Volume XLI-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 701-708, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-701-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 701-708, 2016
https://doi.org/10.5194/isprs-archives-XLI-B3-701-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  10 Jun 2016

10 Jun 2016

NETWORK DETECTION IN RASTER DATA USING MARKED POINT PROCESSES

A. Schmidt1, C. Kruse1, F. Rottensteiner1, U. Soergel2, and C. Heipke1 A. Schmidt et al.
  • 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany
  • 2Institute for Photogrammetry, University of Stuttgart, Germany

Keywords: Marked point processes, RJMCMC, graph, digital terrain models, networks

Abstract. We propose a new approach for the automatic detection of network structures in raster data. The model for the network structure is represented by a graph whose nodes and edges correspond to junction-points and to connecting line segments, respectively; nodes and edges are further described by certain parameters. We embed this model in the probabilistic framework of marked point processes and determine the most probable configuration of objects by stochastic sampling. That is, different graph configurations are constructed randomly by modifying the graph entity parameters, by adding and removing nodes and edges to/ from the current graph configuration. Each configuration is then evaluated based on the probabilities of the changes and an energy function describing the conformity with a predefined model. By using the Reversible Jump Markov Chain Monte Carlo sampler, a global optimum of the energy function is determined. We apply our method to the detection of river and tidal channel networks in digital terrain models. In comparison to our previous work, we introduce constraints concerning the flow direction of water into the energy function. Our goal is to analyse the influence of different parameter settings on the results of network detection in both, synthetic and real data. Our results show the general potential of our method for the detection of river networks in different types of terrain.