CN106530243A - Synthetic aperture radar image speckle noise filtering method - Google Patents
Synthetic aperture radar image speckle noise filtering method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000001914 filtration Methods 0.000 title claims abstract description 24
- 230000009466 transformation Effects 0.000 claims abstract description 6
- 238000010008 shearing Methods 0.000 claims description 28
- 230000001427 coherent effect Effects 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000000717 retained effect Effects 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 abstract description 4
- 230000003287 optical effect Effects 0.000 abstract description 3
- 230000010365 information processing Effects 0.000 abstract 2
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- 238000005516 engineering process Methods 0.000 description 10
- 238000003384 imaging method Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 3
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- 230000015572 biosynthetic process Effects 0.000 description 2
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- 239000011159 matrix material Substances 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
- G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
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Abstract
The invention belongs to the field of spaceborne synthetic aperture radar and optical image information processing and discloses a synthetic aperture radar image speckle noise filtering method, which can improve the visual quality of the image, can reduce influences on the image by the speckle noise and can carry out filtering and denoising processing on the synthetic aperture radar. The method of the adopted technical scheme comprises the following steps: 1, a synthetic aperture radar image f is inputted; 2, logarithm taking operation is carried out on a coherence tomography image; 3, shear wave transformation is carried out on the image log(f) after logarithm taking to acquire a shear wave coefficient; 4, hard threshold operation is carried out on the acquired shear wave coefficient S<j, l, x, y>; 5, shear wave inverse transformation is carried out on the kept part after hard threshold operation, and the filtering purpose is achieved; and 6, an image after filtering is outputted. The method of the invention is mainly applied to a synthetic aperture radar and optical image information processing situation.
Description
Technical field
The invention belongs to satellite-borne synthetic aperture radar and optical image information process field, are related to a kind of new synthetic aperture
The speckle noise filtering method of radar image.
Background technology
Synthetic aperture radar (synthetic aperture radar, SAR) is a kind of high-resolution microwave imaging system,
The technology comes across the 1950's earliest, in decades thereafter, improves and perfect through continuous, exists now
Extensive practical application is obtained on many fields of national economy and military affairs, such as agricultural, geology, the hydrology, ocean, taking precautions against natural calamities subtracts
Calamity, national defence, military surveillance, marine surveillance, military mapping and camouflage identification and detect etc. aspect.The imaging of synthetic aperture radar is former
Reason is to irradiate tested surface using microwave section pulse signal, and receives the electromagnetic signal reflected from irradiation area, is then adopted
Signal processing mode synthesizes the spatial domain high-resolution two-dimensional radar image of earth surface reflection from all signals for receiving.Using synthesis
Aperture technique, pulse compression technique and signal processing technology, can realize very high azimuth discrimination with little true aperture antenna
Rate.[1-3]
But as other coherence imaging systems, being studded with the SAR image formed by polarization sensitive synthetic aperture radar system big
The coherent speckle noise of the amount property taken advantage of.The speckle noise of a large amount of random distributions not only causes the visual effect of SAR image greatly to decline, and
And greatly constrain the reliability and effectiveness of the successive image technology such as SAR image feature extraction, target following.Therefore, SAR
The suppression research of image speckle is with important theoretical and realistic meaning.[4-9]
List of references
1 clear ripple synthetic aperature radar principle, systematic analysiss and applied science publishing house, 1989
2 Liu Yong are smooth to wait radar imaging technology, publishing house of Harbin Institute of Technology, 199962-68
Beijing Chinese science and technology technology publishing house, 19967-10 are studied in 3 Guo East China, the spaceborne imaging radar resource exploration of Xu Guanhua
4Cumming I.G., Wong F.H.Digital Processing of Synthetic Aperture Radar
Data：Algorithms and Implementation [M] .Norwood, MA：Artech House, 2005.
5Soumekh M.Synthetic Aperture Radar Signal Processing with MATLAB
Algorithm[M].1999
6Henri M.Processing of synthetic aperture radar images[M].London
2008.
7 old few ripple .SAR speckle reduction algorithms research [D]. Wuhan：Central China University of Science and Technology Ph.D. Dissertation,
2010, pp：1-5.
8Lee J.S.Digital image enhancement and noise filtering by using local
Statistics [J] .IEEE Trans.Pattern Anal.Machine Intell., 1980,2 (2), pp：165-168.
9Kuan D.T., Sawchuk A.A., Strand T.C., et al.Adaptive noise smoothing
filter for images with signal-dependent noise[J].IEEE Trans.Pattern Anal
Machine Intell., 1985,7 (2), pp：165-177.
The content of the invention
To overcome the deficiencies in the prior art, it is contemplated that realizing improving the visual quality of image, speckle noise pair is reduced
The impact of image, can be filtered noise reduction process to synthetic aperture radar.The technical solution used in the present invention is, synthetic aperture
The speckle noise filtering method of radar image, step are as follows：
Step 1：It is input into a width diameter radar image f；
Step 2：Operation of taking the logarithm is carried out to this width coherent tomographic image, both：Log (f)=log (x)+log (z), wherein,
Log () represents operation of taking the logarithm to image, and x represents the image for not having influence of noise, and z represents speckle noise；
Step 3：To both having taken the image after logarithm：Log (f) carries out shearing wave conversion in order to obtain shearing wave system
Number：
Sj,l,x,y=Xj,l,x,y+Zj,l,x,y, wherein Sj,l,x,y,Xj,l,x,y,Zj,l,x,yLog (f), log (x), log are represented respectively
The shearing wave coefficient of (z)；J, l represent the scale parameter in shearing wave coefficient, directioin parameter respectively；X, y represent the space of image
Coordinate.
Step 4：To the shearing wave coefficient S for obtainingj,l,x,yA hard -threshold operation is carried out, the shearing wave coefficient of noise is put
For 0, retain other parts；
Here hard -threshold operates and is：
δ=Tscalars (i) * σ * dst_scalars (j)
I=1,2,3.j=1,2,3,4
Step 5：The part retained after to carrying out hard -threshold operation carries out shearing wave inverse transformation, reaches filtering purpose；
Step 6：Image after output filtering.
Shearing wave is selected to be four layers, three directions.
The characteristics of of the invention and beneficial effect are：
The method for being filtered with diameter radar image that presently, there are mainly has wavelet filtering, the filter of profile ripple
Ripple, bent ripple filtering method, and some filter in spatial domain all achieve certain achievement.The present invention comes relative to frequency domain filtering
Say, shearing wave has simpler mathematical expression mode, a more rigorous complete demonstration, and be truly many
Change of scale.Relative to the filter in spatial domain such as certain methods such as PM models, method of the present invention computational efficiency is higher, it is not necessary to
Iterative process, saves the calculating time.
Description of the drawings：
Fig. 1 represents the image-forming principle of diameter radar image.
The Performance comparision of tri- kinds of filtering methods of Fig. 2.
Specific embodiment
The present invention is that noise reduction process is carried out to the image that synthetic aperture radar imaging technology is obtained, therefore first illustratively
The image-forming principle of synthetic aperture radar, as shown in Figure 1, SAR imaging systems include synthetic aperture radar image-forming principle：One micro-
Wave impulse emitter, a receptor and the antenna for radio signals.SAR system is normally held in a movement
On platform, ground scene, such as accompanying drawing 1 are irradiated using side-looking mode, the emitter of SAR imaging systems is along vertical with movement locus
Side-looking direction transmitting microwave section pulse signal, the pulse signal is irradiated onto in ground scene and reflects, and receptor is then
It is responsible for receiving all of reflected signal, and emitter is required for completing at the transmitting-receiving of wireless signal by sideward-looking antenna with receptor
Reason.Then the reflected signal for receiving is synthesized SAR image by signal processing technology by SAR imaging systems again.
Next introduce the formation mechenism of speckle：As in scattering unit, numerous physical scatterers are sub in distribution and characteristic
On randomness, therefore, although the subreflexive radar return of numerous physical scatterers that SAR system is received in same scattering unit
It is consistent in frequency, but all have differences in amplitude and phase place.Further, it is anti-by inconsistent scattering of numerous amplitudes and phase place
It is emitted back towards ripple and is superimposed the scattering unit echo-signal (correspondence one pixel in SAR image) to be formed showing in some scattering units
For strong signal, in some scattering units, weak signal is shown as.So, echo-signal presents different powers in different scattering units
The random character of signal so that each scattering unit echo just not exclusively determined by the scattering coefficient of the irradiated scene of SAR wave beams, and
It is to present strong random fluctuation up and down around these scattering coefficient values, so as to build SAR using each scattering unit echo
During image, in causing SAR image, random scatter goes out a large amount of granular coherent spots.
Next process is filtered to the image with speckle noise using filtering method proposed by the present invention.It is concrete to walk
It is rapid as follows：
1 under matlab platforms input picture, matrixing process will be carried out with noise image
2 because speckle noise is a kind of multiplicative noise in itself, it would be desirable to carries out operation of taking the logarithm to image, allows the property taken advantage of to make an uproar
Sound becomes additive noise, is so conducive to the realization of algorithm, carries out operation of taking the logarithm to this width SAR image, both：Log (f)=
Log (g)+log (z), wherein, log () represents operation of taking the logarithm to image, and g represents the image for not having influence of noise, and z represents scattered
Speckle noise
3 pairs of images taken after logarithm were both：Log (f) carries out shearing wave conversion in order to obtain shearing wave coefficient：
Sj,l,x,y=Xj,l,x,y+Zj,l,x,y, wherein Sj,l,x,y,Xj,l,x,y,Zj,l,x,yLog (f), log (x), log are represented respectively
The shearing wave coefficient of (z)；J, l represent the scale parameter in shearing wave coefficient, directioin parameter respectively；X, y represent the space of image
Coordinate.
The shearing wave coefficient S of 4 pairs of acquisitionsj,l,x,yA hard -threshold operation is carried out, the shearing wave coefficient of noise is set to into 0,
Retain other parts.
Here hard -threshold operates and is：
δ=Tscalars (i) * σ * dst_scalars (j)
I=1,2,3.j=1,2,3,4. shink represents the threshold value of shearing wave coefficient here, and Tscalars is represented to shearing
The direction set direction of wave system matrix number, dst_scalars (j) represent the set direction of shearing wave coefficient matrix.
Here it is four layers that we select shearing wave, three directions.Here the threshold method that we select is according to noise
Variance is converting
5 pairs of parts retained after carrying out hard -threshold operation carry out shearing wave inverse transformation, reach filtering purpose.
Image after 6 output filtering.
For the quality of method, herein cited Y-PSNR (PSNR), noise vs' degree (CNR), equivalent number (ENL)
And edge tetra- values of conservation degree X are illustrating the quality of algorithm, and with current very effective wavelet transformation and warp wavelet
Filtering method is compared.The concrete formula for providing this four values is as follows：
This three class value is bigger, shows that the filter effect of the method is better.
Experimental data is given below：
And in two forms below, we provide Y-PSNR (PSNR), and noise vs' degree (CNR) is equivalent to regard
The comparative result of three kinds of methods of three values of number (ENL).Either from visual effect still from the angle of quantitative analyses, the present invention
Method will be outstanding in small echo and the filtering method of warp wavelet.
For the quantitative analyses of the filter result of satellite-borne SAR image
Although above in conjunction with diagram, invention has been described, the invention is not limited in above-mentioned being embodied as
Mode, above-mentioned specific embodiment are only schematic rather than restricted, and one of ordinary skill in the art is at this
Under the enlightenment of invention, without deviating from the spirit of the invention, many variations can also be made, these belong to the present invention's
Within protection.
Claims (2)
1. a kind of speckle noise filtering method of diameter radar image, is characterized in that, step is as follows：
Step 1：It is input into a width diameter radar image f；
Step 2：Operation of taking the logarithm is carried out to this width coherent tomographic image, both：Log (f)=log (x)+log (z), wherein, log
() represents operation of taking the logarithm to image, and x represents the image for not having influence of noise, and z represents speckle noise；
Step 3：To both having taken the image after logarithm：Log (f) carries out shearing wave conversion in order to obtain shearing wave coefficient：
Sj,l,x,y=Xj,l,x,y+Zj,l,x,y, wherein Sj,l,x,y,Xj,l,x,y,Zj,l,x,yLog (f), log (x), log (z) are represented respectively
Shearing wave coefficient；J, l represent the scale parameter in shearing wave coefficient, directioin parameter respectively；X, y represent that the space of image is sat
Mark.
Step 4：To the shearing wave coefficient S for obtainingj,l,x,yA hard -threshold operation is carried out, the shearing wave coefficient of noise is set to into 0,
Retain other parts；
Here hard -threshold operates and is：
δ=Tscalars (i) * σ * dst_scalars (j)
I=1,2,3.j=1,2,3,4
Step 5：The part retained after to carrying out hard -threshold operation carries out shearing wave inverse transformation, reaches filtering purpose；
Step 6：Image after output filtering.
2. the speckle noise filtering method of diameter radar image as claimed in claim 1, is characterized in that, select shearing wave
For four layers, three directions.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110058241A (en) * | 2019-04-09 | 2019-07-26 | 天津大学 | Shearing wave method for weather radar image diametral interference Echo cancellation |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2432984A (en) * | 1978-05-31 | 2007-06-06 | British Aerospace | Distinguishing signals from noise |
| CN103077508A (en) * | 2013-01-25 | 2013-05-01 | 西安电子科技大学 | Transform domain non local and minimum mean square error-based SAR (Synthetic Aperture Radar) image denoising method |
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2016
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2432984A (en) * | 1978-05-31 | 2007-06-06 | British Aerospace | Distinguishing signals from noise |
| CN103077508A (en) * | 2013-01-25 | 2013-05-01 | 西安电子科技大学 | Transform domain non local and minimum mean square error-based SAR (Synthetic Aperture Radar) image denoising method |
Non-Patent Citations (2)
| Title |
|---|
| DEEP GUPTA等: "Enhancement of Medical Ultrasound Images Using Multiscale Discrete Shearlet Transform Based", 《 2012 INTERNATIONAL SYMPOSIUM ON ELECTRONIC SYSTEM DESIGN》 * |
| SHUAIQI LIU等: "Synthetic aperture radar image de-noising based on Shearlet transform using the context-based model", 《PHYSICAL COMMUNICATION》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110058241A (en) * | 2019-04-09 | 2019-07-26 | 天津大学 | Shearing wave method for weather radar image diametral interference Echo cancellation |
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