Open AccessVoxel-based spatial elongation filtering method for airborne single-photon LiDAR data
Author(s) -
Ting Luo,
Deying Chen,
Zhaodong Chen,
Dong Zeng,
Wentao Wu,
Xing Wang,
Renpeng Yan,
Rongwei Fan
Publication year - 2020
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.382783
Subject(s) - voxel , noise (video) , spatial filter , computer science , lidar , filter (signal processing) , artificial intelligence , signal (programming language) , constant false alarm rate , computer vision , pattern recognition (psychology) , remote sensing , optics , physics , geology , image (mathematics) , programming language
A novel voxel-based spatial elongation filtering method is proposed, to reduce noise in airborne single-photon lidar (SPL) data. In this method, six additional points are generated adjacent to each point of interest in the SPL data. Then, we count the number of points within each voxel and discriminate signals from noise via a predefined threshold. A filter performance evaluation index (taking into account the false alarm and signal loss rates, and the average distance between the residual noise points and their nearest signal points) is introduced. We compare the proposed and voxel-based spatial filtering method. The average false alarm rate found with our method (3.5%) is 18.6% smaller than that of the voxel-based spatial filtering method (4.3%).