Method for restraining speckles of polarized SAR data based on Bayes non-local mean value

Abstract

The invention discloses a method for restraining speckles of polarized SAR data based on a Bayes non-local mean value, solves the problem that the traditional polarized SAR filter technique cannot eliminate the speckle noise in a homogeny area very well, and overcomes the shortcoming that the traditional technique cannot maintain the edge detailed information. The method comprises the steps of: (1) inputting a coherence matrix T of the polarized SAR data, (2) maintaining bright targets in the coherence matrix T, (3) filtering the Bayes non-local mean value of each element of the coherence matrix T, wherein the filtering value used for filtering is acquired from SPAN filter operation, and (4) generating a fake color drawing from the filtered coherence matrix T through a Pauli vector methodin order to observe the filtering effect. Compared with the prior art, the method of the invention obviously increases the capability of restraining the speckle noise of the polarized SAR data, can efficiently smoothen the homogeny area and maintain the edge detailed information, and can be applied to the process of pre-treating 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 spots, which can be used for denoising 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 suppression of polarimetric SA...

AI Technical Summary

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

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