DocumentCode :
699465
Title :
Unsupervised line network extraction from remotely sensed images by polyline process
Author :
Lacoste, Caroline ; Descombes, Xavier ; Zerubia, Josiane ; Baghdadi, Nicolas
Author_Institution :
Joint Res. Group, UNSA, Sophia Antipolis, France
fYear :
2004
fDate :
6-10 Sept. 2004
Firstpage :
261
Lastpage :
264
Abstract :
This article presents a new stochastic geometric model for unsupervised extraction of line network (roads, rivers,...) from remotely sensed images. The line network in the observed scene is modeled by a polyline process, named CAROLINE. The prior model incorporates the topological properties of the network considered through potentials on the polyline shape and interactions between polylines. Data properties are taken into account through a data term based on statistical tests. Optimization is realized by simulated annealing using a RJMCMC algorithm. Some experimental results are provided on aerial and satellite images.
Keywords :
computational geometry; geophysical image processing; remote sensing; rivers; roads; simulated annealing; statistical analysis; CAROLINE; RJMCMC algorithm; aerial images; polyline process; remotely sensed images; rivers; roads; satellite images; simulated annealing; statistical tests; stochastic geometric model; unsupervised line network extraction; Abstracts; Artificial neural networks; Monte Carlo methods; Optimization; Radio access networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference, 2004 12th European
Conference_Location :
Vienna
Print_ISBN :
978-320-0001-65-7
Type :
conf
Filename :
7079995
Link To Document :
بازگشت