顾及局部统计特征的均值滤波方法及其应用

Mean Filtering Method and Its Application Based on Local Statistical Features

针对Caris软件自动滤波结果中仍存在较多异常数据点、手工剔除需要耗费较长时间的问题,提出了一种基于局部统计特征的均值滤波方法。先利用局部统计特征剔除局部异常点,再结合均值滤波方法估计格网点水深值。实验结果表明,方法处理结果与手工编辑结果水深差值中误差仅为0.121 m,而处理时长极大缩短,对工作效率的提升很大。

Aiming at the problems that there are still many abnormal data points in Caris software automatic filtering results and it takes a long time to remove them manually,this paper proposes a mean filtering method based on local statistical features.Firstly,local statistical features are used to remove local abnormal points,and then combined with mean filtering method to estimate the depth of grid points.The experimental results show that the root mean square error(RMSE)between the processing results and the manual editing results is only 0.121 m,and the processing time greatly decreases,which greatly improves the work efficiency.