Local Maximal Homogeneous Region Search for SAR Speckle Reduction With Sketch-Based Geometrical Kernel Function

With the flourish of the nonlocal mean method, the neighborwise similarity metric is widely applied in speckle reduction for its robust performance on the search of similar samples. In this metric, an isotropic kernel function is usually chosen to aggregate the corresponding pixels' distance between two neighborhoods. It means that the kernel function is considered as the explanation of the local spatial relationship at each pixel. However, for anisotropic features (such as edges and lines), a strong relationship exists along their directions rather than across them, so the isotropic kernel is not suitable to explain the spatial relationship around these features. Meanwhile, due to the inherent speckle in synthetic aperture radar (SAR) images, the discrimination and exploration of the geometrical properties of anisotropic features are important for the construction of adaptive kernel function. In this paper, the sketch map which is a representation of the sketch information of SAR images is extracted as the criterion for designing the kernel function. Meanwhile, due to the properties of symmetric and maximal self-similarity, a modified ratio distance is proposed and used jointly with the constructed kernel function as a similarity metric. Then, under the local stationary assumption, the local maximal homogeneous region of each pixel is searched by using the region growing method with the proposed metric. Moreover, maximal likelihood rule is used within the region for the estimation of true value. From the experiments on the synthetic and real SAR images, a promising performance in terms of speckle reduction and preservation of the details is achieved by our proposed method.