Autobinomial Model for SAR Image Despeckling and Information Extraction

This paper presents a model-based despeckling (MBD) of synthetic aperture radar (SAR) images using Bayesian analysis. The SAR image is despeckled using first-order Bayesian inference. The novelty in this paper is an autobinomial model (ABM), which models a prior probability density function (pdf); meanwhile, the likelihood pdf is modeled as a gamma distribution. Analytically, a solution for a maximum a posteriori estimate using an autobinomial prior cannot be computed; therefore, an approximation is introduced using differential. The best ABM for approximating the texture parameters in SAR images is found by using second-order Bayesian inference. The edges in the SAR images are detected using region borders, which have statistically different properties. Coefficient of variation is used to distinguish between homogeneous and heterogeneous areas. The experimental results show that the proposed method preserves the textural features and removes noise significantly in the homogeneous and heterogeneous regions. The proposed despeckling method is good regarding objective measures for synthetic images and better despeckles the real SAR images, when compared with the state-of-the-art MBD methods.