Cramer-Rao Bound-Based Evaluation of Texture Extraction from SAR Images

SAR images are affected by speckle which is a coherent process modelled as a multiplicative noise. It makes the automatic image classification difficult. Thus, many methods have been developed to remove speckle from SAR images while preserving the useful information of the image such as texture. This paper presents an evaluation of texture extraction parameter estimation methods using Cramer-Rao lower bound (CRLB). The first evaluated method is model-based despeckling (MBD) algorithm, which uses Gauss-Markov random fields as prior. The second one is the maximum a posteriori auto-binomial method (MAP-ABM), which rather uses auto-binomial model as prior. The evaluation has been carried out using simulated SAR data. In here, data with increasing number of looks have been used in order to study 1) how the estimated parameters approach the real one, and 2) how their variances get closer to CRLB. The experimental results show the superiority of MBD parameter estimation. Both MBD and MAP-ABM provide the most robust texture parameters when the number of look is between 3 and 4.