Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 297-304, 2014
https://doi.org/10.5194/isprsarchives-XL-3-297-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
11 Aug 2014
Extraction of fluvial networks in lidar data using marked point processes
A. Schmidt1, F. Rottensteiner1, U. Soergel2, and C. Heipke1 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Hanover, Germany
2Institute of Geodesy, Remote Sensing and Image Analysis, Technische Universität Darmstadt, Darmstadt, Germany
Keywords: Marked point processes, RJMCMC, Lidar, Networks, Coast Abstract. We propose a method for the automatic extraction of fluvial networks in lidar data with the aim to obtain a connected network represented by the fluvial channels' skeleton. For that purpose we develop a two-step approach. First, we fit rectangles to the data using a stochastic optimization based on a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler and simulated annealing. High gradients on the rectangles' border and non-overlapping areas of the objects are introduced as model in the optimization process. In a second step, we determine the principal axes of the rectangles and their intersection points. Based on this a network graph is constructed in which nodes represent junction points or end points, respectively, and edges in-between straight line segments. We evaluate our method on lidar data with a tidal channel network and show some preliminary results.
Conference paper (PDF, 844 KB)


Citation: Schmidt, A., Rottensteiner, F., Soergel, U., and Heipke, C.: Extraction of fluvial networks in lidar data using marked point processes, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 297-304, https://doi.org/10.5194/isprsarchives-XL-3-297-2014, 2014.

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