Combined use of MAP estimation and K-means classifier for speckle noise filtering in SAR images

The main purpose of this work is to study and implement a maximum a posteriori (MAP) filter combined with the K-means algorithm in order to reduce speckle noise in SAR images. The K-means algorithm over Li's (1988) coefficient is used to classify the noisy image in regions of homogenous statistics. This kind of information is used as a guide for choosing the best window size for parameter estimation in the MAP filtering. This paper is based on the multiplicative model for speckle and considers different densities to describe the "a priori" knowledge. It suggests a new adaptive filtering algorithm based on the MAP approach and clustering.