Filtering LiDAR Data Using Binary Quadric Trend Surface and Morphological Approaches

Abstract

Automatically identifying and removing above-ground laser points from terrain surface is proved to be a challenging task for complicated and discontinuous scenarios. Eight methods have been developed and contrasted with each other for filtering LiDAR (Light Detection and Ranging) data. Only one approach is difficult to acquire high precisions for various landscapes. This paper presents a method filtering point clouds in which firstly a binary quadric trend surface is used to remove most non-terrain points by a defined height threshold and subsequently a progressive morphological filter further is employed to detect ground measurements. The experimental results demonstrate that this method yields less type I and total errors compared with other eight approaches based on ISPRS sample data sets.