Speckle Suppression Of Heterogeneous Scenes In PolSAR Images

Polarimetric synthetic aperture radar(PolSAR)provides backscattering information of targets in different polarization modes,which broadens the dimensions of radar imaging.The speckle suppression plays an important role in the physical parameters extraction,target recognition and classification of PolSAR data,so it has always been an important topic in PolSAR image processing.With the increase of resolution and the deepening of PolSAR application,the processing of heterogeneous areas such as urban areas with complex textures and detailed areas on PolSAR images has attracted more and more attention.In this paper,the speckle suppression of heterogeneous regions of the polarimetric synthetic aperture radar(PolSAR)is studied and discussed.The main research work can be summarized as follows.1)This paper systematically expounds the commonly used PolSAR data model and the speckle model from the aspects of noise model and statistical properties.The heterogeneous clutter model is also introduced based on spherical invariant random vector(SIRV)in detail.This paper also expounds the popular methods of PolSAR heterogenous region and heterogenous clutter analysis based on Merlin transform and logarithmic cumulant of matrix in detail.Aiming at the problems of multi-look processing and large amount of computation in calculating logarithmic cumulants of matrices,this paper presents a fast and simple method to distinguish heterogeneity and homogeneity based on ENL parameter.2)For heterogeneous regions of low and medium resolution PolSAR images,a speckle filter based on global weighted least squares(GWLS)fitting method is proposed.The method directly fits the multiplicative fluctuation sample points globally under smoothing constraints without opening local filtering windows,thus avoiding the problem of matching and adapting filtering windows to complex heterogeneous regions.At the same time,compared with other optimization methods,GWLS method is simpler because it does not lead to complex optimization and iterative convergence problems.Solving the PolSAR despeckling problem based on GWLS is equivalent to solving nine sparse linear systems.Firstly,the SPAN image is used to construct the guidance image,and the five-point spatially inhomogeneous Laplacian matrix is calculated.Secondly,the coefficient matrices of nine linear systems are constructed by using the weighted sum of Laplacian matrix and an identity matrix.Finally,each speckle-free channel of PolSAR data is reconstructed equally and globally by solving the linear systems with the same coefficient matrix.3)For heterogeneous regions of high resolution PolSAR images,a SIRV-based dual-domain filter for speckle suppression of PolSAR is proposed.SIRV model is a simple clutter decomposition model,which decomposes the clutter into two independent domains: texture domain and polarimetric domain(also known as speckle domain).Dual-domain filter takes full advantages of the SIRV model to decompose the PolSAR speckle suppression task into polarimetric domain filtering and texture domain filtering.The polarimetric domain filtering involves estimating a stable state of the normalized coherency matrix with similar samples via fixed-point iteration.For this purpose,the nonlocal patch matching distance measure and the cutoff threshold in the SIRV model case are defined to select similar samples.The texture domain filtering involves reconstructing a sparse texture image without the effect of speckle.For this purpose,patch ordering-based SAR image despeckling via transform-domain filtering(SAR-POTDF),which is based on simultaneous sparse coding(SSC),is applied for the texture filtering.After the dual-domain filtering,a specklefree PolSAR image can be reconstructed by the product of the texture and the normalized coherency matrix.The effectiveness and robustness of the proposed method was demonstrated by a series of experiments.