Name
Abstract | ||
|---|---|---|
Airborne light detection and ranging (LiDAR) technology is becoming the primary method for generating high-resolution digital terrain models (DTMs), which is essential for commercial and scientific uses. In order to generate DTMs, non-ground features as buildings, vehicles, and vegetation must be recognized and distinguished from the LiDAR point cloud. However, various degrees of errors may accumulate in the separated filtering and modeling processes. In this paper, a novel physical process driven DTM generating method for airborne LiDAR measurement is proposed, which combines the point cloud classification and surface fitting process simultaneously. Actually, the physical dynamic process is integrated with the common non-uniform rational b-splines (NURBS) model under the corresponding parameter mediation. The experimental results show that the proposed method is efficacious in reducing errors and have a nice performance in terrain fitting. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2019.2962385 | IEEE ACCESS |
Keywords | Field | DocType |
Digital terrain model, physical process driven fitting, NURBS, LiDAR point cloud | Computer graphics (images),Computer science,Digital elevation model,Distributed computing | Journal |
Volume | ISSN | Citations |
8 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danlei Ye | 1 | 0 | 0.34 |
| Xin Jiang | 2 | 20 | 1.77 |
| Guanying Huo | 3 | 0 | 0.34 |
| Cheng Su | 4 | 0 | 0.34 |
| Zehong Lu | 5 | 0 | 0.34 |
| Bolun Wang | 6 | 2 | 2.73 |
| Zhiming Zheng | 7 | 128 | 16.80 |