Unsupervised segmentation of radar images using wavelet decomposition and cumulants

Intensity radar images are difficult to classify because of the speckle phenomenon, which acts like a multiplicative noise and which is characterized by a Gamma distribution law. Unsupervised Bayesian segmentation applied to the whole radar image gives only good results in terms of classification rate when the look number is sufficiently high to approximate the Gamma law by a Gaussian law [1]. Multiresolution image analysis by wavelets has proved to be efficient for increasing classification performances in this Gaussian case [2,3], and also for optical images [4]. In this paper, it is proposed to extend the method to nongaussian images,by using cumulants to approximate the conditional distribution used in the classification algorithm at each level of the pyramid and to apply the procedure to simulated radar images with a low look number.<>