Abstract:

For better interpretation of synthetic aperture radar (SAR) images, the speckle filtering is an important issue. In the area of speckle filtering, the proper averaging of samples with similar scattering characteristics is of great importance. However, existing filtering algorithms are either lack of a similarity judgment of scattering characteristics or using only intensity information for similarity judgment. A novel polarimetric SAR (PolSAR) speckle filtering algorithm based on the mean shift theory is proposed. As polarimetric covariance matrices or coherency matrices form Riemannian manifold, the pixels with similar scattering characteristics gather closely and those with different scattering characteristics separate in this hyperspace. By using the range-spatial joint mean shift theory in Riemannian manifold, the pixels chosen for averaging are ensured to be close not only in scattering characteristics but also in the spatial domain. German Aerospace Center (DLR) L-Band Experiment SAR (E-SAR) data and East China Research Institute of Electronic Engineering (ECRIEE) PolSAR data are used to demonstrate the efficiency of the proposed algorithm. The filtering results of two commonly used speckle filtering algorithms, refined Lee filtering algorithm and intensity driven adaptive neighborhood (IDAN) filtering algorithm, are also presented for the comparison purpose. Experiment results show that the proposed speckle filtering algorithm achieves a good performance in terms of speckle filtering, edge protection as well as polarimetric characteristics preservation.