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

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

       2012年遥感计算器视觉国际会议——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.

Guests :

Yongtao Yu

Year:2012