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Unsupervised line network extraction in remote sensing using a polyline process
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DOI：10.1016/j.patcog.2009.11.003.png)
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Abstract
En 中文
Marked point processes provide a rigorous framework to describe a scene by an unordered set of objects. The efficiency of this modeling has been shown on line network extraction with models manipulating interacting segments. In this paper, we extend this previous modeling to polylines composed of an unknown number of segments. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Results on aerial and satellite images show that this new model outperforms the previous one. (C) 2009 Published by Elsevier Ltd.
Keywords:
Line network extraction
Aerial and satellite images
Stochastic geometry
Marked point process
Simulated annealing
RJMCMC
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