A Polyline Process for Unsupervised Line Network Extraction in Remote SensingReportar como inadecuado




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

Abstract : This report presents a new stochastic geometry model for unsupervised extraction of line networks roads, rivers, etc. from remotely sensed images. The line network in the observed scene is modeled by a polyline process, named CAROLINE. The prior model incorporates strong geometrical and topological constraints through potentials on the polyline shape and interaction potentials. Data properties are taken into account through a data term based on statistical tests. Optimization is done via a simulated annealing scheme using a Reversible Jump Markov Chain Monte Carlo RJMCMC algorithm, without any specific initialization. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Experimental results are provided on aerial and satellite images and compared with the results obtained with a previous model, that is a segment process called -Quality Candy-.

Keywords : STOCHASTIC GEOMETRY MARKED POINT PROCESS SIMULATED ANNEALING RJMCMC LINE NETWORK EXTRACTION AERIAL AND SATELLITE IMAGES





Autor: Caroline Lacoste - Xavier Descombes - Josiane Zerubia -

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



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