偏度特征约束下的机载激光雷达点云数据分类

Classification of Airborne Lidar Point Cloud Data with Skewness Feature Constraint

机载激光雷达获得的点云具有密度低、分布不均匀、分支结构不清晰等特点,其动态扫描过程的数据特征动态偏差很小,无法提取有效的数据去噪特征;为此提出偏度特征约束下的机载激光雷达点云数据实时分类方法;该方法将扫描获取的点云大容量实时数据引入在正态分布中,利用衡量对称性正态分布的关键度量偏度特征作为动态特征分界约束,完成数据滤波;提取机载激光雷达点云特征,从中选取优质特征,以此构建SVM分类器;点云大容量数据训练结果即为最终的分类结果;实验结果表明,所提方法对不同类别的机载激光雷达点云数据分类的准确性与效率较高。

The point cloud obtained by airborne lidar has the characteristics of low density,uneven distribution,unclear branch structure,etc.The dynamic deviation of data features in the dynamic scanning process is very small,and it is unable to extract effective data denoising features.Therefore,a real-time classification method of airborne lidar point cloud data under the constraint of skewness features is proposed.In this method,the large capacity real-time data of point cloud obtained by scanning is introduced into the normal distribution,and the key metric skewness feature measuring the symmetry of the normal distribution is used as the dynamic feature boundary constraint to complete the data filtering;The point cloud features of airborne lidar are extracted,from which high-quality features are selected to build a support vector machine(SVM)classifier.The final classification result is the result of point cloud high-capacity data training.The experimental results show that the proposed method has high accuracy and efficiency in classifying different kinds of airborne lidar point cloud data.