Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), School of Mathematics and Systems Science, Beihang University, Beijing, China
Xin Jiang
Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), School of Mathematics and Systems Science, Beihang University, Beijing, China
Guanying Huo
Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), School of Mathematics and Systems Science, Beihang University, Beijing, China
Cheng Su
Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), School of Mathematics and Systems Science, Beihang University, Beijing, China
Zehong Lu
Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), School of Mathematics and Systems Science, Beihang University, Beijing, China
Bolun Wang
Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), School of Mathematics and Systems Science, Beihang University, Beijing, China
Zhiming Zheng
Key Laboratory of Mathematics, Informatics and Behavioral Semantics (LMIB), School of Mathematics and Systems Science, Beihang University, Beijing, China
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.
