CN108062745A - A kind of method for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms - Google Patents
A kind of method for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms Download PDFInfo
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- CN108062745A CN108062745A CN201610982130.9A CN201610982130A CN108062745A CN 108062745 A CN108062745 A CN 108062745A CN 201610982130 A CN201610982130 A CN 201610982130A CN 108062745 A CN108062745 A CN 108062745A
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- 238000003706 image smoothing Methods 0.000 abstract 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
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- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
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Abstract
The present invention proposes a kind of method for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms, by setting Gaussian kernel, it determines Gaussian filter, determines grey level's changing value of each pixel in SAR image, determine the Differential Characteristics value of pixel, SAR image is handled using total variation model, obtain the SAR image of resolving power enhancing.The present invention carries out noise restraint using weight to SAR image, applies big weight in image smoothing region to inhibit noise, the noise level of radar image is reduced；And edge region, applying small weight is used for keeping image border and detailed information so that details enhances, and image border profile becomes apparent from, and is kept so as to take into account noise suppressed with details.
Description
Technical field
The present invention relates to a kind of methods for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms, belong to accurate
Technical field of guidance.
Background technology
SAR imagings extensively should with the incomparable round-the-clock of optics (infrared, visible ray), round-the-clock imaging advantage
For the platforms such as airborne, spaceborne, aircraft.Due to being limited to space and the load of some aircraft platforms, antenna size one
As it is smaller, make its full aperture azimuth resolution far above matching reference chart.In practical applications, when target is located at aircraft flight
The front in direction, this requires radar is imaged under big preceding tiltedly pattern.Since Sea background or land background clutter all compare
Complexity, it is mixed in together with target echo by clutter, it results in target imaging result and obscures.In radar imagery last handling process
In, when further extracting target signature, image restoration and super-resolution rebuilding are the difficult points in signal processing.How in hardware item
Under part limitation, the spatial resolution of SAR image is further improved, causes the concern of more researcher.Common SAR image enhancing
Algorithm iteration number is excessive, and computation complexity is higher, and cannot realize image enhancement faster.
The content of the invention
It is an object of the invention to overcome the shortage of prior art, provide a kind of better simply, very fast realization aircraft and put down
The Enhancement Method of platform SAR image spatial resolving power.
The technical solution of the present invention：A kind of side for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms
Method is realized by following steps：
The first step sets Gaussian kernel, and determines the Gaussian filter based on Hessian matrixes, and to SAR original images
It is filtered；
This step is the prior art, and Gaussian kernel setting specifically refers to Zhang Xiaoyun, Liu Yun and just writes《Gaussian kernel support vector machines
Performance evaluation》(computer engineering 29 (8):2003), Gaussian filter determines specifically to refer to Ou Renxia, Chen Hongbin, Bao Jiezhu《It is high
This filter characteristic is analyzed and application study》.
Second step, determine each pixel variable u in SAR image after first step filtering process details enhancing and
The weight w (u (x, y)) of noise suppressed,
A2.1, the gray scale water that each pixel variable u and its coordinate u (x, y) in SAR image are obtained using formula (1)
Flat changing value ζ (x, y),
Wherein ki×kjIt is the region centered on coordinate u (x, y), ki、kjTo be more than 2 natural number, value is according to figure
The required precision of picture determines that required precision is higher, and value is bigger, and calculation amount also accordingly increases, this field can be according to essence
The requirement of degree and calculating speed, makes choice.
U (x+i, y+j) is the coordinate of the pixel on pixel variable u peripheries；
A2.2, obtained using formula (2) pixel variable u details enhancing and noise suppressed weight w (u (x,
Y)),
Wherein min (ζ) and max (ζ) be the grey level changing value ζ (x, y) of all pixels variable in SAR image most
Small value and maximum；
3rd step, the details of each pixel variable u in SAR image is obtained according to second step to be enhanced and noise suppressed
Weight obtains the Differential Characteristics value D (x, y) of pixel variable u using formula (3),
D (x, y)=(λ1-λ2)λ1w(u(x,y)) (3)
Wherein λ1And λ2It is the corresponding pixel maximums of pixel variable u and minimum value in the SAR image after filtering process；
4th step is handled the SAR image after first step filtering process using the total variation model of formula (4),
The SAR image of resolving power enhancing is obtained,
Wherein θ is reduced factor,Expression seeks gradient to the coordinate u (x, y) of each pixel variable.
The value range of reduced factor θ is 0~1, and value influences SAR image resolution ratio less, to reflect SAR image
Comparison degree, θ values are bigger, and the comparison after processing in image between different things is bigger, but influence the details of independent object
Display；Those skilled in the art require according to having for SAR image after processing, comparative selection factor θ, and optimum valuing range is
0.4~0.7.
For the pixel of smooth region, since D (x, y) is close to zero,Level off to 1, it means that one
Big total variation regularization is reinforced, and noise is suppressed；For edge and details pixel, since D (x, y) is very big, andVery little, this will weaken the intensity of total variation regularization term, and grain details will be maintained.
The advantageous effect of the present invention compared with prior art：
(1) present invention takes into account noise suppressed and edge is kept, and the weight of details of use enhancing and noise suppressed is distinguished
Flat site and marginal texture information in image, improve radar image resolution ratio；
(2) present invention carries out noise restraint using weight to SAR image, applies big weights in image smoothing region
The factor reduces the noise level of radar image to inhibit noise；And edge region, apply small weight and be used for protecting
Hold image border and detailed information so that details enhances, and image border profile becomes apparent from, so as to take into account noise suppressed and details
It keeps；
(3) calculation of the present invention is simple, quick, can comparatively fast realize that resolution ratio enhances.
Description of the drawings
Fig. 1 is original image of the embodiment of the present invention；
Fig. 2 is the image handled through the method for the present invention；
Fig. 3 is flow chart of the present invention.
Specific embodiment
With reference to specific example and attached drawing, the present invention is described in detail.
The present invention by following steps as shown in figure 3, realized：
1st, Gaussian kernel is set, and determines the Gaussian filter based on Hessian matrixes, SAR original images are filtered
Processing.
Gaussian kernel size is set as 5*5, variable 8.0, and combined to obtain Gaussian filter with pixel point coordinates.
2nd, determine each pixel u in the SAR image of filtering details enhancing and noise suppressed weight w (u (x,
y))。
1) for balance quality and calculating speed, ki×kj3*3 scopes are taken, formula (1) is rewritten as formula (1-1) obtains
Each pixel u grey level changing value ζ (x, y) in SAR image,
2) the details enhancing of pixel u and the weight w (u (x, y)) of noise suppressed are obtained using formula (2),
Wherein min (ζ) and max (ζ) is the minimum and maximum value of the corresponding grey level's changing value ζ (x, y) of pixel u.
3rd, the Differential Characteristics value D (x, y) of pixel u is obtained using formula (3),
D (x, y)=(λ1-λ2)λ1w(u(x,y)) (3)
4th, the SAR image of filtered processing is handled using the total variation model of formula (4), obtains resolving power enhancing
SAR image,
Wherein θ is reduced factor, and value is 0.7 in the present embodiment,Expression seeks gradient to u (x, y).
According to above-mentioned steps, the original image of Fig. 1 is handled, obtains result as shown in Figure 2.It is apparent from Fig. 2
As can be seen that picture noise is inhibited, details profile is also more obvious, and resolving power is improved.
Unspecified part of the present invention is known to the skilled person technology.
Claims (4)
- A kind of 1. method for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms, which is characterized in that pass through following step It is rapid to realize：The first step sets Gaussian kernel, determines the Gaussian filter based on Hessian matrixes, and SAR original images are filtered Processing；Second step determines details enhancing and the noise of each pixel variable u in SAR image after first step filtering process The weight w (u (x, y)) of inhibition,A2.1, the grey level for being obtained each pixel variable u in SAR image and its coordinate u (x, y) using formula (1) are become Change value ζ (x, y),<mrow> <mi>&zeta;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>&times;</mo> <msub> <mi>k</mi> <mi>j</mi> </msub> </mrow> </mfrac> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>-</mo> <mn>1</mn> </mrow> <mn>2</mn> </mfrac> </mrow> <mfrac> <mrow> <msub> <mi>k</mi> <mi>i</mi> </msub> <mo>-</mo> <mn>1</mn> </mrow> <mn>2</mn> </mfrac> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>-</mo> <mn>1</mn> </mrow> <mn>2</mn> </mfrac> </mrow> <mfrac> <mrow> <msub> <mi>k</mi> <mi>j</mi> </msub> <mo>-</mo> <mn>1</mn> </mrow> <mn>2</mn> </mfrac> </munderover> <mo>&lsqb;</mo> <mi>u</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>+</mo> <mi>i</mi> <mo>,</mo> <mi>y</mi> <mo>+</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>u</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>Wherein ki×kjIt is the region centered on coordinate u (x, y), ki、kjTo be more than 2 natural number, u (x+i, y+j) is pixel The coordinate of the pixel on variable u peripheries；A2.2, the details enhancing of pixel variable u and the weight w (u (x, y)) of noise suppressed are obtained using formula (2),<mrow> <mi>w</mi> <mrow> <mo>(</mo> <mi>u</mi> <mo>(</mo> <mrow> <mi>x</mi> <mo>,</mo> <mi>y</mi> </mrow> <mo>)</mo> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>&zeta;</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>min</mi> <mrow> <mo>(</mo> <mi>&zeta;</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <mi>&zeta;</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>min</mi> <mrow> <mo>(</mo> <mi>&zeta;</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>Wherein min (ζ) and max (ζ) be the grey level changing value ζ (x, y) of all pixels variable in SAR image minimum value and Maximum；3rd step obtains the details enhancing of each pixel variable u in SAR image and the weights of noise suppressed according to second step The factor obtains the Differential Characteristics value D (x, y) of pixel variable u using formula (3),D (x, y)=(λ1-λ2)λ1w(u(x,y)) (3)Wherein λ1And λ2It is the corresponding pixel maximums of pixel variable u and minimum value in the SAR image after filtering process；4th step handles the SAR image after first step filtering process using the total variation model of formula (4), obtains The SAR image of resolving power enhancing,<mrow> <mi>S</mi> <mi>A</mi> <mi>T</mi> <mi>V</mi> <mo>=</mo> <msub> <mo>&Integral;</mo> <mi>&Omega;</mi> </msub> <mfrac> <mn>1</mn> <mrow> <mn>1</mn> <mo>+</mo> <mi>&theta;</mi> <mi>D</mi> <mrow> <mo>(</mo> <mi>x</mi> <mo>,</mo> <mi>y</mi> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>|</mo> <mo>&dtri;</mo> <mi>u</mi> <mo>|</mo> <mi>d</mi> <mi>x</mi> <mi>d</mi> <mi>y</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>Wherein θ is reduced factor,Expression seeks gradient to the coordinate u (x, y) of each pixel variable.
- 2. a kind of method for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms according to claim 1, It is characterized in that：The value range of reduced factor θ is 0~1 in 4th step.
- 3. a kind of side for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms according to claim 1 or 2 Method, it is characterised in that：Reduced factor θ value ranges are 0.4~0.7 in 4th step.
- 4. a kind of method for enhancing the big preceding tiltedly SAR image spatial resolving power of aircraft platforms according to claim 1, It is characterized in that：K in the step A2.1i×kjFor 3 × 3.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS6280767A (en) * | 1985-10-04 | 1987-04-14 | Hitachi Ltd | Reproducing processing system for synthetic aperture radar image |
| US20100141508A1 (en) * | 2008-12-10 | 2010-06-10 | Us Government As Represented By The Secretary Of The Army | Method and system for forming an image with enhanced contrast and/or reduced noise |
| CN102073992A (en) * | 2010-12-09 | 2011-05-25 | 国网电力科学研究院 | High-resolution SAR satellite image speckle de-noising method |
| CN102903080A (en) * | 2012-09-06 | 2013-01-30 | 西安工程大学 | Non-supervision estimation method on speckle noise suppression performance of synthetic aperture radar image |
| 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 |
| CN105957030A (en) * | 2016-04-26 | 2016-09-21 | 成都市晶林科技有限公司 | Infrared thermal imaging system image detail enhancing and noise inhibiting method |
-
2016
- 2016-11-08 CN CN201610982130.9A patent/CN108062745B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS6280767A (en) * | 1985-10-04 | 1987-04-14 | Hitachi Ltd | Reproducing processing system for synthetic aperture radar image |
| US20100141508A1 (en) * | 2008-12-10 | 2010-06-10 | Us Government As Represented By The Secretary Of The Army | Method and system for forming an image with enhanced contrast and/or reduced noise |
| CN102073992A (en) * | 2010-12-09 | 2011-05-25 | 国网电力科学研究院 | High-resolution SAR satellite image speckle de-noising method |
| CN102903080A (en) * | 2012-09-06 | 2013-01-30 | 西安工程大学 | Non-supervision estimation method on speckle noise suppression performance of synthetic aperture radar image |
| 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 |
| CN105957030A (en) * | 2016-04-26 | 2016-09-21 | 成都市晶林科技有限公司 | Infrared thermal imaging system image detail enhancing and noise inhibiting method |
Non-Patent Citations (7)
| Title |
|---|
| POOJA H.M 等: "Change Detection in Synthetic Aperture Radar Image based on Image Fusion and Fuzzy Clustering", 《INTERNATIONAL JOURNAL OF SCIENCE AND RESEARCH (IJSR)》 * |
| YAO ZHAO等: "AN ADAPTIVE TOTAL VARIATION REGULARIZATION METHOD FOR SAR IMAGE DESPECKLING", 《IEEE》 * |
| 吴涛等: "一种基于局域自适应处理的SAR图像降斑算法", 《系统工程与电子技术》 * |
| 周蓉蓉等: "一种基于小波变换的SAR图像斑点噪声抑制方法", 《装备指挥技术学院学报》 * |
| 张旭等: "基于自适应收缩因子的SAR图像去噪", 《武汉大学学报》 * |
| 杨婧玮等: "改进Frost算子在SAR图像斑点噪声抑制中的应用", 《测绘科学技术学报》 * |
| 沈琪琪等: "图像局部纹理特征自适应超分辨率重建", 《光电技术应用》 * |
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