TY - JOUR

T1 - Forest point processes for the automatic extraction of networks in raster data

AU - Schmidt, Alena

AU - Lafarge, Florent

AU - Brenner, Claus

AU - Rottensteiner, Franz

AU - Heipke, Christian

N1 - Publisher Copyright: © 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2017/4

Y1 - 2017/4

N2 - In this paper, we propose a new stochastic approach for the automatic detection of network structures in raster data. We represent a network as a set of trees with acyclic planar graphs. We embed this model in the probabilistic framework of spatial point processes and determine the most probable configuration of trees by stochastic sampling. That is, different configurations are constructed randomly by modifying the graph parameters and by adding or removing nodes and edges to/ from the current trees. Each configuration is evaluated based on the probabilities for these changes and an energy function describing the conformity with a predefined model. By using the Reversible jump Markov chain Monte Carlo sampler, an approximation of the global optimum of the energy function is iteratively reached. Although our main target application is the extraction of rivers and tidal channels in digital terrain models, experiments with other types of networks in images show the transferability to further applications. Qualitative and quantitative evaluations demonstrate the competitiveness of our approach with respect to existing algorithms.

AB - In this paper, we propose a new stochastic approach for the automatic detection of network structures in raster data. We represent a network as a set of trees with acyclic planar graphs. We embed this model in the probabilistic framework of spatial point processes and determine the most probable configuration of trees by stochastic sampling. That is, different configurations are constructed randomly by modifying the graph parameters and by adding or removing nodes and edges to/ from the current trees. Each configuration is evaluated based on the probabilities for these changes and an energy function describing the conformity with a predefined model. By using the Reversible jump Markov chain Monte Carlo sampler, an approximation of the global optimum of the energy function is iteratively reached. Although our main target application is the extraction of rivers and tidal channels in digital terrain models, experiments with other types of networks in images show the transferability to further applications. Qualitative and quantitative evaluations demonstrate the competitiveness of our approach with respect to existing algorithms.

KW - Digital terrain models

KW - Graphs

KW - RJMCMC

KW - Spatial point processes

UR - http://www.scopus.com/inward/record.url?scp=85012236989&partnerID=8YFLogxK

U2 - 10.1016/j.isprsjprs.2017.01.012

DO - 10.1016/j.isprsjprs.2017.01.012

M3 - Article

AN - SCOPUS:85012236989

VL - 126

SP - 38

EP - 55

JO - ISPRS Journal of Photogrammetry and Remote Sensing

JF - ISPRS Journal of Photogrammetry and Remote Sensing

SN - 0924-2716

ER -