Assessment of two Gibbs random field based feature extraction methods for SAR images using a Cramer-Rao bound

This paper is an assessment of two feature extraction methods based on Gibbs random field models using a Cramer-Rao Lower Bound and a Fisher Information Matrix. The evaluated methods are the model-based despeckling (MBD) method and the maximum a posteriori autobinomial method (MAP-ABM). The assessment has been carried out using TerraSAR-X and simulated SAR data. In here, data with an increasing number of looks have been used in order to study 1) how the estimated parameters approach the real ones, 2) how their variances get closer to the CRLB, and 3) how to make a comparison between both model parameter