**6. Conclusion**

The purpose of this study was the production of space maps with the synthetic aperture radar (SAR) images. To achieve this, we proceed by adopting approaches optimized of texture analysis of images, using the statistical parameters of Haralick generalized at the order n. The approach is based on the concept of generic tree. It has the advantage of being less time consuming calculation from the conventional approach which frequently uses cooccurrence matrices for texture analysis and especially the processed images are generally very large sizes. In classification, the approach relies on the concept of detecting "modes" and "valleys" of histograms in a SAR image using classification of type unsupervised. For each of the SAR images, the histogram of the convolution image obtained at base of texture parameter is represented and approximated by a regression line called "signature" using

least square method. The operation of the histogram and the signature of the texture image can facilitate the detection of classification thresholds. The main interest of the proposed approach is that we have results that are approaching the best of the reality field; it also does not require a serial multi-date SAR data for the realization of satellite image maps. The method was successfully tested on two satellite images from two different sensors: one from the ESAR program obtained at the resolution 6m and one other from the ERS-1 sensor of resolution 25 m.

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A limitation of the classification approach lies at the empirical detection of local extrema. A perspective would then be to automate the detection of the number of classes and local extrema.
