Singular value decomposition non-local mean-based polarized synthetic aperture radar (SAR) data speckle suppression method

Abstract

The invention discloses a singular value decomposition non-local mean-based polarized synthetic aperture radar (SAR) data speckle suppression method for mainly overcoming the defects that the conventional polarized SAR filter technology cannot well filter speckle noise of homogeneous areas and cannot effectively keep marginal detail information. The method comprises the following processes of: (1) inputting a covariance matrix C of polarized SAR data; (2) performing bright target retention on the covariance matrix C; (3) acquiring a logarithmic characteristic matrix from a span matrix, and performing singular value decomposition; (4) performing singular value decomposition non-local mean filtration on elements of the covariance matrix C one by one; and (5) generating a pseudo color graph through the filtered covariance matrix C by a Sinclair vector method to display and observe the filtration effect. Compared with the prior art, the method has the advantages of remarkably improving the speckle noise suppression capacity of the polarized SAR data and effectively smoothing the homogeneous areas and keeping the marginal detail information, and can be used for the pre-processing process of the polarized SAR data.

Description

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

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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 edge windows for filtering. The filtered data has a significant effect in maintaining the characteristics of the edge. However, the filtering effect is not particularly ideal in maintaining texture detail information. Therefore, in the suppression of coherent speckles, some original characteristics of the data cannot be 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. The smoothness of the texture area is better than the refined polarization 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, making it useful The edge texture information cannot be completely preserved

[0006] 4) Non-local mean filtering has achieved remarkable results in the denoising of natural images, but the original non-local mean filtering uses the gray value between two pixels as the similarity Euclidean distance to calculate, so It is not very consistent with the characteristics of the image. Due to the existence of noise, it is not accurate to calculate the similarity distance, especially in polarimetric SAR data, because the coherent speckle noise can not be ignored for the measurement of the similarity distance, so when using non-local mean filtering It causes the blurring of the edge and the loss of some original information, so that the useful edge information and texture information cannot be well preserved.

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