一种改进的线性预测滤波算法

Progressive Linear Prediction Fitting for Extracting DTM from Airborne LiDAR Data

对传统的线性预测算法进行改进,通过粗差剔除、初始点选取、特殊地形分析及光滑度检验等步骤提高了滤波精度。利用ISPRS发布的6组标准测试数据与原方法进行实验比较,结果证明,改进后的算法提高了滤波总精度以及地面点分类精度。

A progressive linear prediction filtering algorithm is proposed for extracting DEM from LiDAR data. Some processes are inserted in the ordinary linear prediction algorithm, such as gross error detection, initial value selection, landform analysis, and smoothness detection. The authors used this algorithm to process 6 datasets published by ISPRS as standard filtering test data. The results show that the improvement in the traditional methods can increase the precision of DEM.