CN114677290A - Processing method and device for suppressing scallops in SAR image - Google Patents
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Abstract
The embodiment of the invention relates to the technical field of image processing, in particular to a processing method and a processing device for suppressing scallops in an SAR image. The method comprises the following steps: obtaining a first subimage and a second subimage by performing threshold segmentation on the SAR image to be processed; the pixel value of each pixel point in the first sub-image is greater than a preset threshold value, and the pixel value of each pixel point in the second sub-image is not greater than the preset threshold value; then partitioning the second sub-image in the distance direction to obtain at least two image blocks; secondly, performing Kalman filtering processing on all image blocks included in the second sub-image, and combining all the image blocks subjected to the Kalman filtering processing to obtain a target sub-image; and finally, merging the first sub-image and the target sub-image to obtain a target SAR image. The SAR image processing method can effectively inhibit the scallop effect in the image and improve the quality of the image.
Description
Technical Field
The embodiment of the invention relates to the technical field of image processing, in particular to a processing method and a processing device for restraining scallops in an SAR image.
Background
As a working mode of the SAR system, a satellite-borne ScanSAR (scanning synthetic aperture radar) can realize observation of a wide observation area by changing an observation angle in a wide angle range. The working mode is high in imaging efficiency, and has very important application significance especially for large-scale observation areas with fast change, such as the fields of crop growth change detection, flood disaster prevention and control and the like.
In the SAR working mode, because the antenna beam is discontinuous for scanning in the observation area, the transfer function of the SAR system changes with space and time, so that the SAR image after imaging processing has noise fringes with alternate bright and dark stripes, i.e. scallop effect. The scallop effect has great influence on the image quality, and the difficulty of subsequent application of the image is increased.
In the related art, although some methods have been available to suppress the scallop effect of the SAR image, the suppression effect is not good. Therefore, a new SAR image processing method is needed to solve the above problems.
Disclosure of Invention
In order to effectively suppress the scallop effect of the SAR image, the embodiment of the invention provides a processing method and a processing device for suppressing the scallop effect of the SAR image.
In a first aspect, an embodiment of the present invention provides a processing method for suppressing scallops in an SAR image, including:
carrying out threshold segmentation on the SAR image to be processed to obtain a first subimage and a second subimage; the pixel value of each pixel point in the first sub-image is greater than a preset threshold value, and the pixel value of each pixel point in the second sub-image is not greater than the preset threshold value;
partitioning the second sub-image in the distance direction to obtain at least two image blocks; wherein the distance direction is along the direction of radar wave emission;
performing Kalman filtering on all image blocks included in the second sub-image, and merging all image blocks subjected to Kalman filtering to obtain a target sub-image;
and merging the first sub-image and the target sub-image to obtain a target SAR image.
In one possible design, before performing threshold segmentation on the SAR image to be processed, the method includes:
preprocessing the SAR image to be processed to obtain a preprocessed SAR image;
classifying the preprocessed SAR images to obtain a first SAR image and a second SAR image; the first SAR image is an SAR image with uniform and homogeneous pixel values, and the second SAR image is an SAR image with a sea-land connection background.
In a possible design, the performing threshold segmentation on the SAR image to be processed to obtain a first sub-image and a second sub-image includes:
performing threshold segmentation on the first SAR image to obtain a first sub-image and a second sub-image of the first SAR image;
and performing threshold segmentation on the second SAR image to obtain a first sub-image and a second sub-image of the second SAR image.
In one possible design, after the performing threshold segmentation on the second SAR image to obtain a first sub-image and a second sub-image of the second SAR image, the method further includes:
and performing morphological segmentation on a second subimage of the second SAR image to obtain an ocean subimage and a land subimage.
In one possible design, the performing morphological segmentation on the second sub-image of the second SAR image to obtain an ocean sub-image and a land sub-image includes:
performing binarization processing on a second subimage of the second SAR image to obtain a first binarized image containing an ocean area and a land area; wherein the pixel of the ocean area is 0, and the pixel of the land area is 1;
sequentially performing hole filling operation, object deleting operation with the area smaller than a preset area and closing operation on the binary image to be processed to obtain a second binary image;
Keeping the pixel at the first position in a second subimage of the second SAR image unchanged and setting the pixel at the second position to be 0 to obtain an ocean subimage; wherein the first position is the same as the position where the pixel in the second binarized image is 0, and the second position is the same as the position where the pixel in the second binarized image is 1;
setting the pixel at the first position in the second sub-image of the second SAR image as 0 and keeping the pixel at the second position unchanged to obtain a land sub-image; the first position is the same as the position of a pixel of 0 in the second binarized image, and the second position is the same as the position of a pixel of 1 in the second binarized image.
In one possible design, before performing kalman filtering on all image blocks included in the second sub-image, the method includes:
constructing an additive model for estimating the scallop intensity: sc(r，x)＝So(r，x)+o(x)；
In the formula, r and x respectively represent the positions of pixel points in the image block in the distance direction and the azimuth direction, and So(r, x) is a scallop noise-free image of a pixel point with the distance position r and the azimuth position x, Sc(r, x) is a scallop noise image of the pixel point with the distance position r and the position x, and o (x) is the scallop intensity of the pixel point with the position x;
Constructing a Kalman filtering model suitable for the additive model, wherein a prediction equation of the Kalman filtering model is a formula set as follows:
in the formula (I), the compound is shown in the specification,
is an a priori estimate of the ith time,
the error covariance of the ith priori estimation is obtained, and Q is the covariance of system noise;
the corresponding state update equation is:
in the formula, KiIn order to be the basis of the kalman gain,
for the i-th a-posteriori estimation, i.e. the result of filtering, P, of each step of the estimationiIs the i-th posterior error covariance, R is the observation noise variance, ziIs the observed value of the ith time.
In one possible design, the kalman filtering all image blocks included in the second sub-image includes:
performing the following operations on each image block by using the additive model, the prediction equation set and the state updating equation set:
traversing each column of azimuth directions in the image block along azimuth directions in a preset window, and acquiring an average value u and a variance s of pixels in the preset window as the average value u and the variance s corresponding to the current azimuth direction.
Determining initial states of a posteriori estimates
Error covariance P of a posteriori estimation1And the noise covariance Q of the system; subtracting the average value u from the gray value of each non-0 pixel in the current azimuth direction of the image block to be used as an observed value z of a Kalman filter 1～zkWill do itThe variance s of the current azimuth direction is used as the observation noise R of the Kalman filter; wherein k is the number of non-0 pixels in the image block;
performing k iterations on the current azimuth direction based on the prediction equation set and the state updating equation set to obtain the k-th posterior estimation
Taking the scallop intensity as the scallop intensity o (x) of the current orientation of the image block;
based on the k-th posterior estimation
The additive model is used for realizing scallop inhibition in the current direction;
and traversing all azimuth directions of the image block, and repeating the processing steps to obtain the image block without scallop noise.
In a second aspect, an embodiment of the present invention further provides a processing apparatus for suppressing scallops in an SAR image, including:
the segmentation module is used for carrying out threshold segmentation on the SAR image to be processed to obtain a first subimage and a second subimage; the pixel value of each pixel point in the first sub-image is greater than a preset threshold value, and the pixel value of each pixel point in the second sub-image is not greater than the preset threshold value;
the partitioning module is used for partitioning the second sub-image in the distance direction to obtain at least two image blocks; wherein the distance direction is along the direction of radar wave emission;
The processing module is used for performing Kalman filtering processing on all image blocks included in the second sub-image and combining all image blocks subjected to Kalman filtering processing to obtain a target sub-image;
and the merging module is used for merging the first sub-image and the target sub-image to obtain a target SAR image.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes the computer program to implement the method according to any embodiment of this specification.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed in a computer, the computer program causes the computer to execute the method described in any embodiment of the present specification.
The embodiment of the invention provides a processing method and a processing device for suppressing scallops of an SAR (synthetic aperture radar) image, wherein a first sub-image and a second sub-image are obtained by performing threshold segmentation on the SAR image to be processed; then, the second sub-image is partitioned in the distance direction to obtain at least two image blocks; secondly, performing Kalman filtering processing on all image blocks included in the second sub-image, and combining all the image blocks subjected to the Kalman filtering processing to obtain a target sub-image; and finally, merging the first sub-image and the target sub-image to obtain a target SAR image, thereby effectively inhibiting the scallop effect of the SAR image and improving the quality of the SAR image.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a processing method for suppressing scallops in an SAR image according to an embodiment of the present invention;
FIG. 2 is a SAR image with a relatively smooth background dominated by land regions with strong scattering points provided by an embodiment of the present invention;
fig. 3(a) is a strong scattering point subgraph obtained by segmenting fig. 2 according to an embodiment of the present invention;
fig. 3(b) is a land area sub-map obtained by segmenting fig. 2 according to an embodiment of the present invention;
FIG. 4 is a graph of results of a prior art process of FIG. 2 provided by an embodiment of the present invention;
FIG. 5 is a graph illustrating the results of processing FIG. 2 using the method of the present invention, according to one embodiment of the present invention;
FIG. 6 is an SAR image of a sea-land connected background with scallop noise provided by an embodiment of the present invention;
Fig. 7(a) is a sub-graph of the strong scattering point obtained by segmenting fig. 6 according to an embodiment of the present invention;
fig. 7(b) is an ocean region subgraph obtained by segmenting fig. 6 according to an embodiment of the present invention;
FIG. 7(c) is a land area sub-map obtained by segmenting FIG. 6 according to an embodiment of the present invention;
FIG. 8 is a graph of the results of processing FIG. 6 using the prior art provided by an embodiment of the present invention;
FIG. 9 is a graph illustrating the results of processing of FIG. 6 using the method of the present invention, according to one embodiment of the present invention;
FIG. 10 is a graph comparing the cumulative curves of FIG. 2 before processing a portion of the area and after processing based on the method of the present invention, according to one embodiment of the present invention;
FIG. 11 is a graph comparing the cumulative curves of FIG. 6 before processing a portion of the area and after processing based on the method of the present invention, according to one embodiment of the present invention;
FIG. 12 is a diagram of a hardware architecture of an electronic device according to an embodiment of the present invention;
fig. 13 is a structural diagram of a processing device for suppressing scallop in an SAR image according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer and more complete, the technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some but not all embodiments of the present invention, and based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.
As described above, although the related art can already suppress the scallop effect of the SAR image, the suppression effect is not good, and scallop streaks still exist in the processed SAR image.
In order to solve the above technical problem, it may be considered to segment the SAR image, then block the segmented sub-images in the distance direction, and perform filtering processing on each image block separately and then merge the image blocks, so as to improve the filtering effect of each sub-image.
Referring to fig. 1, an embodiment of the present invention provides a processing method for suppressing scallops in an SAR image, including:
step 100: carrying out threshold segmentation on the SAR image to be processed to obtain a first subimage and a second subimage; the pixel value of each pixel point in the first sub-image is greater than a preset threshold value, and the pixel value of each pixel point in the second sub-image is not greater than the preset threshold value;
step 102: partitioning the second sub-image in the distance direction to obtain at least two image blocks; wherein, the distance direction is along the emission direction of radar waves;
step 104: performing Kalman filtering on all image blocks included in the second sub-image, and combining all image blocks subjected to Kalman filtering to obtain a target sub-image;
Step 106: and merging the first sub-image and the target sub-image to obtain a target SAR image.
In the embodiment of the invention, the method comprises the following steps: the method comprises the steps of carrying out threshold segmentation on an SAR image to be processed to obtain a first sub-image and a second sub-image, then carrying out blocking on the second sub-image in the distance direction to obtain at least two image blocks, carrying out Kalman filtering processing on all image blocks included in the second sub-image, merging all image blocks subjected to Kalman filtering processing to obtain a target sub-image, and finally merging the first sub-image and the target sub-image to obtain a target SAR image, so that the scallop effect of the SAR image can be effectively inhibited, and the processing quality of the SAR image is improved.
It should be noted that, because the kalman filter is suitable for the gaussian linear system, and the SAR image follows rayleigh distribution, especially, the statistical characteristics of the sea-land connected non-stationary region and the strong scattering point region are greatly different from the gaussian distribution, which results in a large estimation error of the kalman filter. However, although the statistical distribution of the image does not satisfy the gaussian distribution, the kalman filter can still achieve accurate results as long as the statistical distribution of each azimuth direction is not much different from the gaussian distribution. Therefore, the SAR image is segmented into the plurality of sub-images by the segmentation preprocessing of the SAR image, so that the statistical characteristics of each direction of the sub-images are closer to Gaussian distribution, the application condition of the Kalman filter is met, and the estimation error is reduced.
The manner in which the various steps shown in fig. 1 are performed is described below.
In some embodiments, before performing step 100, further comprising:
step A1: preprocessing an SAR image to be processed to obtain a preprocessed SAR image;
step A2: classifying the preprocessed SAR images to obtain a first SAR image and a second SAR image; the first SAR image is an SAR image with uniform and homogeneous pixel values, namely the SAR image with relatively stable background; the second SAR image is an SAR image with a sea-land connected background, namely an image with a more complex background.
In the embodiment, the SAR image is preprocessed, so that the accuracy of image processing can be ensured, and the preprocessed image is classified, so that the image with relatively stable background and the image with relatively complex background and connected with sea and land can be processed separately, thereby ensuring the image processing precision, accelerating the processing speed and reducing the occupation of computer resources.
With respect to step a1, the pretreatment method includes:
in some embodiments, the direction of the image is adjusted according to the preset image direction of the calculation model, so that the direction of the image is consistent with the preset image direction of the calculation model, thereby ensuring the accuracy of image processing. The azimuth direction of the preset image is the horizontal direction, namely the scallop stripes in the image are ensured to be vertical to the horizontal direction. Certainly, the user can also preset the distance direction of the image to be the horizontal direction, at this time, the scallop stripes in the image are parallel to the horizontal direction, the specific direction of the SAR image is not specifically limited in the present application, as long as the direction of the image is ensured to be consistent with the preset direction of the calculation model.
In some embodiments, adjusting the gray scale dynamic range of the image to 0-255 comprises:
obtaining maximum value I of image gray pixelmaxAnd minimum value of Imin；
Calculating each gray pixel I (I, j) of the image according to the following formula to realize the gray adjustment of the image:
it should be noted that the adjustment of the image gray scale range is not limited to this embodiment, and the user may determine the gray scale range of the image according to the image type and the reading method of the calculation model.
It should be noted that, for convenience of subsequent use, the distance between the scallops in the SAR image may also be acquired to determine the window size of the input parameter of the kalman filter. In general, half of the scallop fringe interval in an image can be taken as the size of a window, and in addition, the pixel interval of the adjacent scallop fringes of the image can be obtained by manually selecting the pixels of the image and reading the position coordinates of the pixels.
Researchers find in actual work that severe gray scale changes caused by the strong scattering point targets in the image with relatively stable background also affect the estimation accuracy of the Kalman filter, so that large scallop residues exist after azimuth filtering with the strong scattering points; meanwhile, the image of the sea-land joint background usually has severe gray scale change, which causes instability of the Kalman filter in the region with severe gray scale change, so that residual scallop effect and even artificially introduced artifacts can be left at the sea-land joint.
Thus, with respect to step 100, in some embodiments, there is provided:
performing threshold segmentation on the first SAR image to obtain a first sub-image and a second sub-image of the first SAR image;
and performing threshold segmentation on the second SAR image to obtain a first sub-image and a second sub-image of the second SAR image.
In the embodiment, the SAR image is subjected to threshold segmentation, so that the targets belonging to strong scattering points can be separated to obtain a first sub-image, namely a first sub-image of the first SAR image and a first sub-image of the second SAR image; and separating the targets belonging to the non-strong scattering points to obtain a second sub-image, namely a second sub-image of the first SAR image and a second sub-image of the second SAR image. In this way, it is advantageous to adopt different processing methods according to the respective characteristics of the first sub-image and the second sub-image, thereby accelerating the processing speed while ensuring the image processing accuracy.
Since the scenes included in the first SAR image and the second SAR image are different from each other, the segmentation threshold to be adopted in the threshold segmentation is different from each other, and it is necessary to select an optimal segmentation threshold for each SAR image based on the overall pixel distribution of the image.
In addition, since the second sub-image of the second SAR image includes a marine background and a land background, in some embodiments, the method further includes, for step B2:
performing morphological segmentation on a second subimage of the second SAR image to obtain an ocean subimage and a land subimage;
in some implementations, the morphological segmentation may be performed on a second sub-image of the second SAR image using the following method:
performing binarization processing on a second sub-image of the second SAR image based on the gray level difference of land and sea in the image to obtain a first binarized image comprising a sea area and a land area; wherein, the pixel of the ocean area is 0, and the pixel of the land area is 1;
sequentially performing hole filling operation, object deleting operation and closing operation on the binary image to be processed, wherein the area of the object is smaller than the preset area, so as to obtain a second binary image;
keeping the pixel at the first position in a second subimage of the second SAR image unchanged and setting the pixel at the second position as 0 to obtain an ocean subimage; the first position is the same as the position of a pixel 0 in the second binary image, and the second position is the same as the position of a pixel 1 in the second binary image;
Setting the pixel at the first position in a second sub-image of the second SAR image as 0, and keeping the pixel at the second position unchanged to obtain a land sub-image; the first position is the same as the position of the pixel 0 in the second binary image, and the second position is the same as the position of the pixel 1 in the second binary image.
In this embodiment, coarse segmentation of the ocean and the land can be realized by performing binarization processing on the second sub-image of the second SAR image; through the hole filling operation, the pixels of the missing part in the land area can be filled to be 1; an undesirable interference area in the image can be removed by executing the operation of deleting the small-area object, so that a clear segmentation boundary is obtained; by executing the closed operation, the space continuity and the area integrity of the segmented image can be realized, a smooth boundary is obtained, and the respective connectivity of an ocean area or a land area is further ensured; by traversing the binary image, the ocean subimage and the land subimage can be obtained, and the missing pixels in the ocean subimage and the land subimage are filled to be 0, so that the non-0 pixels in the ocean subimage and the land subimage are all the real pixels in the SAR image, and the accuracy of Kalman filtering is further ensured.
It should be noted that, by filling the missing pixels in the sea sub-image and the land sub-image with 0, when calculating the mean and the variance, the missing pixels will not be counted, only the real pixels belonging to the sea area from the original image are counted, and the same is true for other sub-images to be processed. The reason is that the distribution of the real pixels of the segmented sub-images is close to the Gaussian distribution, and the effective real pixels can be directly used for participating in the calculation of the Kalman filter, so that the missing pixel interference needs to be removed, the interference of the pixels of other heterogeneous regions is avoided, and the filtering accuracy of the homogeneous region is improved.
In practical applications, energy inhomogeneities caused by antenna pattern weighting are also present in the SAR image inward of the distance, i.e. the scallop intensity along the distance direction is not strictly constant, but shows a certain degree of space variability, i.e. the distance-to-space variability of the scallop effect. In the prior art, when the SAR image is subjected to kalman filtering, the scallop intensity of the whole image in a specific azimuth direction in the distance direction is generally taken as a constant value to be processed. However, due to the influence of the distance direction space variation, when the distance direction size is larger, the intensity difference of the scallops actually corresponding to different distance gates is larger, so that the constant scallop intensity estimated by the prior art is not suitable for the whole distance direction range, and scallop residue with part of distances upward is caused.
To solve this problem, in some embodiments, regarding step 102, if the number of pixels in the second sub-image in the distance direction is m and the number of pixels in the azimuth direction is n, the size of the second sub-image is m × n:
partitioning the second sub-image along the distance direction to obtain s overlapped distance image blocks, wherein the size of each image block is msX is x n; wherein s is more than or equal to 2, msThe number of pixels in each image block in the distance direction.
In this embodiment, the distance direction blocking is performed on the second sub-image, so that the scallop intensity of each image block can be ensured to be a constant value in the distance direction, and thus, the kalman filtering processing is performed on each image block, the problem of scallop residue caused by the distance direction null change can be solved, and the accuracy of image processing is improved.
It can be understood that, for each image, if the number of blocks is too small, the suppression of the spatial variability of the scallop effect distance cannot be guaranteed, and if the number of blocks is too large, the number of iterations is too small because the observed value estimated each time is too small, which results in inaccurate estimation and occupies more computer resources, so that reasonable blocking is required. The application does not specifically limit the number of the blocks, and the user can determine the number of the blocks according to the actual size and the precision requirement of the image.
Prior to performing step 104, in some embodiments, comprising:
constructing an additive model of scallop intensity estimation: s. thec(r，x)＝So(r，x)+o(x)；
In the formula, r and x respectively represent the positions of pixel points in the image block in the distance direction and the azimuth direction, and So(r, x) is a scallop noise-free image of the pixel point with the distance position r and the azimuth position x, Sc(r, x) is a scallop noise image of the pixel point with the distance position r and the position x, and o (x) is the scallop intensity of the pixel point with the position x;
constructing a Kalman filtering model suitable for the additive model, wherein a prediction equation of the Kalman filtering model is a formula set as follows:
in the formula (I), the compound is shown in the specification,
is an a priori estimate of the ith time,
the error covariance of the ith priori estimation is obtained, and Q is the covariance of system noise;
the corresponding state update equation is:
in the formula, KiIn order to be the basis of the kalman gain,
for the i-th a-posteriori estimation, i.e. the result of filtering, P, of each step of the estimationiIs the ithSecond a posteriori error covariance, R is the observed noise variance, ziIs the observed value of the ith time.
In the embodiment, the scallop intensity of each image block is regarded as a constant value in all distance directions of the current azimuth direction, the additive model is used as an estimation model of the scallop intensity, and then a prediction equation of the Kalman filter is constructed.
With respect to step 104, in some embodiments, it comprises:
and (3) performing the following operations on each image block by using the additive model, the prediction equation set and the state updating equation set:
traversing each column of azimuth directions in the image block along the azimuth direction by using a preset window, and acquiring an average value u and a variance s of pixels in the preset window as an average value u and a variance s corresponding to the current azimuth direction; the length of the preset window is equal to the length of the image block in the distance direction, and the width of the preset window is half of the distance between the scallop fringes in the current direction;
determining initial states of a posteriori estimates
Error covariance P of a posteriori estimation1And the noise covariance Q of the system;
subtracting the average value u from the gray value of each non-0 pixel in the current orientation direction of the image block to obtain an observed value z of the Kalman filter1～zkTaking the variance s of the current orientation direction as the observation noise R of the Kalman filter; wherein k is the number of non-0 pixels in the image block;
based on the prediction equation set and the state updating equation set, performing k iterations on the current azimuth direction to obtain the kth posterior estimation
Taking the scallop intensity as the scallop intensity o (x) of the current orientation of the image block; the specific iterative process is as follows: using the state posteriori estimation of step i-1 according to a prediction equation set 
Covariance of the a posteriori estimation error Pi-1And the system noise covariance Q is calculated to obtain the prior estimation of the current ith step state
And a priori error covariance
Updating the equation set according to the state, and estimating the state prior based on the ith step
First-check estimation error covariance
Ith observation ziAnd calculating the variance s to obtain the posterior estimation of the ith state
Sum a posteriori estimation error covariance Pi；
Posterior estimation based on k-th order
And an additive model, which realizes scallop inhibition in the current azimuth direction;
and traversing all azimuth directions of the image block, and obtaining the image block without scallop noise based on the processing steps.
And combining all the image blocks after filtering along the distance direction, wherein the gray value of the overlapped part is the average value of the two gray values of the two image blocks at the pixel position, and the gray value of the image block which is subjected to filtering at the corresponding position is directly adopted by the non-overlapped part, so that a noise-free target sub-image is obtained.
In the embodiment, the scallop intensity of each azimuth direction of each image block is obtained by solving the kalman filter equation system, and the scallop intensity is brought into an additive model to obtain the image block without scallop noise. Because the distance of each image block in the distance direction is small enough, the scallop intensity of the image block is approximate to a constant value in the distance direction, and the obtained scallop-noise-free image block has better scallop fringe suppression effect and high image quality. With respect to step 106, in some embodiments, comprises:
Merging the first sub-image of the first SAR image and the target second sub-image of the filtered first SAR image to obtain the filtered first SAR image;
and merging the first sub-image of the second SAR image, the filtered target ocean sub-image and the filtered target land sub-image to obtain the filtered second SAR image.
In this embodiment, by combining the first sub-image and the target sub-image after the filtering process, the SAR image without the scallop fringes can be obtained.
It should be noted that, in this embodiment, the kalman filtering process is not performed on the first sub-image of the SAR image, which is mainly based on two reasons: on one hand, the goal of scallop effect suppression is to better observe an image, the number of pixels of a strong scattering point obtained by threshold segmentation is small, and the gray level brightness of the strong scattering point is large, so that the visual influence of the scallop effect existing in the strong scattering point on the image quality is very little; on the other hand, the number of the strong scattering pixel points is too small, and the spatial distance is sparse, so that the correlation among the pixel points is poor, and the Kalman filter cannot accurately estimate the scallop intensity. Therefore, in the embodiment, the first sub-image of the SAR image is not subjected to kalman filtering, so that the visual effect of the SAR image is not affected, and meanwhile, computer resources can be greatly saved, and the image processing speed is increased.
The effectiveness of the processing method for the SAR image provided by the present application is explained below using experimental results:
as shown in fig. 2, the SAR image is relatively stable in background and mainly contains a land area with strong scattering points. The threshold segmentation for fig. 2 results in segmentation results as shown in fig. 3(a) and 3(b), and it can be seen that the segmentation results are segmented into a strong scattering point sub-image and a land sub-image, the strong scattering points are effectively separated, and the scattering characteristics of the land sub-image are stable. The results obtained by directly processing fig. 2 by using the existing scallop suppression method based on the kalman filter are shown in fig. 4. The results of processing fig. 2 using the method provided by the present invention are shown in fig. 5.
As can be seen from fig. 4 and 5, the scallop effect of both images is suppressed to some extent. Compared with fig. 2, the scallop effect is effectively inhibited in most regions in fig. 4, but due to the influence of the distance space variation, scallop residues are obviously existed in some regions. The scallop effect of each area in fig. 5 is better suppressed, and details such as rivers are well maintained. Therefore, the invention can effectively inhibit the scallop effect of the image with relatively stable background.
As shown in fig. 6, the SAR image is a sea-land connected background. Thresholding and morphological segmentation of fig. 6 yields the results shown in fig. 7(a) -7 (c), from which it can be seen that fig. 6 is segmented into strong scatter point sub-images, ocean sub-images and land sub-images, and that ocean and land areas are effectively separated. Fig. 6 is processed directly by using the existing scallop suppression method based on the kalman filter, and the obtained result is shown in fig. 8. The results of processing fig. 6 using the method provided by the present invention are shown in fig. 9.
As can be seen from fig. 8 and 9, the scallop effect of both images is suppressed to some extent. Compared with the original image, the scallop effect in fig. 8 is reduced, but more obvious scallop stripes still remain in the land area, the remaining degree changes along with the distance direction due to the influence of the distance space variation, and the inhibition effect in the unstable area connected with the sea and the land is not ideal. In fig. 9, it can be seen that the scallop noise is well suppressed in both sea area and land area, and the details of sea island and the like are maintained while the scallop noise is significantly suppressed in the sea-land junction area.
In order to quantitatively describe the scallop inhibition effect before and after image processing, the application introduces an average gray level intensity index, which is defined as the average gray level of a certain column of a tested area, as shown in the following formula:
and selecting the uniform scattering areas before and after the treatment, calculating the average gray intensity value of all distance directions of each column along the azimuth direction, and drawing a curve graph.
Fig. 10 shows average gray scale intensity curves before and after processing of the relatively smooth background image corresponding to fig. 2, where sharp fluctuations in the gray scale intensity curve of the original image represent scallop noise between light and dark in the original image. As can be seen from fig. 10, the average gray scale curve corresponding to fig. 5 is significantly reduced in the processing result of the method of the present invention, which means that the gray scale change caused by the scallop effect is smaller in the processing result of fig. 5, i.e. the scallop effect is suppressed.
The average gray intensity curve before and after the processing of the sea-land connected background image corresponding to fig. 6 is shown in fig. 11, wherein an ocean area with approximately uniform scattering in the image is selected, and it can be observed from the curve that the average gray intensity curve corresponding to the processing result of the method of the present invention shown in fig. 9 is very smooth compared with the corresponding curve of the original image, which proves that the scallop residue in fig. 9 is less, and further proves the effectiveness of the method of the present invention.
In order to further verify the universality of the processing method, in the embodiment, 6 pictures (hereinafter referred to as simple pictures 1 to 6) containing more scenes are selected for processing, and the change of objective statistical indexes is given to illustrate the effectiveness and the universality of the processing method, wherein the selected evaluation index is the intensity of the residual scallops in the azimuth direction.
The azimuth residual scallop intensity represents the relative maximum value of radiation fluctuation in two azimuthally adjacent bursts in a uniform scattering scene of the SAR image. According to the index definition, the scallop index evaluation formula is as follows:
Sa＝20log(ΔA)
wherein, Δ a measures the relative amplitude fluctuation in two adjacent bursts, i.e. the ratio of the maximum radiation value to the minimum radiation value at the maximum fluctuation.
The index testing method comprises the following steps:
(1) selecting a uniform scattering scene area in the image, and accumulating and averaging the data in each distance direction;
(2) smoothing adjacent points of the averaged data in each burst to obtain an azimuth radiation fluctuation characteristic curve;
(3) obtaining a maximum relative value of local radiation fluctuation according to the azimuth radiation fluctuation curve, namely a ratio of a maximum value to a minimum value of the maximum radiation fluctuation position in two adjacent bursts to obtain delta A;
(4) And obtaining a result according to a scallop index formula to be used as the relative scallop intensity of the image.
The suppression results for the selected scene images using the method of the present invention are shown in table 1:
TABLE 1 statistics of residual scallop index for experimental images before and after processing
The analysis of the results in the table shows that the intensity index of the residual scallop after the processing by the method is further reduced compared with the existing Kalman filtering method, and is greatly reduced compared with the original image, so that the relative intensity of the scallop after the processing by the method is greatly reduced, which shows the effectiveness of the method in inhibiting the scallop effect of the SAR image.
In summary, the SAR image processing method provided by the invention provides a distance direction blocking scheme and image segmentation preprocessing aiming at the scallop residue problem of the distance direction space variation of the scallop effect and the complex scene, further improves the scallop inhibition method based on the Kalman filter, can realize scallop effect inhibition on the general scene and the complex sea-land scene, and can improve the radiation quality of the SAR image.
As shown in fig. 12 and fig. 13, an embodiment of the present invention provides a processing device for scallop suppression of SAR images. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. From a hardware aspect, as shown in fig. 12, for a hardware architecture diagram of an electronic device in which a processing apparatus for SAR image scallop suppression according to an embodiment of the present invention is located, in addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 12, the electronic device in which the apparatus is located may also include other hardware, such as a forwarding chip responsible for processing a packet. Taking a software implementation as an example, as shown in fig. 13, as a logical device, a CPU of an electronic device in which the device is located reads a corresponding computer program in a non-volatile memory into a memory and runs the computer program.
As shown in fig. 13, the processing device for suppressing scallop in an SAR image according to this embodiment includes:
the segmentation module 300 is configured to perform threshold segmentation on the SAR image to be processed to obtain a first sub-image and a second sub-image; the pixel value of each pixel point in the first sub-image is greater than a preset threshold value, and the pixel value of each pixel point in the second sub-image is not greater than the preset threshold value;
a blocking module 302, configured to block the second sub-image in the distance direction to obtain at least two image blocks; wherein, the distance direction is along the emission direction of radar waves;
the processing module 304 is configured to perform kalman filtering processing on all image blocks included in the second sub-image, and merge all image blocks after the kalman filtering processing to obtain a target sub-image;
and a merging module 306, configured to merge the first sub-image and the target sub-image to obtain a target SAR image.
In an embodiment of the present invention, the dividing module 300 may be configured to perform the step 100 in the above-described method embodiment, the partitioning module 302 may be configured to perform the step 102 in the above-described method embodiment, the processing module 304 may be configured to perform the step 104 in the above-described method embodiment, and the merging module 306 may be configured to perform the step 106 in the above-described method embodiment.
In one embodiment of the present invention, the segmentation module 300 is configured to perform the following operations:
performing threshold segmentation on the first SAR image to obtain a first sub-image and a second sub-image of the first SAR image;
and performing threshold segmentation on the second SAR image to obtain a first sub-image and a second sub-image of the second SAR image.
In an embodiment of the present invention, the processing module 304 is configured to perform the following operations:
performing the following operations on each image block by using the additive model, the prediction equation set and the state updating equation set:
traversing each row of azimuth directions in the image block along azimuth directions by using a preset window, and acquiring an average value u and a variance s of pixels in the preset window as an average value u and a variance s corresponding to the current azimuth direction;
determining initial states of a posteriori estimates
The error covariance P1 of the a posteriori estimate and the noise covariance Q of the system; subtracting the average value u from the gray value of each non-0 pixel in the current azimuth direction of the image block to be used as an observed value z of a Kalman filter1～zkTaking the variance s of the current azimuth direction as the observation noise R of a Kalman filter; wherein k is the number of non-0 pixels in the image block;
Performing K iterations on the current azimuth direction based on the prediction equation set and the state updating equation set to obtain the K-th posterior estimation
Taking the scallop intensity as the scallop intensity o (x) of the current orientation of the image block;
posterior estimation based on the k-th time
The additive model is used for realizing scallop inhibition in the current azimuth direction;
and traversing all azimuth directions of the image block, and repeating the processing steps to obtain the image block without scallop noise.
It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation to the processing device for SAR image scallop suppression. In further embodiments of the invention, a SAR image scallop suppression processing apparatus may include more or fewer components than those shown, or combine certain components, or split certain components, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
Because the content of information interaction, execution process, and the like among the modules in the device is based on the same concept as the method embodiment of the present invention, specific content can be referred to the description in the method embodiment of the present invention, and is not described herein again.
The embodiment of the invention also provides electronic equipment which comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor executes the computer program to realize the processing method for suppressing the SAR image scallops in any embodiment of the invention.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, causes the processor to execute a processing method for SAR image scallop suppression in any embodiment of the present invention.
Specifically, a system or an apparatus equipped with a storage medium on which a software program code that realizes the functions of any of the above-described embodiments is stored may be provided, and a computer (or a CPU or MPU) of the system or the apparatus is caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium can realize the functions of any one of the above-described embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.
Examples of the storage medium for supplying the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (e.g., CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD + RW), a magnetic tape, a nonvolatile memory card, and a ROM. Alternatively, the program code may be downloaded from a server computer by a communications network.
Further, it should be clear that the functions of any one of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing a system or the like operating on the computer to perform a part or all of the actual operations based on the instructions of the program code, thereby implementing the functions of any one of the above-described embodiments.
Further, it is to be understood that the program code read out from the storage medium is written to a memory provided in an expansion board inserted into the computer or to a memory provided in an expansion module connected to the computer, and then causes a CPU or the like mounted on the expansion board or the expansion module to perform part or all of the actual operations based on instructions of the program code, thereby realizing the functions of any of the above-described embodiments.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Those of ordinary skill in the art will understand that: all or part of the steps of implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer-readable storage medium, and when executed, executes the steps including the method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic or optical disks, etc. in various media that can store program codes.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A processing method for suppressing scallop in SAR images is characterized by comprising the following steps:
carrying out threshold segmentation on the SAR image to be processed to obtain a first subimage and a second subimage; the pixel value of each pixel point in the first sub-image is greater than a preset threshold value, and the pixel value of each pixel point in the second sub-image is not greater than the preset threshold value;
Partitioning the second sub-image in the distance direction to obtain at least two image blocks; wherein the distance direction is along the direction of radar wave emission;
performing Kalman filtering on all image blocks included in the second sub-image, and merging all image blocks subjected to Kalman filtering to obtain a target sub-image;
and merging the first sub-image and the target sub-image to obtain a target SAR image.
2. The method of claim 1, wherein before thresholding the SAR image to be processed, the method comprises:
preprocessing the SAR image to be processed to obtain a preprocessed SAR image;
classifying the preprocessed SAR images to obtain a first SAR image and a second SAR image; the first SAR image is an SAR image with uniform and homogeneous pixel values, and the second SAR image is an SAR image with a sea-land connection background.
3. The method of claim 2, wherein performing threshold segmentation on the SAR image to be processed to obtain a first sub-image and a second sub-image comprises:
performing threshold segmentation on the first SAR image to obtain a first sub-image and a second sub-image of the first SAR image;
And performing threshold segmentation on the second SAR image to obtain a first sub-image and a second sub-image of the second SAR image.
4. The method of claim 3, wherein after the thresholding the second SAR image to obtain a first sub-image and a second sub-image of the second SAR image, further comprising:
and performing morphological segmentation on a second subimage of the second SAR image to obtain an ocean subimage and a land subimage.
5. The method of claim 4, wherein the morphological segmentation of the second sub-image of the second SAR image into an ocean sub-image and a land sub-image comprises:
carrying out binarization processing on a second sub-image of the second SAR image to obtain a first binarized image comprising an ocean area and a land area; wherein the pixel of the ocean area is 0, and the pixel of the land area is 1;
sequentially performing hole filling operation, object deleting operation with the area smaller than a preset area and closing operation on the binary image to be processed to obtain a second binary image;
keeping the pixel at the first position in a second subimage of the second SAR image unchanged and setting the pixel at the second position to be 0 to obtain an ocean subimage; the first position is the same as the position of a pixel of 0 in the second binary image, and the second position is the same as the position of a pixel of 1 in the second binary image;
Setting the pixel at the first position in the second sub-image of the second SAR image as 0 and keeping the pixel at the second position unchanged to obtain a land sub-image; the first position is the same as the position of a pixel of 0 in the second binary image, and the second position is the same as the position of a pixel of 1 in the second binary image.
6. The method according to claim 1, before performing kalman filtering processing on all image blocks included in the second sub-image, comprising:
constructing an additive model of scallop intensity estimation: s. thec(r,x)＝So(r,x)+o(x)；
In the formula, r and x respectively represent the positions of pixel points in the image block in the distance direction and the azimuth direction, and So(r, x) is a scallop noise-free image of a pixel point with the distance position r and the azimuth position x, Sc(r, x) is a scallop noise image of the pixel point with the distance position r and the position x, and o (x) is the scallop intensity of the pixel point with the position x;
constructing a Kalman filtering model suitable for the additive model, wherein a prediction equation of the Kalman filtering model is a formula set as follows:
in the formula (I), the compound is shown in the specification,
is an a priori estimate of the ith time,
the error covariance of the ith priori estimation is obtained, and Q is the covariance of system noise;
The corresponding state update equation is:
in the formula, KiIn order to be the basis of the kalman gain,
for the i-th a-posteriori estimation, i.e. the result of filtering, P, of each step of the estimationiIs the i-th posterior error covariance, R is the observation noise variance, ziIs the observed value of the ith time.
7. The method according to claim 6, wherein the performing kalman filtering on all image blocks included in the second sub-image comprises:
performing the following operations on each image block by using the additive model, the prediction equation set and the state updating equation set:
traversing each row of azimuth directions in the image block along azimuth directions by using a preset window, and acquiring an average value u and a variance s of pixels in the preset window as an average value u and a variance s corresponding to the current azimuth direction;
determining initial states of a posteriori estimates
Error covariance P of a posteriori estimation1And the noise covariance Q of the system; subtracting the average value u from the gray value of each non-0 pixel in the current azimuth direction of the image block to be used as an observed value z of a Kalman filter1～zkTaking the variance s of the current orientation direction as observation noise R of a Kalman filter; wherein k is the number of non-0 pixels in the image block;
Based on the prediction equation set and the state updating equation set, performing k iterations on the current azimuth direction to obtain the kth posterior estimation
Taking the intensity as the scallop intensity o (x) of the current orientation of the image block;
posterior estimation based on the k-th time
The additive model is used for realizing scallop inhibition in the current direction;
and traversing all azimuth directions of the image block, and repeating the processing steps to obtain the image block without scallop noise.
8. A processing device for suppressing a scallop in an SAR image is characterized by comprising:
the segmentation module is used for carrying out threshold segmentation on the SAR image to be processed to obtain a first subimage and a second subimage; the pixel value of each pixel point in the first sub-image is greater than a preset threshold value, and the pixel value of each pixel point in the second sub-image is not greater than the preset threshold value;
the partitioning module is used for partitioning the second sub-image in the distance direction to obtain at least two image blocks; wherein the distance direction is along the direction of radar wave emission;
the processing module is used for performing Kalman filtering processing on all image blocks included in the second sub-image and merging all image blocks subjected to Kalman filtering processing to obtain a target sub-image;
And the merging module is used for merging the first sub-image and the target sub-image to obtain a target SAR image.
9. An electronic device comprising a memory in which a computer program is stored and a processor which, when executing the computer program, carries out the method according to any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when executed in a computer, causes the computer to carry out the method of any one of claims 1-7.
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