A Markov Point Process for Road Extraction in Remote Sensed ImagesReport as inadecuate




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1 ARIANA - Inverse problems in earth monitoring CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes

Abstract : In this paper we propose a new method to extract roads in remote sensed images. Our approach is based on stochastic geometry theory and reversible jump Monte Carlo Markov Chains dynamic. We consider that roads consist of a thin network in the image. We make the hypothesis that such a network can be approximated by a network composed of connected line segments. We build a marked point process, which is able to simulate and detect thin networks. The segments have to be connected, in order to form a line-netw- ork. Aligned segments are favored whereas superposition is penalized. Those constraints are taken in account by the prior model Candy model, which is an area-interaction point process.The location of the network and the specifities of a road network in the image are given by the likelihood term. This term is based on statistical hypothesis tests. The proposed probabilistic model yelds a MAP estimator of the road network. In order to avoid local minima, a simulated annealing algorithm, using a reversible jump MCMC dynamic is designed. Results are shown on SPOT, ERS and aerial images.

Keywords : ROAD EXTRACTION STOCHASTIC GEOMETRY MARKED POINT PROCESS CANDY MODEL HYPOTHESIS TESTS REVERSIBLE JUMP MCMC DYNAMICS





Author: Radu Stoica - Xavier Descombes - Josiane Zerubia -

Source: https://hal.archives-ouvertes.fr/



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