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.

[1]  Josiane Zerubia,et al.  A Marked Point Process of Rectangles and Segments for Automatic Analysis of Digital Elevation Models , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  A. Baddeley,et al.  Stochastic geometry models in high-level vision , 1993 .

[3]  Chris Chatfield,et al.  Statistical Methods for Spatial Data Analysis , 2004 .

[4]  Josiane Zerubia,et al.  A marked point process model for tree crown extraction in plantations , 2005, IEEE International Conference on Image Processing 2005.

[5]  Charles C. Taylor,et al.  Bayesian texture segmentation of weed and crop images using reversible jump Markov chain Monte Carlo methods , 2003 .

[6]  Josiane Zerubia,et al.  Building Outline Extraction from Digital Elevation Models Using Marked Point Processes , 2007, International Journal of Computer Vision.

[7]  A. Large.  Laser Scanning for the Environmental Sciences , 2009 .

[8]  J. Shan,et al.  Topographic laser ranging and scanning : principles and processing , 2008 .

[9]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[10]  Josiane Zerubia,et al.  Marked point process in image analysis , 2002, IEEE Signal Process. Mag..

[11]  Morten LarsenRoyal.  INDIVIDUAL TREE TOP POSITION ESTIMATION BY TEMPLATE VOTING , 1999 .

[12]  Josiane Zerubia,et al.  Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles , 2010, 2010 20th International Conference on Pattern Recognition.

[13]  T. Mattfeldt.  Stochastic Geometry and Its Applications , 1996 .

[14]  J. Besag,et al.  Bayesian Computation and Stochastic Systems , 1995 .