Fouad Badran

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This paper presents a new method for segmenting multispectral satellite images. The proposed method is unsupervised and consists of two steps. During the rst step the pixels of a learning set are summarized by a set of codebook vectors using a Probabilistic Self-Organizing Map (PSOM, [9]) In a second step the codebook vectors of the map are clustered using(More)
This paper presents a neural network methodology to retrieve wind vectors from ERS1 scatterometer data. First a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated wind direction are given. At least 75 % of the most probable wind directions are consistent with ECMWF winds (at ± 20°). Then the remaining(More)
We have computed two Geophysical Model Functions (one for the vertical and one for the horizontal polarization) for the NSCAT scatterometer by using neural networks. These Neural Network Geophysical Model Functions (NN-GMF) were estimated with NSCAT scatterometer sigma-0 measurements collocated with ECMWF analyzed wind vectors during the period 15 January(More)
We propose a new criteria to cluster the referent vectors of the self-organizing map. This criteria contains two terms which take into account two di erent errors simultaneously: the square error of the entire clustering and the topological structure given by the Self Organizing Map. A parameter T allows to control the corresponding in uence of these two(More)
We propose a new algorithm using topological map on binary data. The usual Euclidean distance is replaced by binary distance measures, which take into account possible asymmetries of binary data. The method is illustrated on an example taken from literature. Finally an application from chemistry is presented. We show the e ciency of the proposed method when(More)
This paper deals with an application of Neural Networks to satellite remote sensing observations. Because of the complexity of the application and the large amount of data, the problem cannot be solved by using a single method. The solution we propose is to build multimodules NN architectures where several NN cooperate together. Such system suffer from(More)
Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and some actual observations. The gradient based minimization methods require the multiplication of the transpose jacobian matrix (adjoint model), which is of huge dimension, with the derivative vector of(More)