Abstract
Automatic extraction of ground points, called filtering, is an essential step in producing Digital Terrain Models from airborne LiDAR data. Scene complexity and computational performance are two major problems that should be addressed in filtering, especially when processing large point cloud data with diverse scenes. This paper proposes a fast and intelligent algorithm called Semi-Global Filtering (SGF). The SGF models the filtering as a labeling problem in which the labels correspond to possible height levels. A novel energy function balanced by adaptive ground saliency is employed to adapt to steep slopes, discontinuous terrains, and complex objects. Semi-global optimization is used to determine labels that minimize the energy. These labels form an optimal classification surface based on which the points are classified as either ground or non-ground. The experimental results show that the SGF algorithm is very efficient and able to produce high classification accuracy. Given that the major procedure of semi-global optimization using dynamic programming is conducted independently along eight directions, SGF can also be paralleled and sped up via Graphic Processing Unit computing, which runs at a speed of approximately 3 million points per second.
Highlights
Since the 1990s, airborne Light Detection and Ranging (LiDAR) has been an important technological innovation in remote sensing and mapping science
Algorithm [25,28], we model the filtering task as a labeling problem in which an optimal classification surface close to the bare ground is computed by minimizing a novel energy function
We first apply the Semi-Global Filtering (SGF) algorithm to the benchmark dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) Commission III/WG3
Summary
Since the 1990s, airborne Light Detection and Ranging (LiDAR) has been an important technological innovation in remote sensing and mapping science. LiDAR is capable of directly acquiring high-accuracy from airborne LiDAR point cloud has been an attractive research topic [1]. As both ground and non-ground objects (e.g. buildings, vegetation, and vehicles) reflect laser light, the first step towards generating a DTM from a LiDAR point cloud is classifying the point cloud into ground and non-ground points. This task is referred to as filtering. Filtering is challenging for real-time applications or large-volume data processing
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