| Paper: | IMDSP-P10.5 | ||
| Session: | Image Analysis | ||
| Time: | Thursday, May 20, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Image and Multidimensional Signal Processing: Image and Video Analysis | ||
| Title: | A BAYESIAN GEOMETRIC MODEL FOR LINE NETWORK EXTRACTION FROM SATELLITE IMAGES | ||
| Authors: | Caroline Lacoste; INRIA | ||
| Xavier Descombes; INRIA | |||
| Josiane Zerubia; INRIA | |||
| Nicolas Baghdadi; BRGM | |||
| Abstract: | This paper presents a two-steps algorithm to perform an unsupervised extraction of line networks from satellite images,within a stochastic geometry framework. First, we propose a new operator providing a measure of the possibility of linear structure presence on each image pixel. Second, we propose a Bayesian model in order to extract the line network from the operator output. The prior model, a Markov object process, incorporates the topological properties of the network through interactions between objects, while the line operator answers are taken into account in the likelihood. Optimization is realized by simulated annealing using a RJMCMC algorithm. An application to hydrographic network extraction is presented. | ||
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