基于机载LiDAR数据林木识别与重建
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摘要
机载激光雷达(LiDAR)是一种新型测量系统,正逐步广泛应用到林业资源调查研究中,并日益成为林木模型数据获取与分析处理的一种重要工具。然而现有方法大多数是针对大面积林分识别和参数反演的,不能满足“精准林业”的更精确、更自动化、更细致的要求,研究在高郁闭度等复杂环境中单株木识别是现代林业调查的必然趋势。同时,由于目前单株木自动化建模算法的缺失,大大限制了机载LiDAR数据在数字林业发展中的应用。因此,研究发展自动、高效的单株木点云识别和建模方法具有重要的研究价值。
本文以全波形LiDAR数据为基础,以林木点云分割和三维建模为目标,以获取定位精确、结构完整的单株木点云及模拟生成三维林木模型为研究重点,以“全波形LiDAR数据处理—林木识别—单株木点云分割—三维林木重建”为研究主线,针对其中一些关键技术主要进行以下几个方面研究。
1.建立一套LiDAR数据林木识别和单株木模型重建的技术框架。首先,利用全波形LiDAR数据获取高质量的点云；其次利用图像处理方法在树冠高程模型的基础上进行林木初步判别；然后,利用各向异性马尔可夫模型和Bayesian理论实现单株木三维点云分割；在此基础上,提取主要树干骨架,并借助先验知识模拟生成较完整的枝条架；最后,对骨架和树叶进行格网化处理和纹理映射实现林木模型重建与三维场景绘制。
2.提出了一种全波形LiDAR数据分解并获取高质量点云的方法。传统的分解方法依靠设备厂商提供的简单阈值,无法获得高精度的分解结果。本文通过广义高斯函数对波形进行分解,利用Levenberg-Marquardt优化算法进行脉冲波形参数估算,获取高密度的点云三维坐标同时提取出相应的脉冲强度、宽度以及背向散射截面等重要波形信息,为后续点云分类提供数据支持。
3.提出一种林木点云判别与树冠边缘提取方法。首先,用线性预测算滤波方法分离出非地表点云；在此基础上,根据地物表面点云分布情况及其波形参数特征分类出林木点云；然后,将林木点云生成树冠高程模型CHM,并利用标记控制分水岭算法进行树顶位置判断及树冠边缘提取,为后续单株木点云精确分割提供先验信息。
4.提出一种三维单株木点云分割方法。传统方法是在二维图像上利用树冠确定单株木,这在稀疏林地效果较好,但在高郁闭度森林环境中,单株木往往挨在一起、树冠连成一片,造成森林参数反演的准确度不高。本文提出基于各向异性马尔可夫模型的三维单株木点云分割方法,利用Bayesian理论将点云分割问题转化为求解后验能量组合优化问题,根据单株木点云分布和外形特征,设计出各向异性的先验模型,并采用图割技术(Graph Cuts)求解能量函数值局部最优,利用近似期望最大值(EM)的迭代算法估算模型的参数。
5.提出一种基于机载LiDAR点云数据的三维林木模型重建方法。传统的单株木建模方法需要有相当的生态学知识,且存在模型参数多、外形难以控制、自动化程度不高等不足。本文结合几何构造法和生物形态法的特点,在LiDAR点云基础上进行单株木模型重建。首先,利用最短路径逼近算法,在由离散点云构成的带权连通图上重建主要树干骨架；根据单株木生长的自相似性特征以及树叶点云位置模拟生成更完整、更细致的的枝条；利用B样条方法拟合出更符合视觉效果的树干曲线,并对其进行圆柱体面格网参数化处理；最后在切空间中利用法线纹理贴图以及光照模型实现单株木纹理映射,重建三维模型。
实验结果表明,本文技术方案具有以下特点：(1)可以有效提高点云密度,增强点云的层次,并可获得林木点云有价值的波形特征；(2)可快速实现林木点云的判别,树冠边缘提取；(3)可有效、快速实现高郁闭度下的单株木点云分割；(4)可生成较逼真的单株木模型,模型的随机性和可控性较好。本文方案可为林业资源调查和森林经营管理等方面提供新思路、新技术,以期为现代林业可持续发展做出新贡献。
Airborne LiDAR is a new type of measuring system, which is being widely applied in the forest resources research and increasingly being regarded as an important means in obtaining and analyzing the model forest data. However, most of the existing methods target at the forest stand identification and parametric inversion. In order to satisfy the demand of more precise, automatic and meticulous, it is inevitable in modern forestry research to find out how to identify a single tree in high canopy density and such complex conditions. In the meantime, there is no automatic modeling algorithm of a single tree, which hinders wide spread of Airborne LiDAR in digital forestry. As a result, it is meaningful to devising an automatic and efficient system of identifying a single tree and modeling.
Based on full waveform LiDAR data, with forest point cloud segmentation and three-dimensional modeling as its aim, obtaining precise and integrated forest point and cloud data as its focus, and the "full waveform LiDAR data processing-tree identification-forest point cloud segmentation-three-dimensional reconstruction of trees" as its main line, this paper conducts a research on the key technology in the following aspects:
1. Establishing a set of technical framework based on LiDAR data to identify trees and reconstruct the tree model. First of all, making use of full waveform LiDAR data to obtain high-density point clouds; secondly, utilizing the image processing based on the crown elevation model to conduct a preliminary identification; thirdly, based on Markov Random Field and the Bayesian Theory to realize three-dimensional point cloud segmentation; then on this basis, to extract the trunk skeleton, and with a priori knowledge to generate a complete single tree skeleton; lastly, through the grid processing and texture mapping of the skeleton and leaves to reconstruct the tree model and draw the three-dimensional scenes.
2. Proposing a means of full-waveform LiDAR data decomposition and obtaining high quality point cloud. The tradition method refers to the simple threshold provided by Equipment Manufacturers so that they can not obtain highly accurate data. This paper manages to solve such a problem. Firstly, based on the Generalized Gaussian function to decompose the radar waveform data; then, using Levenberg-Marquardt algorithm for parameter optimization in order to obtain a three-dimensional coordinate of high-density point clouds, at the same time, collect the amplitude of the point cloud, height and backscatter of cross-section and other important information which provide data support for the follow-up point cloud classification.
3. Presenting a method of identifying point cloud and Extracting the crown edge of the tree. Firstly, extracting non-surface point cloud by linear foreseeing filter method, on this basis, classifying different forest point clouds according to point cloud distribution and waveform parameters; then, turning the forest point cloud into the CHM forest canopy elevation models, and marker-controlled watershed segmenation to locate the treetop and extract the crown, which provide a priori information for plant trees for subsequent precise segmentation.
4. Proposing a three-dimensional point cloud segmentation algorithm. The traditional way locates s a single tree by using the canopy in the two-dimensional image, which works well in sparse woodland. However, in the high-density forest, plant trees often crowd together, which leads to low accuracy of traditional methods. In this paper, a new solution is proposed. Based on the anisotropic Markov model and three-dimensional point cloud segmentation method, by using Bayesian estimation theory to convert the problem of point cloud segmentation into the combinatorial optimization of posterior energy function; with Markov random fields, designing a priori anisotropy model based on the trees distribution and shape characteristics, and adopting Graph Cuts to optimize the combination of energy functions, then utilizing the approximate EM algorithm to iteratively calculate the model parameters.
5. Proposing a method of reconstructing a three-dimensional tree model based on airborne LiDAR point cloud data. The traditional modeling approach requires considerable ecological knowledge, in addition, such approach is influenced by various parameters and it is difficult to control the model shape. This paper, combined geometric structure and biological morphology and on the basis of LiDAR point cloud, aims to reconstruct the tree model. First of all, utilizing the shortest path approximation algorithm to reestablish the in the connected graph with weight formed by scattered forest point cloud; based on trees' self-similarity and information of leaves point cloud, combined with geometry and biological method, reconstructing a more complete and detailed skeleton of the trunk; using the cubic B-spline to build a more suitable trunk curve, and conduct the cylinder surface grid parameterization; lastly, in tangent space making use of the normal texture mapping and illumination model to obtain a single tree's texture map so as to reconstruct a three-dimensional model.
The experimental results show that the technical program has the following characteristics:(1) effectively improve the point cloud density, enhance the level of point cloud and obtain valuable spectrum eigenvalue of forest point cloud;(2) rapidly realize the monitoring of forest point cloud and locate a single tree;(3) effectively and efficiently achieve forest point cloud segmentation in high canopy density;(4) generate a relatively realistic tree model, with good randomness and controllability.
In a word, this paper sheds new light on forest resource research and forest management, which makes a great contribution to the sustainable development of modern forestry.
本文以全波形LiDAR数据为基础,以林木点云分割和三维建模为目标,以获取定位精确、结构完整的单株木点云及模拟生成三维林木模型为研究重点,以“全波形LiDAR数据处理—林木识别—单株木点云分割—三维林木重建”为研究主线,针对其中一些关键技术主要进行以下几个方面研究。
1.建立一套LiDAR数据林木识别和单株木模型重建的技术框架。首先,利用全波形LiDAR数据获取高质量的点云；其次利用图像处理方法在树冠高程模型的基础上进行林木初步判别；然后,利用各向异性马尔可夫模型和Bayesian理论实现单株木三维点云分割；在此基础上,提取主要树干骨架,并借助先验知识模拟生成较完整的枝条架；最后,对骨架和树叶进行格网化处理和纹理映射实现林木模型重建与三维场景绘制。
2.提出了一种全波形LiDAR数据分解并获取高质量点云的方法。传统的分解方法依靠设备厂商提供的简单阈值,无法获得高精度的分解结果。本文通过广义高斯函数对波形进行分解,利用Levenberg-Marquardt优化算法进行脉冲波形参数估算,获取高密度的点云三维坐标同时提取出相应的脉冲强度、宽度以及背向散射截面等重要波形信息,为后续点云分类提供数据支持。
3.提出一种林木点云判别与树冠边缘提取方法。首先,用线性预测算滤波方法分离出非地表点云；在此基础上,根据地物表面点云分布情况及其波形参数特征分类出林木点云；然后,将林木点云生成树冠高程模型CHM,并利用标记控制分水岭算法进行树顶位置判断及树冠边缘提取,为后续单株木点云精确分割提供先验信息。
4.提出一种三维单株木点云分割方法。传统方法是在二维图像上利用树冠确定单株木,这在稀疏林地效果较好,但在高郁闭度森林环境中,单株木往往挨在一起、树冠连成一片,造成森林参数反演的准确度不高。本文提出基于各向异性马尔可夫模型的三维单株木点云分割方法,利用Bayesian理论将点云分割问题转化为求解后验能量组合优化问题,根据单株木点云分布和外形特征,设计出各向异性的先验模型,并采用图割技术(Graph Cuts)求解能量函数值局部最优,利用近似期望最大值(EM)的迭代算法估算模型的参数。
5.提出一种基于机载LiDAR点云数据的三维林木模型重建方法。传统的单株木建模方法需要有相当的生态学知识,且存在模型参数多、外形难以控制、自动化程度不高等不足。本文结合几何构造法和生物形态法的特点,在LiDAR点云基础上进行单株木模型重建。首先,利用最短路径逼近算法,在由离散点云构成的带权连通图上重建主要树干骨架；根据单株木生长的自相似性特征以及树叶点云位置模拟生成更完整、更细致的的枝条；利用B样条方法拟合出更符合视觉效果的树干曲线,并对其进行圆柱体面格网参数化处理；最后在切空间中利用法线纹理贴图以及光照模型实现单株木纹理映射,重建三维模型。
实验结果表明,本文技术方案具有以下特点：(1)可以有效提高点云密度,增强点云的层次,并可获得林木点云有价值的波形特征；(2)可快速实现林木点云的判别,树冠边缘提取；(3)可有效、快速实现高郁闭度下的单株木点云分割；(4)可生成较逼真的单株木模型,模型的随机性和可控性较好。本文方案可为林业资源调查和森林经营管理等方面提供新思路、新技术,以期为现代林业可持续发展做出新贡献。
Airborne LiDAR is a new type of measuring system, which is being widely applied in the forest resources research and increasingly being regarded as an important means in obtaining and analyzing the model forest data. However, most of the existing methods target at the forest stand identification and parametric inversion. In order to satisfy the demand of more precise, automatic and meticulous, it is inevitable in modern forestry research to find out how to identify a single tree in high canopy density and such complex conditions. In the meantime, there is no automatic modeling algorithm of a single tree, which hinders wide spread of Airborne LiDAR in digital forestry. As a result, it is meaningful to devising an automatic and efficient system of identifying a single tree and modeling.
Based on full waveform LiDAR data, with forest point cloud segmentation and three-dimensional modeling as its aim, obtaining precise and integrated forest point and cloud data as its focus, and the "full waveform LiDAR data processing-tree identification-forest point cloud segmentation-three-dimensional reconstruction of trees" as its main line, this paper conducts a research on the key technology in the following aspects:
1. Establishing a set of technical framework based on LiDAR data to identify trees and reconstruct the tree model. First of all, making use of full waveform LiDAR data to obtain high-density point clouds; secondly, utilizing the image processing based on the crown elevation model to conduct a preliminary identification; thirdly, based on Markov Random Field and the Bayesian Theory to realize three-dimensional point cloud segmentation; then on this basis, to extract the trunk skeleton, and with a priori knowledge to generate a complete single tree skeleton; lastly, through the grid processing and texture mapping of the skeleton and leaves to reconstruct the tree model and draw the three-dimensional scenes.
2. Proposing a means of full-waveform LiDAR data decomposition and obtaining high quality point cloud. The tradition method refers to the simple threshold provided by Equipment Manufacturers so that they can not obtain highly accurate data. This paper manages to solve such a problem. Firstly, based on the Generalized Gaussian function to decompose the radar waveform data; then, using Levenberg-Marquardt algorithm for parameter optimization in order to obtain a three-dimensional coordinate of high-density point clouds, at the same time, collect the amplitude of the point cloud, height and backscatter of cross-section and other important information which provide data support for the follow-up point cloud classification.
3. Presenting a method of identifying point cloud and Extracting the crown edge of the tree. Firstly, extracting non-surface point cloud by linear foreseeing filter method, on this basis, classifying different forest point clouds according to point cloud distribution and waveform parameters; then, turning the forest point cloud into the CHM forest canopy elevation models, and marker-controlled watershed segmenation to locate the treetop and extract the crown, which provide a priori information for plant trees for subsequent precise segmentation.
4. Proposing a three-dimensional point cloud segmentation algorithm. The traditional way locates s a single tree by using the canopy in the two-dimensional image, which works well in sparse woodland. However, in the high-density forest, plant trees often crowd together, which leads to low accuracy of traditional methods. In this paper, a new solution is proposed. Based on the anisotropic Markov model and three-dimensional point cloud segmentation method, by using Bayesian estimation theory to convert the problem of point cloud segmentation into the combinatorial optimization of posterior energy function; with Markov random fields, designing a priori anisotropy model based on the trees distribution and shape characteristics, and adopting Graph Cuts to optimize the combination of energy functions, then utilizing the approximate EM algorithm to iteratively calculate the model parameters.
5. Proposing a method of reconstructing a three-dimensional tree model based on airborne LiDAR point cloud data. The traditional modeling approach requires considerable ecological knowledge, in addition, such approach is influenced by various parameters and it is difficult to control the model shape. This paper, combined geometric structure and biological morphology and on the basis of LiDAR point cloud, aims to reconstruct the tree model. First of all, utilizing the shortest path approximation algorithm to reestablish the in the connected graph with weight formed by scattered forest point cloud; based on trees' self-similarity and information of leaves point cloud, combined with geometry and biological method, reconstructing a more complete and detailed skeleton of the trunk; using the cubic B-spline to build a more suitable trunk curve, and conduct the cylinder surface grid parameterization; lastly, in tangent space making use of the normal texture mapping and illumination model to obtain a single tree's texture map so as to reconstruct a three-dimensional model.
The experimental results show that the technical program has the following characteristics:(1) effectively improve the point cloud density, enhance the level of point cloud and obtain valuable spectrum eigenvalue of forest point cloud;(2) rapidly realize the monitoring of forest point cloud and locate a single tree;(3) effectively and efficiently achieve forest point cloud segmentation in high canopy density;(4) generate a relatively realistic tree model, with good randomness and controllability.
In a word, this paper sheds new light on forest resource research and forest management, which makes a great contribution to the sustainable development of modern forestry.
引文
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