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
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