Danielle Ducrot

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In this paper we present a Markovian method of classification of the satellite images, this method is based on a minimization of the posterior energy by the ICM method (iterated conditionnal mode) with the introduction of constraints of the spatial context. The originality of our method is the variability over the iterations of a temperature factor like in(More)
—The classification of remotely sensed images knows a large progress taking in consideration the availability of images with different resolutions as well as the abundance of classification's algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support vector machines (SVM) which are a group of(More)
The classification of remotely sensed images knows a large progress taking in consideration the availability of images with different resolutions as well as the abundance of classification's algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support vector machines (SVM). For this purpose, we(More)
—The classification of remote sensing images has done great forward taking into account the image's availability with different resolutions, as well as an abundance of very efficient classification algorithms. A number of works have shown promising results by the fusion of spatial and spectral information using Support Vector Machines (SVM) which are a(More)
The objective of this work is, in the first place, the integration in a fusion process using hybrid DSmT model, both, the contextual information obtained from a supervised ICM classification with constraints and the temporal information with the use of two images taken at two different dates. Secondly, we have proposed a new decision rule based on the DSmP(More)