Journal
REMOTE SENSING
Volume 7, Issue 8, Pages 10996-11015Publisher
MDPI
DOI: 10.3390/rs70810996
Keywords
LiDAR; filtering; classification; digital terrain model; semi-global optimization; GPU
Categories
Funding
- National Key Basic Research and Development Program of China [2012CB719904]
- Guangzhou City funding of science and technology [201508020054]
- Chinese Academicians by Guangdong province
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available