LIDAR数据滤波和影像辅助提取建筑物
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摘要
机载激光雷达数据滤波和建筑物激光脚点提取是LIDAR数据应用的一项关键数据预处理技术,对其进行系统而深入的研究具有比较明显的实用价值和应用背景,因此已成为当前摄影测量与遥感领域的前沿性研究课题。本文在对机载激光雷达数据获取原理和特点深入分析的基础上,进一步深入的研究了点云数据滤波理论与方法,引入支持向量机作为激光点分类的方法,系统开展了基于虚拟格网体元的多尺度数学形态学滤波方法和基于不平衡支持向量机的建筑物激光脚点提取算法,并通过实验验证了相关算法的有效性和可靠性。本文的主要研究工作体现在以下几个方面:
1.系统梳理和阐述了机载激光雷达数据的定位原理及其系统参数的定义和相关性,为实际工程应用和后续数据处理提供理论依据。
2.提出了基于虚拟格网体元的数据组织方式。通过对不同格网数据的特点采用不同的重采样方式,提高了重采样效率的同时,避免了在数据空白区域人为的生成虚假数据,并且为后续的滤波算法提供数据组织方式。
3.在虚拟格网体元的数据组织基础上,改进了多尺度数学形态学滤波方法的自适应性。利用坡度值较小的地形坡度参数和固定的滤波参数阈值,通过误分类地面点搜索的质量控制方法,降低滤波结果中的第Ⅰ类误差,解决了多尺度数学形态学滤波方法的参数自适应性问题。
4.提出了直接利用支持向量机以单个激光点作为分类对象的建筑激光脚点提取策略。这种方法的基本思路是不以激光点的分割结果为分类对象,避免分割结果的误差传递以及分割块的误分类代价大等问题,并且融合影像的光谱信息以及DSM和nDSM的高程特征作为支持向量机分类的特征向量。理论分析和实验表明,这种算法提取的建筑物激光脚点精度比较高,并且比单纯基于点云数据的支持向量机分类算法具有一定的优势,算法的设计能够满足融合不同数据源的分类要求。
5.引入基于不平衡支持向量机的建筑物激光脚点提取算法。考虑到城区或建筑物密集地区的建筑物激光脚点数量显著大于植被点的数量,标准的支持向量机在面对这种不平衡数据集分类算法上存在一定问题,因此通过设置不同的惩罚因子,达到调节数据平衡性的问题。实验表明该算法正确可靠、精度高。相对于标准的支持向量机分类算法,本算法在平衡数据集和不平衡数据集中的分类精度都比较高,从而降低了支持向量机对数据集平衡性的要求,提高了算法的实用性。
The filtering and building detection of airborne lase scanning data is one of the critical data pre-process technologies, so systemic and deep research of the technique has practical value and application background, and it has been a hot topic in current photogrammetry and remote sensing domain. On the base of analyzing the acquiring theory of airborne lase scanning data and characteristics of airborne lase scanning system, the filtering theory for point cloud data is researched and the support vector machine theory is introduced as the classification method of laser points. Then the approach of mathematic morphological filtering based on virtual grid volumetric pixel and detection of building laser footprints based on unbalanced support vector machine are researched, and the experiments have proved these algorithms to be effective and reliable. The mian work of this dissertation are as follows:
1. The location theory of LIDAR data and definition and correlation of system parameters are expatiated, which provide theory evidence for practical project application and data post-processing.
2. LIDAR data structure method based on virtual grid volumetric pixel is preposed. Different resampling methods for each grid are used according to the characteristics of grid data. So it improves resampling efficiency, avoids to manually creating false data in data blank area, and provides data structure method for back filtering algorithm.
3. On the base of virtual grid volumetric pixel for data structure method, an improved multi-scale mathematic morphological filtering method is put forward for adaptive of the method. A small value of terrain grade parameter and fixed filtering parameters are used, and then the quality control method of searching for misclassified ground points can reduce the type I error in filtering results, which resolves the adaptive problem of parameters for multi-scale mathematic morphological filtering method.
4.An extraction strategy for building laser footprint is bringed out based on classifying single laser point with support vector machine method. The main idea of the approach is that treate single laser point as classification objects rather than the segementation of laser points, so it can avoid the problems of error transmit of segementation and higher cost of misclassification for segementation of laser points. It also takes the spectrum information and height features of DSM and nDSM as the feature vector. Theory analysis and experiments show that the algorithm has high classification accuracy of building detection, and higher priority than the support vector machine classification method based on only point cloud data. The design of the approach also can satisfy the classification requirements for fusion of different data sources.
5. Detection method for building laser footprints based on unbalanced support vector machine is put forward. Considering that in city area or thick building area, the number of building laser footprints is much larger than tree points, so the standard support vector machine classification method has some problems in such environments. The solution is setting different penalty factors for adjusting unbalanced data. Experiments show the approach to be accurate, credible and precise. Compared with standard support vector machine classification method, it has higher classification precision on both balanced data and unbalanced data, so it can reduce the requirement of support vector machine for data balance and improves the practicability of the approach.
1.系统梳理和阐述了机载激光雷达数据的定位原理及其系统参数的定义和相关性,为实际工程应用和后续数据处理提供理论依据。
2.提出了基于虚拟格网体元的数据组织方式。通过对不同格网数据的特点采用不同的重采样方式,提高了重采样效率的同时,避免了在数据空白区域人为的生成虚假数据,并且为后续的滤波算法提供数据组织方式。
3.在虚拟格网体元的数据组织基础上,改进了多尺度数学形态学滤波方法的自适应性。利用坡度值较小的地形坡度参数和固定的滤波参数阈值,通过误分类地面点搜索的质量控制方法,降低滤波结果中的第Ⅰ类误差,解决了多尺度数学形态学滤波方法的参数自适应性问题。
4.提出了直接利用支持向量机以单个激光点作为分类对象的建筑激光脚点提取策略。这种方法的基本思路是不以激光点的分割结果为分类对象,避免分割结果的误差传递以及分割块的误分类代价大等问题,并且融合影像的光谱信息以及DSM和nDSM的高程特征作为支持向量机分类的特征向量。理论分析和实验表明,这种算法提取的建筑物激光脚点精度比较高,并且比单纯基于点云数据的支持向量机分类算法具有一定的优势,算法的设计能够满足融合不同数据源的分类要求。
5.引入基于不平衡支持向量机的建筑物激光脚点提取算法。考虑到城区或建筑物密集地区的建筑物激光脚点数量显著大于植被点的数量,标准的支持向量机在面对这种不平衡数据集分类算法上存在一定问题,因此通过设置不同的惩罚因子,达到调节数据平衡性的问题。实验表明该算法正确可靠、精度高。相对于标准的支持向量机分类算法,本算法在平衡数据集和不平衡数据集中的分类精度都比较高,从而降低了支持向量机对数据集平衡性的要求,提高了算法的实用性。
The filtering and building detection of airborne lase scanning data is one of the critical data pre-process technologies, so systemic and deep research of the technique has practical value and application background, and it has been a hot topic in current photogrammetry and remote sensing domain. On the base of analyzing the acquiring theory of airborne lase scanning data and characteristics of airborne lase scanning system, the filtering theory for point cloud data is researched and the support vector machine theory is introduced as the classification method of laser points. Then the approach of mathematic morphological filtering based on virtual grid volumetric pixel and detection of building laser footprints based on unbalanced support vector machine are researched, and the experiments have proved these algorithms to be effective and reliable. The mian work of this dissertation are as follows:
1. The location theory of LIDAR data and definition and correlation of system parameters are expatiated, which provide theory evidence for practical project application and data post-processing.
2. LIDAR data structure method based on virtual grid volumetric pixel is preposed. Different resampling methods for each grid are used according to the characteristics of grid data. So it improves resampling efficiency, avoids to manually creating false data in data blank area, and provides data structure method for back filtering algorithm.
3. On the base of virtual grid volumetric pixel for data structure method, an improved multi-scale mathematic morphological filtering method is put forward for adaptive of the method. A small value of terrain grade parameter and fixed filtering parameters are used, and then the quality control method of searching for misclassified ground points can reduce the type I error in filtering results, which resolves the adaptive problem of parameters for multi-scale mathematic morphological filtering method.
4.An extraction strategy for building laser footprint is bringed out based on classifying single laser point with support vector machine method. The main idea of the approach is that treate single laser point as classification objects rather than the segementation of laser points, so it can avoid the problems of error transmit of segementation and higher cost of misclassification for segementation of laser points. It also takes the spectrum information and height features of DSM and nDSM as the feature vector. Theory analysis and experiments show that the algorithm has high classification accuracy of building detection, and higher priority than the support vector machine classification method based on only point cloud data. The design of the approach also can satisfy the classification requirements for fusion of different data sources.
5. Detection method for building laser footprints based on unbalanced support vector machine is put forward. Considering that in city area or thick building area, the number of building laser footprints is much larger than tree points, so the standard support vector machine classification method has some problems in such environments. The solution is setting different penalty factors for adjusting unbalanced data. Experiments show the approach to be accurate, credible and precise. Compared with standard support vector machine classification method, it has higher classification precision on both balanced data and unbalanced data, so it can reduce the requirement of support vector machine for data balance and improves the practicability of the approach.
引文
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