Polarimetric SAR (synthetic aperture radar) data speckle suppression method based on adaptive shape non-local means

Abstract

The invention discloses a polarimetric SAR (synthetic aperture radar) data speckle suppression method based on adaptive shape non-local means, mainly solving the problems that speckle noises in a homogeneous region can not be well filtered, margin detail information can not be effectively maintained and polarimetric information can not be maintained completely. The realization process of the method comprises the following steps: (1) inputting a covariance matrix C of polarimetric SAR data; (2) maintaining bright targets of the covariance matrix C; (3) carrying out shape adaptive non-local mean filtering on non-bright target elements of the covariance matrix C; (4) carrying out weighted mean on the estimated results of different shape blocks; and (5) generating the despeckled covariance matrix C into a pcolor by adopting a Sinclair vector method so as to display the filtering observation effect. Compared with the prior art, the polarimetric SAR data speckle suppression method disclosedby the invention has the advantages that polarimetric SAR data speckle suppression capability is obviously improved, the homogenous region can be effectively smoothed, the margin detail information can be effectively maintained, and the method can be applicable to preprocessing of the polarimetric SAR data.

Description

technical field [0001] The invention belongs to the technical field of image data processing, in particular to a coherent speckle suppression method, which can be used for coherent speckle suppression of polarimetric SAR data. Background technique [0002] With the rapid development of radar technology, SAR has tended to develop in the direction of polarized SAR. Polarized SAR can obtain richer target information, which is conducive to improving target detection, identification and classification capabilities, etc., which reflects the advantages of polarized SAR system. , but polarimetric SAR, like SAR, is seriously disturbed by coherent speckle noise. Therefore, the suppression of coherence speckle has become an enduring research topic. For polarimetric SAR data, the purpose of speckle suppression is to suppress the speckle while maintaining the polarization characteristics, edge details and texture information of the data. There are many existing methods for speckle supp...

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Problems solved by technology

[0003] 1) Polarization whitening filter PWF is the earliest filtering method. This method completes the coherent speckle suppression of span data by optimizing the combination of polarization SAR data scattering matrix elements, but the disadvantage of this method is that it only affects the polarization SAR The span data in the data is subject to coherent speckle suppression, while the elements of the other polarimetric SAR data are not subject to coherent speckle suppression

[0004] 2) The most classic method is refined polarized Lee filtering, which uses a non-square edge direction window for filtering. The filtered data has a significant effect in maintaining the characteristics of the edge. However, in terms of texture detail information and edge preservation, filtering The effect is not particularly ideal, making the useful edges blurred, so in the suppression of coherent speckles, some original details of the data are not well preserved

[0005] 3) The newly proposed improved sigma filter solves the shortcomings of the original sigma filter that the dark pixels are not filtered and there are errors in the filtered data, and effectively maintains the bright target pixel. This method is better than fine in edge preservation Polarized Lee filtering method, but in the processing of edges and textures, due to the influence of coherent speckle noise, this kind of filtering still cannot best distinguish coherent speckle noise and edge texture information. Useful edge texture information cannot be fully preserved

This method is superior to the refined polarized Lee filter and the improved Sigma filter method in terms of edge preservation and homogeneous area smoothing, but in the processing of edges and textures, due to the inability to obtain enough similar blocks, This kind of filtering still cannot best smooth the homogeneous area while maintaining the edge texture information, so that the useful edge texture information cannot be completely preserved.

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