Volume II-3/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W1, 41-46, 2013
https://doi.org/10.5194/isprsannals-II-3-W1-41-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W1, 41-46, 2013
https://doi.org/10.5194/isprsannals-II-3-W1-41-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  16 May 2013

16 May 2013

SINGLE TREE DETECTION FROM AIRBORNE LASER SCANNING DATA USING A MARKED POINT PROCESS BASED METHOD

J. Zhang1, G. Sohn1, and M. Brédif2 J. Zhang et al.
  • 1GeoICT lab, Department of Earth and Space Science & Engineering, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
  • 2Université Paris Est, IGN, MATIS 73, avenue de Paris, 94165 Saint-Mandé, France

Keywords: Single tree detection, marked point process, segmentation

Abstract. Tree detection and reconstruction is of great interest in large-scale city modelling. In this paper, we present a marked point process model to detect single trees from airborne laser scanning (ALS) data. We consider single trees in ALS recovered canopy height model (CHM) as a realization of point process of circles. Unlike traditional marked point process, we sample the model in a constraint configuration space by making use of image process techniques. A Gibbs energy is defined on the model, containing a data term which judge the fitness of the model with respect to the data, and prior term which incorporate the prior knowledge of object layouts. We search the optimal configuration through a steepest gradient descent algorithm. The presented hybrid framework was test on three forest plots and experiments show the effectiveness of the proposed method.