CN111583267A - Generalized fuzzy C-means clustering-based fast SAR image sidelobe suppression method - Google Patents
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Abstract
A side lobe suppression method of a rapid SAR image based on generalized fuzzy C-means clustering comprises the following steps: determining the oversampling rate of the SAR image, determining the total number of distance-direction and azimuth-direction image pixel points, and calculating the parameter threshold of the side lobe suppression weighting window of the SAR image; based on a generalized fuzzy C-means clustering algorithm, carrying out self-adaptive image segmentation on an area with overhigh side lobe in the SAR image; calculating a sidelobe suppression balance operator and a sidelobe suppression weighting parameter, and calculating a segmented image after sidelobe suppression in the strong scattering area; updating a subspace image block clustering center; updating a fuzzy clustering membership function; calculating a fuzzy clustering objective function JnWhen | | Jn+1‑JnWhen | | | is less than or equal to the threshold value, iteration is finished, and an SAR image subjected to side lobe suppression is obtained; otherwise, n is made to be n +1, and the sidelobe suppression balance operator and the sidelobe suppression weighting parameter are returned to be calculated to continue execution. The invention avoids processing the whole SAR image, andand through dimension reduction processing, the calculated amount is obviously reduced, the processing time is effectively improved, and the engineering requirements are met.
Description
Technical Field
The invention relates to the technical field of radar imaging, in particular to a method for quickly suppressing side lobes of an SAR image based on generalized fuzzy C-means clustering.
Background
Synthetic Aperture Radars (SAR) are widely used in remote sensing imaging and other fields due to their advantages of imaging all day long, all weather, and high penetration. Because the SAR image is a matched filter obtained on the basis of the principle of outputting the maximum signal-to-noise ratio, but the radar signal is limited in a distance direction and an azimuth direction support domain, the obtained SAR fuzzy function is in a sinc form, and the ratio of image resolution to peak sidelobe is taken as two important indexes for evaluating the characteristic, in actual imaging, particularly near strong radar scattering cross-section targets such as urban buildings, the image quality is reduced due to excessively high sidelobe, the image contrast is reduced, and the small targets in a scene are submerged due to excessively high sidelobe, so that the rapid and effective reduction of the SAR image sidelobe has important significance for the application of a radar system.
The existing methods for suppressing side lobes mainly comprise the following methods: one is based on the window function theory, but the method can cause the main lobe of the image to be widened and reduce the resolution of the image; the other method is based on a spectrum expansion theory, such as an Autoregressive (AR) method and an sva (spatial variant adaptation) method, but the methods still have residual side lobes, and for a distributed target, the residual side lobes still overlap, and in addition, the methods cannot adaptively process the vicinity of a strong scattering point in an image, so that the real-time performance of engineering is poor for an SAR image with a huge data volume; still another is based on the spectral analysis theory, for example, methods such as MUSIC, ESPRIT, etc. based on subspace, but these methods rely heavily on the signal hypothesis test model, and the calculated amount is large, and it is difficult to meet the engineering requirements, and in addition, because the SAR image data volume is large, the existing methods all process the whole image, and only need to restrain the strong scattering point with too high side lobe in the actual engineering, and then improve the operation and reduce, therefore, the existing methods are difficult to meet the engineering requirements.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide a method for fast SAR image sidelobe suppression based on generalized fuzzy C-means clustering, so as to partially solve at least one of the above technical problems.
In order to achieve the above object, as an aspect of the present invention, a method for fast suppressing side lobe of an SAR image based on generalized fuzzy C-means clustering is provided, which includes the following steps:
step 1: determining the oversampling rate of the SAR image, determining the total number of distance-direction and azimuth-direction image pixel points, and calculating the parameter threshold of the side lobe suppression weighting window of the SAR image;
step 2: based on a generalized fuzzy C-means clustering algorithm, carrying out self-adaptive image segmentation on an area with overhigh side lobe in the SAR image;
and step 3: calculating a sidelobe suppression balance operator and a sidelobe suppression weighting parameter, and calculating a segmented image after sidelobe suppression in the strong scattering area;
and 4, step 4: updating a subspace image block clustering center;
and 5: updating a fuzzy clustering membership function;
step 6: calculating a fuzzy clustering objective function JnAnd making judgment, when | | Jn+1-JnWhen | | | is less than or equal to the threshold value, iteration is finished, and an SAR image subjected to side lobe suppression is obtained; otherwise, let n be n +1, and return to the step 3 to continue execution, where it is an iteration termination parameter.
Based on the technical scheme, compared with the prior art, the fast SAR image sidelobe suppression method based on the generalized fuzzy C-means clustering has at least one of the following beneficial effects:
(1) the SAR image target identification method based on generalized fuzzy C-means clustering can realize self-adaptive segmentation of the SAR image, performs side lobe suppression weighting processing on a strong scattering point region with overhigh side lobe in the image, effectively suppresses the side lobe of the SAR target, can effectively suppress the side lobe far away from the main lobe, improves the SAR image target identification performance, reduces the main lobe width on the premise of not losing the main lobe energy through a balance operator, and improves the image resolution of the SAR image.
(2) The method avoids processing the whole SAR image, remarkably reduces the calculated amount through dimension reduction processing, effectively improves the processing time and meets the engineering requirement.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is an onboard SAR raw image;
fig. 3 is an image of an airborne SAR processed by the method of the present invention.
Detailed Description
The invention relates to the technical field of radar imaging, in particular to a method for quickly suppressing side lobes of an SAR image based on generalized fuzzy C-means clustering. The SAR image self-adaptive segmentation method is based on generalized fuzzy C-means clustering, self-adaptive segmentation can be realized on the SAR image, side lobe suppression weighting processing is carried out on a strong scattering point area with overhigh side lobe in the image, the side lobe of the SAR target is effectively suppressed, the target identification performance of the SAR image is improved, the width of the main lobe is reduced on the premise that the energy of the main lobe is not lost through a balance operator, and the image resolution of the SAR image is improved. The method avoids processing the whole SAR image, remarkably reduces the calculated amount through dimension reduction processing, effectively improves the processing time and meets the engineering requirement.
In order that the objects, technical solutions and advantages of the present invention will become more apparent, the present invention will be further described in detail with reference to the accompanying drawings in conjunction with the following specific embodiments.
The SAR image parameters in this example are as follows:
the SAR original image obtained by adopting a single-channel C-band strip SAR system and a radar working frequency of 5.4GHz by using an imaging algorithm as a Range Doppler (RD) algorithm is shown in figure 2, wherein the radar Range over-sampling rate is 2.32, and the azimuth over-sampling rate is 2.54.
Specifically, according to the flow chart of the method of the present invention shown in fig. 1, the method of the present invention comprises the following steps:
step 1: according to the radar system parameters, the over-sampling rate parameters of the SAR image in the distance direction and the azimuth direction are respectively fsr＝2.32、fsa2.54, and determining the total number of distance and orientation image pixel points, which are respectively marked as Nr＝4096、Na4096, I (I, j) represents the grayscale value of the ith row and the jth column of the SAR image. Calculating a parameter threshold of a side lobe suppression weighting window of the SAR image, wherein the calculation expressions are respectively as follows:
wherein
Step 2: and preprocessing the SAR image, namely performing self-adaptive image segmentation on the region with the overhigh side lobe in the SAR image based on a generalized fuzzy C-means clustering algorithm.
Step 2-1: setting the number M of the generalized fuzzy C mean clustering subspace as 600, setting the weighting index W as 3, setting the iteration termination parameter as 0.0001, and setting the iteration statistical number n.
Step 2-2: setting fuzzy clustering subspace distance correlation degree rhosSetting fuzzy clustering subspace gray scale correlation degree rho as 5.5gAnd 4.5, defining a clustering weighted image omega according to the clustering subspace distance correlation degree and the clustering subspace gray scale correlation degree in the SAR image.
The calculation formula of the clustering weighted image omega in the step 2-2 is as follows:
wherein
Representing distance characteristics within the fuzzy clustering subspace, and
representing a gray scale characteristic within the fuzzy clustering subspace, and
Kxand representing the length of the fuzzy clustering subspace, wherein x represents a central point pixel of the fuzzy clustering subspace, and y represents any point pixel in the fuzzy clustering subspace.
And step 3: calculating sidelobe suppression balance operators psi, psixThe method can be used for representing that the xth pixel point in the SAR image needs to be subjected to side lobeAnd calculating the suppressed intensity of the segmented image W (i, j) after sidelobe suppression in the strong scattering area according to the balance operator psi and the sidelobe suppression weighting parameter.
Step 3-1: the sidelobe suppression balance operator psi calculation formula is as follows:
in the formula wxy(wxyLess than or equal to 1) representing the similarity between the pixel points in the fuzzy clustering subspace, wherein the size depends on the pixel at the central point of the fuzzy clustering subspace and any pixel point in the fuzzy clustering subspace, and wxyThe calculation formula of (a) is as follows:
wherein P (x) and P (y) respectively represent subspace image blocks with pixel points x and y as centers, the size of the subspace image block is (2l +1) × (2l +1), l is an empirical value of 10, | | Gσ*[P(x)-P(y)]||2Characterizing the gray level difference and spatial distance characteristics, G, of two of said sub-space imagesσA gaussian window representing a gaussian variance σ of 5.
Step 3-2: calculating a side lobe suppression weighting window parameter gamma (x) of the SAR image, wherein | gamma (x) | ≦ 0.7171, and the calculation formula of the gamma (x) is as follows:
step 3-3: calculating the segmented image W after sidelobe suppression in the strong scattering area according to the balance operator psi and the sidelobe suppression weighting parameterx，WxThe calculation formula of (a) is as follows:
the larger the balance operator psi is, the larger the gray level difference of the sub-space image block is, the image block needs to be subjected to side lobe suppression, the smaller the balance operator psi is, the smaller the gray level difference of the sub-space image block is, the image has no strong scattering point, side lobe suppression is not needed, the size of pixels of an original image block is kept, self-adaption judgment whether side lobe suppression needs to be carried out or not is achieved through the balance operator psi, and therefore the automatic judgment effect is achieved, and the purpose of reducing the calculated amount is achieved.
And 4, step 4: updating the clustering center in the nth iteration of the subspace image block
The image block clustering center
The calculation formula of (a) is as follows:
wherein T represents the number of gray levels in the SAR image, and T is 255, sLRepresenting the number of pixels with the gray level of L in the current iteration process, wherein L is more than or equal to 0 and less than or equal to T, UnAnd representing a fuzzy clustering membership function in the nth iteration of the subspace image block, wherein the initialization value of the fuzzy clustering membership function is 0.
And 5: updating a fuzzy clustering membership function U in the nth iteration of the subspace image blockn。
The fuzzy clustering membership function UnThe calculation formula of (a) is as follows:
step 6: calculating fuzzy clustering objective function J in nth iterationnAnd making judgment, when | | Jn+1-JnAnd if | | is less than or equal to the preset value, ending the iteration, otherwise, enabling n to be n +1, and returning to the step 3 to continue executing.
Fuzzy clustering objective function J in the nth iterationnMeter (2)The calculation formula is as follows:
thus, a side lobe suppressed SAR image is obtained, as shown in fig. 3.
The SAR image self-adaptive segmentation method is based on generalized fuzzy C-means clustering, self-adaptive segmentation can be realized on the SAR image, side lobe suppression weighting processing is carried out on a strong scattering point area with overhigh side lobe in the image, the side lobe of the SAR target is effectively suppressed, the target identification performance of the SAR image is improved, the width of the main lobe is reduced on the premise that the energy of the main lobe is not lost through a balance operator, and the image resolution of the SAR image is improved.
The method avoids processing the whole SAR image, remarkably reduces the calculated amount through dimension reduction processing, effectively improves the processing time and meets the engineering requirement.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A side lobe suppression method of a rapid SAR image based on generalized fuzzy C-means clustering is characterized by comprising the following steps:
step 1: determining the oversampling rate of the SAR image, determining the total number of distance-direction and azimuth-direction image pixel points, and calculating the parameter threshold of the side lobe suppression weighting window of the SAR image;
step 2: based on a generalized fuzzy C-means clustering algorithm, carrying out self-adaptive image segmentation on an area with overhigh side lobe in the SAR image;
and step 3: calculating a sidelobe suppression balance operator and a sidelobe suppression weighting parameter, and calculating a segmented image after sidelobe suppression in the strong scattering area;
and 4, step 4: updating a subspace image block clustering center;
and 5: updating a fuzzy clustering membership function;
step 6: calculating a fuzzy clustering objective function JnAnd making judgment, when | | Jn+1-JnWhen | | | is less than or equal to the threshold value, iteration is finished, and an SAR image subjected to side lobe suppression is obtained; otherwise, let n be n +1, and return to the step 3 to continue execution, where it is an iteration termination parameter.
2. The sidelobe suppression method according to claim 1, wherein in the step 1, the threshold of the weighting window parameter for the sidelobe suppression of the SAR image is calculated by the following expressions:
wherein,
fsis the oversampling rate of the SAR image.
3. The sidelobe suppression method according to claim 1, characterized in that said step 2 specifically includes the substeps of:
substep 2-1: setting the number M of the generalized fuzzy C-means clustering subspaces, setting a weighting index W, setting an iteration termination parameter and setting an iteration statistical number n;
substep 2-2: setting fuzzy clustering subspace distance correlation degree rhosSetting the grey level correlation degree rho of the fuzzy clustering subspacegAnd defining a clustering weighted image omega according to the clustering subspace distance correlation degree and the clustering subspace gray scale correlation degree in the SAR image.
4. The sidelobe suppression method according to claim 3, wherein a calculation formula of the cluster weighting pattern Ω in said substep 2-2 is:
wherein I (I, j) represents the gray value of the ith row and the jth column of the SAR image,
representing distance characteristics within the fuzzy clustering subspace, and
representing a gray scale characteristic within the fuzzy clustering subspace, and
Kxand representing the length of the fuzzy clustering subspace, wherein x represents a central point pixel of the fuzzy clustering subspace, and y represents any point pixel in the fuzzy clustering subspace.
5. The sidelobe suppression method according to claim 1, wherein said sidelobe suppression balance operator ψ calculation formula in step 3 is as follows:
wherein, wxy(wxyLess than or equal to 1) representing the similarity between the pixel points in the fuzzy clustering subspace, wherein the size depends on the pixel at the central point of the fuzzy clustering subspace and any pixel point in the fuzzy clustering subspace, and wxyThe calculation formula of (a) is as follows:
wherein, P (x), P (y) respectively represent subspace image blocks with pixel points x and y as centers, the subspace image blocksThe image block size is (2l +1) × (2l +1), l is the empirical value of 10, | Gσ*[P(x)-P(y)]||2Characterizing the gray level difference and spatial distance characteristics, G, of two of said sub-space imagesσRepresenting the variance of Gauss
The gaussian window of (a).
6. The sidelobe suppression method according to claim 1, wherein said sidelobe suppression weighting window parameter γ (x) in said step 3 is calculated as follows:
wherein | gamma (x) | is less than or equal to gammamax。
7. The sidelobe suppression method according to claim 1, wherein the divided image W after sidelobe suppression in the strong scattering area is calculated based on the balance operator ψ and the sidelobe suppression weighting parameterx，WxThe calculation formula of (a) is as follows:
the larger the balance operator psi is, the larger the gray level difference of the sub-space image block is, the image block needs to be subjected to side lobe suppression, the smaller the balance operator psi is, the smaller the gray level difference of the sub-space image block is, the image has no strong scattering point, side lobe suppression is not needed, the size of pixels of an original image block is kept, self-adaptive judgment whether the side lobe suppression needs to be carried out or not is achieved through the balance operator psi, and therefore the automatic judgment effect is achieved, and the purpose of reducing the calculated amount is achieved.
8. The sidelobe suppression method according to claim 1, wherein the clustering center of the image blocks in said step 4
The calculation formula of (a) is as follows:
wherein T represents the number of gray levels in the SAR image, and T is 255, sLRepresenting the number of pixels with the gray level of L in the current iteration process, wherein L is more than or equal to 0 and less than or equal to T, UnAnd representing a fuzzy clustering membership function in the nth iteration of the subspace image block, wherein the initialization value of the fuzzy clustering membership function is 0.
9. The sidelobe suppression method according to claim 1, characterized in that said fuzzy clustering membership function U in step 5nThe calculation formula of (a) is as follows:
10. the sidelobe suppression method according to claim 1, characterized in that said fuzzy clustering objective function J in step 6nThe calculation formula of (a) is as follows:
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