A progressive morphological filter for removing nonground measurements from airborne LIDAR data

TLDR

Airborne LIDAR provides rapid, inexpensive high‑resolution topography for digital terrain models used in flood modeling and landslide prediction, but its point clouds contain nonground features that must be classified and removed to generate accurate DTMs. This study develops a progressive morphological filter to detect and remove nonground LIDAR measurements. The filter incrementally enlarges its window and applies elevation‑difference thresholds to excise vehicles, vegetation, and buildings while preserving ground, and is evaluated on mountainous and flat urban datasets. Results demonstrate that the filter effectively removes most nonground points.

Abstract

Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.