机载激光点云数据处理方法研究  

Research on Data Processing Methods for Airborne Laser Point Clouds

作　　者：王金凤 

机构地区：[1]新疆水利水电勘测设计研究院有限责任公司,新疆 乌鲁木齐

出　　处：《测绘科学技术》2024年第3期208-215,共8页Geomatics Science and Technology

摘　　要：针对机载激光扫描点云数据中杆状物分类精度不理想的问题,本文从杆状物的空间形态特征出发,构建一种基于支持向量机(SVM)模型的杆状物分类算法。首先,根据杆状物空间形态特征确定10个特征值并建立特征矩阵;其次,进行SVM模型训练并建立分类模型;最后,使用训练好的最优SVM模型进行杆状物分类。选取某段城市道路点云数据进行试验,结果表明,本文分类模型无需人工干预与阈值设定,自动化程度高,其中杆状物的最高分类精度能够达到94.23%,验证了该算法的有效性与优越性,可为基于激光点云数据的地物分类提供一定借鉴与参考。In response to the problem of unsatisfactory classification accuracy of rod shaped objects in airborne laser scanning point cloud data, this paper constructs a rod shaped object classification algorithm based on support vector machine (SVM) model, starting from the spatial morphology characteristics of rod shaped objects. Firstly, determine 10 characteristic values and establish a feature matrix based on the spatial morphology characteristics of the rod-shaped object;secondly, conduct SVM model training and establish a classification model;finally, use the trained optimal SVM model for rod object classification. Selecting a certain section of urban road point cloud data for experimentation, the results show that the classification model in this paper does not require manual intervention or threshold setting, and has a high degree of automation. The highest classification accuracy of pole shaped objects can reach 94.23%, verifying the effectiveness and superiority of the algorithm. This can provide certain reference and guidance for land object classification based on laser point cloud data.

关 键 词：机载激光扫描 点云 杆状物 支持向量机 特征值 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]