DocumentCode :
2315197
Title :
Road network extraction in remote sensing by a Markov object process
Author :
Lacoste, Caroline ; Descombes, Xavier ; Zerubia, Josiane
Author_Institution :
INRIA, Sophia-Antipolis, France
Volume :
3
fYear :
2003
fDate :
14-17 Sept. 2003
Abstract :
In this paper, we rely on the theory of marked point processes to perform an unsupervised road network extraction from optical and radar images. A road network is modeled by a Markov object process, where the objects correspond to interacting line segments. The prior model, called "quality candy" model, is constructed so as to exploit as far as possible the geometric constraints of this type of line network. Data properties are taken into account in the density of the process through a data term based on statistical tests. Optimization is realized by simulated annealing using a RJM-CMC algorithm. Some experimental results are provided on aerial and satellite images (optical and radar data).
Keywords :
Markov processes; feature extraction; image segmentation; optical images; radar imaging; remote sensing by radar; roads; simulated annealing; spaceborne radar; Markov object process; RJM-CMC algorithm; aerial images; algorithm convergence; geometric constraints; line segments; marked point processes; optical images; optimization; quality candy model; quality coefficients; radar images; remote sensing; reversible jump Markov chain Monte Carlo; satellite images; simulated annealing; statistical tests; unsupervised road network extraction; Data mining; Geometrical optics; Laser radar; Optical fiber networks; Optical sensors; Radar imaging; Remote sensing; Roads; Solid modeling; Spaceborne radar;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on
ISSN :
1522-4880
Print_ISBN :
0-7803-7750-8
Type :
conf
DOI :
10.1109/ICIP.2003.1247420
Filename :
1247420
Link To Document :
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