A speckle filter based on spatial texture analysis of SAR data
Olivier D’Hondt, Laurent Ferro-Famil, Éric Pottier · 2005
This paper proposes a new model for the two-point statistics of spatial texture in SAR images. The autocovariance function is locally approximated by a 2-D Anisotropic Gaussian Kernel (AGK) in order to characterise texture by its local orientation and anisotropy. The estimation of texture parameters at a given scale is based on the Gradient Structure Tensor (GST) operator and does not require the explicit computation of the autocovariance. Finally, a new filter called AGK-MMSE that takes into account this spatial information is introduced and compared to the refined MMSE filter. The proposed filter shows better performances in terms of texture preservation and structure enhancement. I. INTRODUCTION In this paper, a new model for the two-point statistics of SAR intensity is presented. A parametric form for the 2-D autocovariance function is used to introduce in the previous models the notions of local orientation and spatial anisotropy. This simple model handles deterministic structures as well as the spatial correlation of heterogeneous clutter. This descrip- tion is then shown to be useful in the context of adaptive speckle filtering. The theoretical model is introduced in Section II, then a method for parameter estimation is presented in Section III. An enhancement of the traditional adaptive filters based on this model is presented in Section IV. Section V shows results on real SAR data. Finally, Section VI concludes the article.