A Marked Point Process for Automated Tree Detection from Mobile Laser Scanning Point Cloud Data
This paper presents a new algorithm for tree detection from airborne / mobile laser scanning or LiDAR point cloud data.The algorithm takes advantage of a marked point process to model the locations of trees and their geometries.The algorithm also uses the Bayesian paradigm to obtain a posterior distribution for the marked point process conditional on the LiDAR point cloud data.A Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm is developed to simulate the posterior distribution.Finally,the maximum a posteriori (MAP) scheme is used to obtain optimal tree detection.This algorithm has been examined by a set of LiDAR point cloud data.The results demonstrate the efficiency of the proposed algorithm for automated detection of trees.
LiDAR tree detection marked point process RJMCMC maximum a posteriori Bayesian inference
Yongtao Yu Jonathan Li Senior Member Haiyan Guan Cheng Wang Ming Cheng
Department of Computer Science, Xiamen University, Xiamen, China 361005 Department of Computer Science, Xiamen University, Xiamen, China 361005;Department of Geography and Department of Geography and Environmental Management, University of Waterloo, Canada N2L 3G1
国际会议
厦门
英文
1-6
2012-12-16（万方平台首次上网日期，不代表论文的发表时间）