**5. Finding a solution for denoising in sar imagery using wavelet**

The wavelet transform is a mathematical tool widely used in image processing. Some applications of the transform to remote sensing images have been investigated in the literature. It was found useful for texture analysis , image compression and noise reduction . The transform allows representation of a signal onto an orthonormal basis. Each term of the basis represents the signal at a given scale. In order to decompose the signal onto the basis, the algorithm developed is applied to the signal. It consists of iterations of one-dimensional highpass and low-pass filtering steps. The algorithm creates a pyramid of low-resolution approximations as well as a wavelet pyramid in which the details a stored as wavelet coefficients. This representation is called wavelet representation(Jiang et al., 2000).. One way of image analysis, is to choose the wavelet for speckle in SAR images which is always problematic(Ali,2007). Often, it is impenitent to reduce noise-before trying to extract scene features. Many filters have been developed to improve image quality by conserving the intrinsic scene features and textures. Interpretation of SAR images by human is possible in the presence of speckle. The wavelet transform, as the mammal visual system, provides and allows for a multiscale analysis of images. This section presents how the wavelet transform can be used for extraction of linear features such as edges and thin stripes. It will also show how speckle can be relaxed by taking into account the speckle contribution to wavelet coefficients.
