The analysis of SAR images by multiscale methods

The analysis of SAR images requires to reduce the speckle noise due to the coherent character of the radar signal. This noise is multiplicative, which often leads to logarithmically transform the modulus. A bias, which depends on the local homogeneity is introduced, and the noise is amplified. The minimum variance estimator leads to process the energy image instead of the modulus one for reducing this multiplicative noise. The proposed methods are based on a multiscale vision model for which the image is only described by its significant structural features on a set of scales. The multiscale analysis is performed by a redundant discrete wavelet transform, the a trous algorithm. We have determined the distribution law of the wavelet coefficient of the energy in the case of a statistically uniform image. This allows us to extract the significant wavelet coefficients, according to the noise. A filtering algorithm is derived by taking into account only these coefficients. This method is extended to a co-addition of SAR images. Taking into account the multiresolution support obtained from the thresholding in the wavelet transform space (WTS), the image is decomposed into a set of objects, by a 3D segmentation in WTS