A Marked Point Process for Automated Tree Detection From Mobile Laser Scanning Point Cloud Data

2012年遥感计算机视觉国际会议——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.


关键词: 计算机 遥感 视觉 国际 2012年遥感计算机视觉国际会议

主讲人:Yongtao Yu 机构:Xiamen University

时长:0:11:32 年代:2012年