A boxcar filter is the most fundamental and simplest means of speckle denoising. As with other PolSAR speckle filters, it uses a coherence matrix or covariance matrix as the processing objects. The underlying implementation strategy of a boxcar filter is to average all the matrix elements within a square window arithmetically. This simple procedure can maintain the polarimetric properties of certain pixels very well. However, it blurs the point targets, causes a mixture of heterogeneous pixels, and degrades the spatial details.1 A series of filters developed by Lee et al., which are still blossoming, fill in part of these gaps. Scattering model-based speckle filter (SMB) was launched by Lee et al.6 SMB first of all applies Freeman and Durden decomposition to the input PolSAR covariance matrix data, and divides all the pixels into three dominant scattering categories: surface, volume, and double-bounce scattering, which serve as the initial input data. Then all the pixels will be reclassified based on the Wishart distance model, which partially characterizes the statistical property of each pixel. Finally, the filtering kernel that minimizes the mean square error is applied, which is often found in the classic filters developed by Lee et al. for single polarization SAR data. The Lee et al. improved sigma filter (LeeSig) is a revised version of the classic one that was set forward for the single polarization SAR data.7 To preserve the strong point targets, a calculation of 98% was conceived by Lee et al. The calculation acts as a preprocessing step that aims at distinguishing strong point targets from the other pixels. This filter fixes the deficiencies of the sigma range in the classic version. When implementing denoising, it adopts the minimum-mean-square-error kernel. Meanwhile, many significantly related explorations and experiments were done by Lopez-Martinez et al., who stated that the characterization of the multidimensional or multichannel speckle noise component played a pivotal role in the processing of PolSAR data.8–11 They established a compound model that consists of a multidimensional, zero-mean, complex Gaussian random variable, and a random texture variable was established. They presented a model-based filter (MB) that processes the diagonal elements and off-diagonal elements differently.