Abstract
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.
Highlights
Methods for automatic object detection can be subdivided into top-down and bottom-up approaches
We present a stochastic approach based on marked point processes for the automatic extraction of networks in raster data
We model the network as an undirected, acyclic graph which is iteratively built during the optimization process
Summary
Methods for automatic object detection can be subdivided into top-down and bottom-up approaches. Tournaire and Paparoditis (2009) as well as Li and Li (2010) model objects by rectangles in the context of marked point processes The former extract dashed lines representing road markings from very high resolution aerial images, while the latter detect oil spills in synthetic aperture radar (SAR) data, integrating knowledge about the distribution of the intensity in the backscattered radar signal. Alternative models are based on ellipses and circles, which were used by Perrin et al (2005) to detect contour lines of tree crowns in optical data, whereas Descombes et al (2009) applied such models to detect birds The authors of both papers penalise overlapping objects and prefer a regular arrangement of the objects. The authors report good results for different application such as the extraction of line networks, buildings and tree crowns
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