顾及地形起伏特征的线性预测滤波方法
发布时间：2019-06-17 20:42
【摘要】：传统的线性预测滤波算法将目标点云划分为多个栅格,滤波在每一个栅格内进行,而滤波栅格大小,需要用户手动调整。针对此问题,该文提出一种从机载激光扫描数据中生成数字高程模型(DEM)的有效方法。引入统计学变量——面高程变异系数,刻画地形起伏特征,并建立线性预测滤波算法中栅格大小与面高程变异系数之间的函数关系。最后,利用几组点云数据为研究对象验证该方法的有效性,实验结果表明,该方法能自适应地根据地形的起伏特征调整滤波参数,得到比较理想的地面点数据,最终内插得到高精度的DEM。
[Abstract]:The traditional linear predictive filtering algorithm divides the target point cloud into multiple grids, and the filter is carried out in each grid, and the size of the filter grid needs to be adjusted manually by the user. In order to solve this problem, an effective method for generating digital elevation model (DEM) from airborne laser scanning data is proposed in this paper. The statistical variable, the variation coefficient of surface elevation, is introduced to depict the characteristics of terrain fluctuation, and the functional relationship between the grid size and the coefficient of variation of surface elevation in linear predictive filtering algorithm is established. Finally, several groups of point cloud data are used to verify the effectiveness of the method. The experimental results show that the method can adaptively adjust the filtering parameters according to the undulating characteristics of the terrain, obtain the ideal ground point data, and finally insert the high precision DEM..
【作者单位】： 广东省国土资源测绘院;国家超级计算深圳中心;南开大学周恩来政府管理学院;
【基金】：空间信息智能感知与服务深圳市重点实验室(深圳大学)开放基金资助项目 中央高校基本科研业务费专项资金项目(NKZXB1483)
【分类号】：P208;P237
[Abstract]:The traditional linear predictive filtering algorithm divides the target point cloud into multiple grids, and the filter is carried out in each grid, and the size of the filter grid needs to be adjusted manually by the user. In order to solve this problem, an effective method for generating digital elevation model (DEM) from airborne laser scanning data is proposed in this paper. The statistical variable, the variation coefficient of surface elevation, is introduced to depict the characteristics of terrain fluctuation, and the functional relationship between the grid size and the coefficient of variation of surface elevation in linear predictive filtering algorithm is established. Finally, several groups of point cloud data are used to verify the effectiveness of the method. The experimental results show that the method can adaptively adjust the filtering parameters according to the undulating characteristics of the terrain, obtain the ideal ground point data, and finally insert the high precision DEM..
【作者单位】： 广东省国土资源测绘院;国家超级计算深圳中心;南开大学周恩来政府管理学院;
【基金】：空间信息智能感知与服务深圳市重点实验室(深圳大学)开放基金资助项目 中央高校基本科研业务费专项资金项目(NKZXB1483)
【分类号】：P208;P237
【相似文献】
相关期刊论文 前10条
1 严瑞;龙毅;郑s，
本文编号：2501251
本文链接：https://www.wllwen.com/kejilunwen/dizhicehuilunwen/2501251.html
