SAR image filtering with the ICM algorithm

The ICM (iterated conditional modes) algorithm is an iterative proposal for the improvement of maximum likelihood segmentation. It is based upon the modelling of the a priori distribution for the classes with a multiclass Potts-Strauss Markov random field (MRF) framework. In this work, a new speckle filtering procedure is proposed, based on the ICM algorithm. This is done by increasing the number of classes on the a priori distribution, considering from 16 up to 256 levels. The model for the SAR image filtering procedure includes a multiplicative noise, described by the Rayleigh distribution, under the conditions of one look and linear detection. The ICM algorithm also uses a parameter estimation technique for the underlying MRF distribution, under the pseudolikelihood framework. These estimators are obtained in a computationally feasible form. The presented results are compared with those obtained by the well-known Nagao-Matsuyama filter, which was proposed as an edge preserving filter. The ICM speckle noise filter gave substantially superior visual results on a real SAR image over all the number of considered classes, at the price of an increased computational effort, when more than sixteen classes (grey levels) are considered