Volume XL-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 115-121, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-115-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 115-121, 2015
https://doi.org/10.5194/isprsarchives-XL-3-W3-115-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

  19 Aug 2015

19 Aug 2015

A GRAPH BASED MODEL FOR THE DETECTION OF TIDAL CHANNELS USING MARKED POINT PROCESSES

A. Schmidt1, F. Rottensteiner1, U. Soergel2, and C. Heipke1 A. Schmidt et al.
  • 1Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Germany
  • 2Institute of Geodesy, Chair of Remote Sensing and Image Analysis, Technische Universität Darmstadt, Germany

Keywords: Marked point processes, RJMCMC, graph model, digital terrain models, coast

Abstract. In this paper we propose a new method for the automatic extraction of tidal channels in digital terrain models (DTM) using a sampling approach based on marked point processes. In our model, the tidal channel system is represented by an undirected, acyclic graph. The graph is iteratively generated and fitted to the data using stochastic optimization based on a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler and simulated annealing. The nodes of the graph represent junction points of the channel system and the edges straight line segments with a certain width in between. In each sampling step, the current configuration of nodes and edges is modified. The changes are accepted or rejected depending on the probability density function for the configuration which evaluates the conformity of the current status with a pre-defined model for tidal channels. In this model we favour high DTM gradient magnitudes at the edge borders and penalize a graph configuration consisting of non-connected components, overlapping segments and edges with atypical intersection angles. We present the method of our graph based model and show results for lidar data, which serve of a proof of concept of our approach.