CN107644408A - A kind of polarization radar image denoising method based on Anisotropic diffusion - Google Patents
A kind of polarization radar image denoising method based on Anisotropic diffusion Download PDFInfo
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Abstract
The invention discloses a kind of polarization radar image denoising method based on Anisotropic diffusion, it is characterised in that comprises the following steps：Step 1, the polarization statistics similarity of each pixel and its neighborhood pixel in original Noise radar remote sensing image is calculated, completes initial Anisotropic diffusion denoising；Step 2, each pixel of image obtained by last iteration and the new polarization similarity of neighborhood pixel are calculated before each iterated denoising starts, and combine the polarization similarity information obtained by step 1 and realize denoising；Step 3, step 2 are persistently carried out, until meeting that stopping criterion for iteration completes denoising；Advantage is polarization radar image denoising method of the present invention compared with the conventional method：Method simple and effective, noise can be significantly removed, also can effectively keep the detailed information of image.
Description
Technical field
The present invention relates to remote sensing image process field, and in particular to a kind of polarization radar based on Anisotropic diffusion is gone
Method for de-noising.
Background technology
Polarimetric synthetic aperture radar (polarimetric synthetic aperture radar, PolSAR) remote sensing
System has the advantages of round-the-clock, round-the-clock earth observation compared with optics remote sensing system, can launch simultaneously, reception is horizontal and
Vertical polarimetric radar ripple, therefore the abundant back scattering information of atural object can be obtained, obtained extensively in many industry fields
Using.In recent years, multiple high-resolution PolSAR systems come into operation in succession both at home and abroad, and particularly, China launched in 2016
Domestic first high-resolution full-polarization SAR satellite -- high score three, further promote PolSAR application prospect.However,
Due to the limitation of imaging mechanism so that SAR images unavoidably have coherent speckle noise, and this problem is in full-polarization SAR image
In it is more prominent and complicated, seriously reduce the quality of image, constrain its application potential in every respect.Therefore, development has
The PolSAR denoising methods of effect, there is important researching value and practical significance.
In recent years, in terms of PolSAR image denoisings, researcher both domestic and external gradually carries from different research angles
Some fruitful methods are gone out, these methods are broadly divided into two big primary categories：One kind is counted based on local noise
The denoising method of characteristic, wherein representational method such as document 1:J.S.Lee,T.L.Ainsworth,Y.Wang,and
K.S.Chen,"Polarimetric SAR Speckle Filtering and the Extended Sigma Filter,"
IEEE Transactions on Geoscience and Remote Sensing,53(3):1150-1160,2015. propose
A kind of wave filter based on local linear least mean-square error, such as document 2：A.Alonso-González,C.López-Mart
ínez,and P.Salembier,"Filtering and segmentation of polarimetric SAR data
based on binary partition trees,"IEEE Transactions on Geoscience and Remote
Sensing,50(2):593-605,2012. first split to image, then take average as object to each cutting object
Value after interior pixel denoising；Another kind of is the denoising method based on non-local mean thought, exemplary process therein such as document
3：J.Chen,Y.Chen,W.An,Y.Cui,and J.Yang,"Nonlocal filtering for polarimetric
SAR data:a pretest approach,"IEEE Transactions on Geoscience andRemote
Sensing,49(5):1744-1754,2011. and document 4：H.Zhong,J.Zhang,and G.Liu,"Robust
polarimetric SAR despeckling based on nonlocal means and distributed Lee
filter,"IEEE Transactions on Geoscience and Remote Sensing,42(7):4198-4210,
2014.Generally speaking, denoising method based on local noise statistical property is although simple and easy to do, computational complexity is low, but often
It is difficult to the detailed information for preferably keeping image after denoising；Although on the contrary, the method based on non-local mean can obtain compared with
Good denoising effect, but computational complexity is generally very big, it is difficult to a wide range of place for being applied to large-size PolSAR images
Reason.
The theoretical research in image processing field of partial differential equation (Partial Differential Equation, PDE)
The 60 to 70's of last century is started from, it is applied to the research in terms of Digital Image Noise earliest.Image processing method based on PDE
Method has a good Fundamentals of Mathematics, deep theoretical background, and algorithm stability is also higher.Among the denoising method based on PDE,
Anisotropy parameter method because its computational complexity is low, significantly inhibit noise while also can effectively keep image texture information etc.
Feature and get the attention.At present, anisotropy parameter method is widely used to the Denoising Problems of all kinds of images, such as
Digital picture, medical image etc..However, due to PolSAR systems and the complexity of data, for each to different of PolSAR images
Property diffusing and de-noising method is also very rare so far.
The content of the invention
It is an object of the invention to overcome the above-mentioned problems in the prior art, there is provided one kind is based on Anisotropic diffusion
Polarization radar image denoising method, can significantly remove noise, also can effectively keep the detailed information of image.
To realize above-mentioned technical purpose and the technique effect, the present invention is to be achieved through the following technical solutions：
A kind of polarization radar image denoising method based on Anisotropic diffusion, it is characterised in that comprise the following steps：
Step 1, it is similar to the polarization statistics of its neighborhood pixel to calculate each pixel in original Noise radar remote sensing image
Degree, completes initial Anisotropic diffusion denoising；
Step 2, it is new that each pixel of image and neighborhood pixel obtained by last iteration are calculated before each iterated denoising starts
Polarize similarity, and combines the polarization similarity information obtained by step 1 and realize denoising；
Step 3, step 2 are persistently carried out, until meeting that stopping criterion for iteration completes denoising.
Further, in the step 1, using the likelihood ratio test law amount raw video being distributed based on Fu Wei Saudi Arabia
Polarization similarity between middle pixel and as original gradient value：
Wherein,Represent the polarization covariance matrix of pixel x in raw video.
Further, in the step 1, the original ladder for the likelihood ratio test method acquisition being distributed based on Fu Wei Saudi Arabia is utilized
Angle value builds diffusion coefficient, and then completes initial Anisotropic diffusion denoising：
Wherein, Z represents the set of pixel x neighborhood territory pixel.
Further, in the step 2, before each iterated denoising starts, joint raw video polarization similarity information
The gradient information of current iteration image is calculated with last time denoising gained image polarization similarity information, is specially：
Before the iterated denoising of t starts, image C obtained by the denoising of t- time Δts is calculatedt-ΔtPolarization between pixel is similar
Property
Wherein, C*True muting image is represented, combines the gradient information of raw videoIt is calculated
The gradient information of current time image is
Further, in the step 2, in each iterated denoising, diffusion coefficient is built using new gradient information,
Then following denoising process is completed：
Further, the diffusion coefficient calculation is
Wherein, k is normalized parameter.
Further, the method to set up of the normalized parameter k is：Calculate current iteration image all pixels and its neighborhood
The Grad of pixel, by these value ascending sorts, 90% accumulation quantile is calculated, and using the numerical value as current iteration parameter
K value.
Further, in the step 3, the iterated denoising end condition is, calculate image obtained by t iteration with
Diversity factor between image obtained by t- time Δts iteration：
Wherein, N represents image total pixel number, and S represents the total power signal of PolSAR data, if before and after iteration between image
Diversity factor is more than default threshold value σ=0.01, then starts the process of next iterated denoising；Otherwise, iterated denoising process is terminated, will
Image is as final denoising image obtained by the moment iteration.
The present invention income effect be：
Advantage is the polarization radar image denoising method of the present invention compared with the conventional method：Method simple and effective, both
Noise can be significantly removed, also can effectively keep the detailed information of image.
Brief description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, used required for being described below to embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for ability
For the those of ordinary skill of domain, on the premise of not paying creative work, it can also be obtained according to these accompanying drawings other attached
Figure.
Fig. 1 is broad flow diagram of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained all other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
As shown in figure 1, the present invention is a kind of polarization radar image denoising method based on Anisotropic diffusion, including with
Lower step：
Step 1, calculate the polarization similarity between adjacent pixel in raw video；
Step 2, initial Anisotropic diffusion denoising is carried out to image；
Step 3 is new between each pixel of image and neighborhood pixel being calculated before each iterated denoising starts obtained by last time denoising
Polarization similarity；
Step 4, combine the original polarization similarity between consideration pixel and previous iteration similarity information and currently changed
For the gradient information of image, and then the process of an iteration denoising is realized to image；
Step 5, after the completion of each iterated denoising, calculate between image obtained by image obtained by this iteration and previous iteration
Diversity factor；
Step 6, the diversity factor between iteration twice is compared with threshold value set in advance, if diversity factor is more than threshold value,
Then return to step three, start the process of next iteration denoising；Otherwise, iterated denoising process is terminated, and by obtained by this iteration
Image is as final denoising image.
In the present invention, handled polarization radar data are to form storage with polarization covariance matrix and characterize.
Meet antenna reciprocity：|SHV|=| SVH| on the premise of, polarization covariance matrix C expression formula is as follows：
Wherein, j represents the imaginary part of symbol, | SHV| represent the amplitude information of the horizontal polarized wave for sending vertical reception, φHVTable
Show the phase information of this polarized wave, other symbol definitions are similar.
The present invention solves the problems, such as it is to measure the polarization similarity in raw video between pixel first.Due to by coherent spot
The covariance matrix of the PolSAR data of influence of noise obeys the distribution of Fu Wei Saudi Arabia, therefore utilizes the likelihood based on the distribution of Fu Wei Saudi Arabia
The polarization similarity between pixel is measured than method of inspection, obtaining the Similarity Measure mode is：
The similarity span for (- ∞, 0], more similar between pixel, then the measure value is closer to 0.
Next structure diffusion coefficientThe structure principle of diffusion coefficient is：For each pixel
Say, the absolute value of the gradient on some direction of image is smaller, then diffusion coefficient in this direction should be bigger；On the contrary, in this direction
Diffusion coefficient should be smaller, so as to ensure that Anisotropic diffusion method can significantly remove noise in image smooth region, and
Texture can nearby reduce the loss of details.Based on above principle, the present invention uses following diffusion coefficient building mode：
Wherein, k is normalized parameter, and its method to set up will do further introduction behind this specification.
According to the principle of anisotropy parameter denoising, the present invention realizes the initial denoising to image in the following way：
In aforesaid operations, compare and pass traditional noise-reduction method, simple and effective, can significantly remove noise, also can be effective
Keep the detailed information of image.
In the description of this specification, the description of reference term " one embodiment ", " example ", " specific example " etc. means
Feature, which is lived, with reference to specific features, structure, the material that the embodiment or example describe is contained at least one implementation of the invention
In example or example.In this manual, identical embodiment or example are not necessarily referring to the schematic representation of above-mentioned term.
Moreover, specific features, structure, material or the feature of description can close in any one or more embodiments or example
Suitable mode combines.
Present invention disclosed above preferred embodiment is only intended to help and illustrates the present invention.Preferred embodiment is not detailed
All details are described, it is only described embodiment also not limit the invention.Obviously, according to the content of this specification,
It can make many modifications and variations.This specification is chosen and specifically describes these embodiments, is to preferably explain the present invention
Principle and practical application so that skilled artisan can be best understood by and utilize the present invention.The present invention is only
Limited by claims and its four corner and equivalent.
Claims (8)
1. a kind of polarization radar image denoising method based on Anisotropic diffusion, it is characterised in that comprise the following steps：
Step 1, the polarization statistics similarity of each pixel and its neighborhood pixel in original Noise radar remote sensing image is calculated,
Complete initial Anisotropic diffusion denoising；
Step 2, each pixel of image obtained by last iteration and the new polarization of neighborhood pixel are calculated before each iterated denoising starts
Similarity, and combine the polarization similarity information obtained by step 1 and realize denoising；
Step 3, step 2 are persistently carried out, until meeting that stopping criterion for iteration completes denoising.
2. according to the method for claim 1, it is characterised in that：In the step 1, using what is be distributed based on Fu Wei Saudi Arabia
Polarization similarity in likelihood ratio test law amount raw video between pixel and as original gradient value：
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Wherein,Represent the polarization covariance matrix of pixel x in raw video.
3. according to the method for claim 1, it is characterised in that：In the step 1, utilize what is be distributed based on Fu Wei Saudi Arabia
The original gradient value structure diffusion coefficient that likelihood ratio test method obtains, and then complete initial Anisotropic diffusion denoising：
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Wherein, Z represents the set of pixel x neighborhood territory pixel.
4. according to the method for claim 1, it is characterised in that：In the step 2, before each iterated denoising starts, connection
Close the ladder that image polarization similarity information obtained by raw video polarization similarity information and last time denoising calculates current iteration image
Information is spent, is specially：
Before the iterated denoising of t starts, image C obtained by the denoising of t- time Δts is calculatedt-ΔtPolarization Characteristics Similarity between pixel
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5. according to the method for claim 1, it is characterised in that：In the step 2, in each iterated denoising, using new
Gradient information structure diffusion coefficient, then complete following denoising process：
6. the method according to right wants p to ask 3 and 5, it is characterised in that：The diffusion coefficient calculation is
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Wherein, k is normalized parameter.
7. according to the method for claim 6, it is characterised in that：The method to set up of the normalized parameter k is：Calculate current
Iteration image all pixels and the Grad of its neighborhood territory pixel, by these value ascending sorts, calculate 90% accumulation quantile, and
Value using the numerical value as current iteration parameter k.
8. according to the method for claim 1, it is characterised in that：In the step 3, the iterated denoising end condition is,
Calculate the diversity factor between image obtained by image obtained by t iteration and t- time Δts iteration：
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Wherein, N represents image total pixel number, and S represents the total power signal of PolSAR data, if difference between image before and after iteration
Degree is more than default threshold value σ=0.01, then starts the process of next iterated denoising；Otherwise, iterated denoising process is terminated, during by this
Image obtained by iteration is carved as final denoising image.
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| CN112950492A (en) * | 2021-01-28 | 2021-06-11 | 中国石油大学(华东) | Full-polarization SAR image denoising method based on self-adaptive anisotropic diffusion |
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| CN104408472A (en) * | 2014-12-05 | 2015-03-11 | 西安电子科技大学 | Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method |
| CN105719249A (en) * | 2016-01-15 | 2016-06-29 | 吉林大学 | Three-dimensional grid-based airborne LiDAR point cloud denoising method |
| CN106251368A (en) * | 2016-06-12 | 2016-12-21 | 中国科学院遥感与数字地球研究所 | SAR image based on BEMD and the fusion method of multispectral image |
-
2017
- 2017-09-27 CN CN201710888640.4A patent/CN107644408A/en not_active Withdrawn
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112950492A (en) * | 2021-01-28 | 2021-06-11 | 中国石油大学(华东) | Full-polarization SAR image denoising method based on self-adaptive anisotropic diffusion |
| CN112950492B (en) * | 2021-01-28 | 2022-04-29 | 中国石油大学(华东) | Full-polarization SAR image denoising method based on self-adaptive anisotropic diffusion |
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