乡土景观树冠空隙度点云数据提取再生算法

A Regeneration Algorithm for Extracting Point Cloud Data of Canopy Porosity in Local Landscape

树冠空隙度点提取的过程中,会产生大量噪声点,导致树冠空隙度点云数据细节特征大量丢失。为了保证点云数据的完整度和精确度,提出乡土景观树冠空隙度点云数据提取再生算法。利用三维激光扫描技术,获取乡土景观树冠点云数据,计算树冠空隙度,并参照树冠空隙度分割点云数据,组建树冠空隙度点云数据再生样本。利用改进小波阈值函数和RBF神经网络,消除点云数据噪声、填补点云数据空洞,实现乡土景观树冠空隙度点云数据提取再生。实验结果证明,所提方法提取再生效果好,且所需时间仅为1.002s,表明所提方法的效率高。

In the process of extracting crown porosity points,a large number of noise poin ts will be generated,leading to the loss of the details of crown po rosity point cloud data.In order to ensure the integrity and accuracy of point cloud data,this paper presented an alg orithm for extracting and regenerating the crown porosity point cloud data in vernacular landscape.Firstly,3D laser scanning technology was used to obtain the crown point cloud data of the vernacular landscape and calculate the voidage of the crown,and thus divide the point cloud data according to the crown voidage.After that,a regeneration sample of the crown voidage point cloud data was constructed.Then,the improved wavelet threshold function and RBF neural network were used to eliminate the noise of point cloud data and fill in the hole of point cloud data,thus achieving the extraction and regeneration of point cloud data.Experimental results show that the proposed method has good extrac tion and regeneration effect,and only needs 1.002s,indicating that the method has high efficiency.