Details
| Original language | English |
|---|---|
| Pages (from-to) | 38-55 |
| Number of pages | 18 |
| Journal | ISPRS Journal of Photogrammetry and Remote Sensing |
| Volume | 126 |
| Early online date | 11 Feb 2017 |
| Publication status | Published - Apr 2017 |
Abstract
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.
Keywords
- Digital terrain models, Graphs, RJMCMC, Spatial point processes
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Engineering (miscellaneous)
- Computer Science(all)
- Computer Science Applications
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 126, 04.2017, p. 38-55.
Research output: Contribution to journal › Article › Research › peer review
}
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 -