring the discrete point cloud have been introduced respectively, which include the two-dimensional grid index, the quick query of K-nearest and region neighbors in the three-dimensional by using KD-tree structure, and Triangular Irregular Network(TIN). Moreover, several key techniques in using the filtering algorithm based on Progressive TIN densification, which consists of selection of seed points, division of grid cell size removal of outliers, and selection of height threshold.
The two-stage filtering method based on scan line has been presented according to the characteristic of LiDAR data aligning scan line. At the first stage, based on the feature of continuous terrain, terrain points can be extracted in one-dimension by using the slope height difference of two neighbors and the maximum terrain slope of scan region. At the second stage, the terrain points extracted from the first stage refer as the candidate points, from which the non-terrain points will be excluded utilizing the local parameter surface fitting in the assumption that urban local terrain surface is flat. Experiments show that the method is the feasibility of urban terrain.
The object extraction of fusing LiDAR data and aerial images has been proposed by analysis of the spectral information and geometric information provided by the aerial image and LiDAR data respectively. Firstly, the homogeneous regions of image can be attained by the pyramid spliting-merging segment algorithm and boundary tracking which can be treated as the object of classification. Then, line features extracted from the aerial images, geometric information, such as height information, discrete measurement, and the height difference between the first echo and the last echo, provided by the LiDAR data, which all refers as the clues for classification. At the last, in the each homogeneous region, the spectrum information and geometric information can be comprehensively used for the classification of LiDAR data in accordance with the experiments to determine the conditions and rules of classification.
An algorithm for simplification of building region polygons, the Least Square Template matching and constraint of right angle for extraction of simple righ angle building and the automatic segmentation for the roof of building has been prensented. On the basis of using RANSAC algorithm to detect the surfaces of roof, the ridge points of gable building can be reconstructed through building the neighbor correlation of surfaces of the roof, from which the ridge lines of roof can be attain. Due to disadvantages of LiDAR data, the boundaries of building are not real meaning of them. Finally, the building models reconstructed from LiDAR data need further to locate accurately by using the advantages of aerial image.
In this thesis, many filtering experiments had been conducted for testifing the two filtering algorithms, and their results show that the proposed algorithms are completely feasible. In addition, a few of key techniques had been discussed. Because this study is on the basis of already registrated between LiDAR data and image data, research on registration does not introduced. Through lots of classification experiments, problems and possibilities in practice are discussed. Due to limitation of resolution of images, only three simple building types had been reconstruction. Object classification and model reconstruction, based on integrating LiDAR and Image, are a complicated task. How to improve the accuracy of classification and automation of building reconstruction still need further study.
1)探讨了快速查询离散LiDAR点云数据的几种方法,主要包括二维分块索引、利用KD-树实现在三维空间中激光点的k个最邻近点和区域的快速查询、以及不规则三角网(TIN)。并且研究了在利用三角网加密迭代方法对LiDAR数据提取DTM过程中需要考虑的几个关键问题：主要是初始种子点的选取,激光点云划分的格网尺寸,极低局外点的去除以及高程阈值的选择。
2)根据激光扫描数据按照扫描线排列的特点,设计了基于扫描线的两步法滤波算法。算法首先根据地形连续性的特征,利用激光数据点之间的坡度、高程差以及扫描区域最大地形坡度进行一维地形点特征提取；其次在假设城市局部地形表面是平坦的前提下,将前一步骤获得的地形点作为候选地形点,采用局部参数化表面拟合进一步将候选地形点中非地形点去除。通过实验验证了该方法对城区地形的可行性。
3)分析了航空影像和LiDAR数据分别所提供的光谱信息和几何信息,提出了LiDAR数据和航空影像融合的地物提取算法。算法首先对LiDAR距离影像与航空影像进行基于Brovey比值法的融合、采用金字塔分裂合并算法进行融合影像的分割、边界跟踪获取若干个同质区域作为分类对象；然后提取航空影像所提供的直线特征,以及LiDAR数据提供的几何信息(LiDAR数据的高程信息、激光点的空间离散度、首末次回波)。最后对每个分类对象统计该区域内光谱信息和几何信息,根据实验所确定分类规则和分类条件,对LiDAR数据进行分类。
4)研究了建筑物区域多边形简化、最小二乘模板和直角约束条件提取简单直角建筑物区域的方法,以及建筑物屋顶面片自动分割算法。提出了在利用RANSAC算法检测建筑物屋顶面片的基础上,建立屋顶面片之间的邻域关系。并分别介绍了利用检测出来的屋顶面片重建平顶、人字型以及四坡型三类简单建筑物过程。由于LiDAR数据的特性,从点云中重建的建筑物轮廓并不是真正的建筑物边界,因此最后需利用航空影像对从LiDAR数据中重建的平顶、人字型以及四坡型建筑物进行精确定位。
用多组LiDAR实验数据来对滤波技术进行了大量的实验,探讨了其中的技术问题。也通过多组融合数据进行了分类实验,并分析了各种可能性以及实践中遇到的问题。由于受所研究影像数据分辨率问题的限制,对建筑物的提取只进行三类简单建筑物重建的研究。基于LiDAR数据和航空影像的分类和三维重建是一项复杂的任务,如何提高地物分类精度、模型三维重建的自动化水平和模型精度,仍然需要大量和深入的研究。
LiDAR, Light Detection and Ranging, provides a new technological means for attaining high-resolution and high temporal-spatial geo-information, which makes data acquisition are being changed from the traditional photogrammetric mode to the continuous automatic mode. It enhances the accuracy and speed of survey, and automate acquisition and processing of the data. Since 1990s, LiDAR technology has been widely used in the fields of object extraction and surface feature extraction and 3D reconstruction. There are still many difficulties in object extraction and building reconstruction only from point cloud, because data provided by LiDAR system is discrete and regular distribution, and lacks spectrum, texture and shape information. in addition, the data gap also exists in LiDAR data. However, with the improvement of hardware techniques, LiDAR system generally includes high-resolution digital camera, from which the high-resolution aerial images, even infrared images, can be attained when accessing the point cloud. The aerial image provides aboundant spectrum and texture information that can compensate the disadvantages of the data of LiDAR. It is a tendency to the field of Photogrametry and Remote Sensing that the shortcomings of one data source can be made up other data sources through the integration of different data sources. This paper studies a few of problems and key techniques in the object classification from integrating LiDAR and aerial images, as well as in the process of building extraction from the following aspects:
The several techniques of quick inquiring the discrete point cloud have been introduced respectively, which include the two-dimensional grid index, the quick query of K-nearest and region neighbors in the three-dimensional by using KD-tree structure, and Triangular Irregular Network(TIN). Moreover, several key techniques in using the filtering algorithm based on Progressive TIN densification, which consists of selection of seed points, division of grid cell size removal of outliers, and selection of height threshold.
The two-stage filtering method based on scan line has been presented according to the characteristic of LiDAR data aligning scan line. At the first stage, based on the feature of continuous terrain, terrain points can be extracted in one-dimension by using the slope height difference of two neighbors and the maximum terrain slope of scan region. At the second stage, the terrain points extracted from the first stage refer as the candidate points, from which the non-terrain points will be excluded utilizing the local parameter surface fitting in the assumption that urban local terrain surface is flat. Experiments show that the method is the feasibility of urban terrain.
The object extraction of fusing LiDAR data and aerial images has been proposed by analysis of the spectral information and geometric information provided by the aerial image and LiDAR data respectively. Firstly, the homogeneous regions of image can be attained by the pyramid spliting-merging segment algorithm and boundary tracking which can be treated as the object of classification. Then, line features extracted from the aerial images, geometric information, such as height information, discrete measurement, and the height difference between the first echo and the last echo, provided by the LiDAR data, which all refers as the clues for classification. At the last, in the each homogeneous region, the spectrum information and geometric information can be comprehensively used for the classification of LiDAR data in accordance with the experiments to determine the conditions and rules of classification.
An algorithm for simplification of building region polygons, the Least Square Template matching and constraint of right angle for extraction of simple righ angle building and the automatic segmentation for the roof of building has been prensented. On the basis of using RANSAC algorithm to detect the surfaces of roof, the ridge points of gable building can be reconstructed through building the neighbor correlation of surfaces of the roof, from which the ridge lines of roof can be attain. Due to disadvantages of LiDAR data, the boundaries of building are not real meaning of them. Finally, the building models reconstructed from LiDAR data need further to locate accurately by using the advantages of aerial image.
In this thesis, many filtering experiments had been conducted for testifing the two filtering algorithms, and their results show that the proposed algorithms are completely feasible. In addition, a few of key techniques had been discussed. Because this study is on the basis of already registrated between LiDAR data and image data, research on registration does not introduced. Through lots of classification experiments, problems and possibilities in practice are discussed. Due to limitation of resolution of images, only three simple building types had been reconstruction. Object classification and model reconstruction, based on integrating LiDAR and Image, are a complicated task. How to improve the accuracy of classification and automation of building reconstruction still need further study.
引文
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