Model-based despeckling and information extraction from SAR images

Abstract

Basic textures as they appear, especially in high res- olution SAR images, are affected by multiplicative speckle noise and should be preserved by despeckling algorithms. Sharp edges between different regions and strong scatterers also must be pre- served. To despeckle images, we use a maximum a posteriori (MAP) estimation of the cross section, choosing between different prior models. The proposed approach uses a Gauss Markov random field (GMRF) model for textured areas and allows an adaptive neigh- borhood system for edge preservation between uniform areas. In order to obtain the best possible texture reconstruction, an expec- tation maximization algorithm is used to estimate the texture pa- rameters that provide the highest evidence. Borders between ho- mogeneous areas are detected with a stochastic region-growing al- gorithm, locally determining the neighborhood system of the Gauss Markov prior. Smoothed strong scatterers are found in the ratio image of the data and the filtering result and are replaced in the image. In this way, texture, edges between homogeneous regions, and strong scatterers are well reconstructed and preserved. Addi- tionally, the estimated model parameters can be used for further image interpretation methods.