CN108062767B - Statistical same-distribution spatial pixel selection method based on time sequence SAR image - Google Patents

Statistical same-distribution spatial pixel selection method based on time sequence SAR image Download PDF

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CN108062767B
CN108062767B CN201810141076.4A CN201810141076A CN108062767B CN 108062767 B CN108062767 B CN 108062767B CN 201810141076 A CN201810141076 A CN 201810141076A CN 108062767 B CN108062767 B CN 108062767B
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蒋弥
肖儒雅
胡睿
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Hohai University HHU
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Abstract

The invention discloses a statistical same-distribution spatial pixel selection method based on a time sequence SAR image, which comprises the steps of firstly preprocessing an original SAR image data sequence to obtain a single-view SAR intensity image sequence; registering the SAR intensity image sequence; acquiring a rejection region of likelihood ratio test under the assumption that the registered single-view SAR intensity image obeys exponential statistical distribution; comparing the statistical similarity of the time sample of each spatial pixel in the rectangular sliding window with the time sample of the central reference pixel; traversing each spatial pixel in the whole image to obtain a statistical homodistribution sample of each spatial pixel; filtering the SAR image by Lee filtering and the same distribution sample of each spatial pixel; according to the method, samples with the same attributes as the reference pixels are selected through likelihood ratio hypothesis test, the space distribution rule and the backscattering characteristic of the pixel points in the radar image are better met, and a filtering image with full resolution closer to a real earth surface is obtained.

Description

Statistical same-distribution spatial pixel selection method based on time sequence SAR image
Technical Field
The invention belongs to the technical field of SAR data processing, and particularly relates to a statistical same-distribution spatial pixel selection method based on a time sequence SAR image.
Background
In recent years, with the successive transmission of a new type of satellite-borne SAR (Synthetic Aperture Radar) sensor, time-series SAR technology has occupied an increasingly important position in Radar remote sensing applications. Since the SAR echo signals are an accumulation of backscatter contributions of scatterers within the ground resolution unit, these scatterers exhibit random phase, resulting in coherent speckle formation of the SAR data signals. How to effectively remove speckle noise in SAR data and recover a real SAR reflection value is a difficult point of SAR research. SAR speckle filtering was proposed from the 80 s, such as multiview filtering, Lee filtering, MAP-Sigma filtering, Kuan filtering, IDAN filtering, NLSAR filtering, and the like. However, these filters use local spatial statistics to attenuate noise, making spatial resolution difficult to maintain, texture edge signal loss, or signal loss and noise suppression difficult to balance.
The SAR filtering method based on the time sequence does not involve space operation because the time samples are used for pixel selection, and has absolute advantages in solving the problems. The method compares the spatial pixel similarity under a nonparametric or parametric hypothesis test framework, but because of the magnitude of the self efficacy of hypothesis test, the first and second types of errors of different hypothesis tests are different, so that the actual filtering effect is different. In contrast, the parametric hypothesis test works better, especially the likelihood ratio test, which is proven to be the optimal hypothesis test by the Riemann-Pearson theorem. However, the rejection domain of the likelihood ratio test is often difficult to give and thus limited in practical applications.
Disclosure of Invention
The invention aims to enable SAR filtering results to reflect real radar backscattering coefficients, provides a statistical same-distribution spatial pixel selection method based on a time sequence SAR image, realizes SAR same-distribution sample selection, and solves the technical problems of boundary blurring and image distortion caused by heterogeneous pixels participating in filtering in the traditional SAR image speckle filtering in the aspect of limiting the similarity of comparison spatial pixels in likelihood ratio test.
The invention adopts the following technical scheme that a statistical same-distribution spatial pixel selection method based on a time sequence SAR image comprises the following specific steps:
1) preprocessing an original SAR image data sequence to obtain a single-view SAR intensity image sequence;
2) registering the preprocessed single-view SAR intensity image sequence to obtain a registered single-view SAR intensity image sequence;
3) acquiring a rejection region of likelihood ratio test under the assumption that the registered single-view SAR intensity image obeys exponential statistical distribution;
4) setting a rectangular sliding window with the size of m multiplied by m, wherein m is the number of pixels, m is an odd number, comparing the statistical similarity of the time sample of each spatial pixel in the window with the time sample of the central reference pixel one by one, setting a zero hypothesis that the two time samples have the same distribution, and if the likelihood ratio test turns over the zero hypothesis under the given significant level alpha condition, determining that the spatial pixel and the central reference pixel are heterogeneous pixels; otherwise, the likelihood ratio test accepts a null hypothesis, namely that the spatial pixel and the central reference pixel are the same distribution pixel;
5) repeating the step 4), traversing each spatial pixel in the whole image size, and obtaining a statistical homodistribution sample of each spatial pixel;
6) and (3) adopting a filter, replacing the pixels in the original directional window of the filter with the statistical identically distributed samples obtained by each spatial pixel, filtering the SAR image, and outputting a result.
Preferably, the single-view SAR intensity image sequence is obtained by a complex modulo squaring method.
Preferably, the registration employs an intensity maximum cross-correlation algorithm.
Preferably, the rejection field of the likelihood ratio test is obtained in particular for time samples { x } that obey an exponential distribution1,x2,…,xnAnd { y }1,y2,…,yn-obtaining a simplified version of the likelihood ratio test under a null hypothesis, which is set to the assumption that the two time samples have the same distribution:
Figure GDA0003258966540000021
where Λ is the equivalent reduced statistic of the likelihood ratio test,
Figure GDA0003258966540000022
and
Figure GDA0003258966540000023
respectively represent the average of two time samples; f (2n,2n) denotes F distribution subject to a degree of freedom (2n,2n), and n denotes the number of time-series SAR images.
Preferably, the statistical similarity of the temporal sample of each spatial pixel within the comparison window with the temporal sample of the central reference pixel is embodied in that the temporal samples { y } of each spatial pixel i are compared one by one in a window of size m × m1 i,y2 i,…,yn iAnd the center reference pixel x1,x2,…,xnThe statistical similarity of 1,2, …, m × m, under the given significance level α, when the estimation result of the equivalent reduced statistic Λ of the likelihood ratio test falls in the reject domain, the null-flipping assumption is pushed, that is, two spatial pixels are not similar, and the spatial pixel is a heterogeneous pixel with the central reference pixelA peptide; otherwise, the assumption of zero is accepted, two pixels are the same distribution pixels, and the discrimination formula is as follows:
Figure GDA0003258966540000031
or
Figure GDA0003258966540000032
Wherein
Figure GDA0003258966540000033
And
Figure GDA0003258966540000034
respectively represent the upper part of the distribution of F (2n,2n)
Figure GDA0003258966540000035
And
Figure GDA0003258966540000036
and (5) dividing the site.
Preferably, the filter is a Lee filter.
The invention has the following beneficial effects: the invention relates to a statistical same-distribution spatial pixel selection method based on a time sequence SAR image, which realizes SAR same-distribution sample selection and solves the technical problems of boundary blurring and image distortion caused by heterogeneous pixels participating in filtering in the traditional SAR image speckle filtering; on the basis of a time sequence SAR image, a likelihood ratio statistical test method with a rejection region is used for selecting out the spatial similar neighborhood pixels of each spatial pixel, so that the confusion of the earth surface characteristics with different scattering mechanisms during speckle filtering is avoided; in the same-distribution statistical sample selection process, a fixed rejection region is given by taking the characteristic that the F distribution is the simplified distribution of two exponential distribution likelihood ratio test statistics as reference, so that the test efficacy is optimal, and the reliability of the selected same-distribution pixels is higher; all the selected same distribution sets are used for Lee filtering, so that the spatial resolution and detail retention of a filtering image can be ensured, and the noise suppression maximization is considered.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a sample set of identically distributed pixels extracted using a likelihood ratio test with a rejection field according to the present invention;
FIG. 3 is a diagram illustrating the results of an original noise image and various filtering methods.
Detailed Description
The technical solution of the present invention is further explained with reference to the embodiments according to the drawings.
Fig. 1 is a flowchart of the present invention, and a statistical same-distribution spatial pixel selection method based on a time sequence SAR image includes the following specific steps:
1) preprocessing an original SAR image data sequence, and obtaining a single-view SAR intensity image sequence by adopting the square of a plurality of modulus;
2) registering the preprocessed single-view SAR intensity image sequence to obtain a registered single-view SAR intensity image sequence;
in order to qualitatively and quantitatively analyze the effectiveness of the method, X-band German TerrraSAR-X radar satellite single polarization data are adopted as the data, the image incidence angle is 37 degrees, and the spatial resolution is 3m multiplied by 3m (distance direction multiplied by azimuth direction). In order to enhance the estimation accuracy of hypothesis testing, the original images are registered, 256 × 256 matching points are selected by using an intensity maximum cross-correlation algorithm, and the size of a matching window is 64 × 64.
It should be noted that: the method is not only suitable for Terras SAR-X data selected in the experiment, but also suitable for other satellite-borne and airborne data, and only different matching point numbers and window sizes need to be selected according to the surface characteristics of different research areas during registration.
3) Acquiring a rejection region of likelihood ratio test under the assumption that the registered single-view SAR intensity image obeys exponential statistical distribution;
for time samples that obey exponential distribution { x1,x2,…,xnAnd { y }1,y2,…,ynGet a simplified version of the likelihood ratio test under the null hypothesis, set the null hypothesis as two time samplesThis has the same distribution:
Figure GDA0003258966540000041
where Λ is the equivalent reduced statistic of the likelihood ratio test,
Figure GDA0003258966540000042
and
Figure GDA0003258966540000043
respectively representing the average values of two time samples of a central reference pixel and a neighborhood pixel to be detected; f (2n,2n) denotes F distribution subject to a degree of freedom (2n,2n), and n denotes the number of time-series SAR images.
4) FIG. 2 is a schematic diagram of a sample set of identically distributed pixels extracted using a likelihood ratio test with a rejection field according to the present invention; fig. 2 (a) is a 15 × 15 rectangular sliding window superimposed on the SAR image; the same distribution pixel sample set taken within the window (b) of fig. 2. A rectangular sliding window of size m x m, m being the number of pixels, m being 15, is set, the temporal sample { y ] of each spatial pixel i1 i,y2 i,…,yn iSee the dot in (a) of FIG. 2, the center reference pixel { x }1,x2,…,xnSee the central triangle point in (a) of fig. 2, comparing the statistical similarity of the two, i is 1,2, …, m × m, and under the condition that the significance level α is 0.05, when the estimation result of the equivalent simplified statistic Λ of the likelihood ratio test falls in the rejection domain, the null hypothesis is inverted, that is, two spatial pixels are not similar, and the spatial pixel is a heterogeneous pixel with the central reference pixel; otherwise, accepting the null hypothesis that the two pixels are the same distribution pixels, see the point in (b) of fig. 2, and the discrimination formula is as follows:
Figure GDA0003258966540000044
or
Figure GDA0003258966540000045
Wherein
Figure GDA0003258966540000046
And
Figure GDA0003258966540000047
respectively represent the upper part of the distribution of F (2n,2n)
Figure GDA0003258966540000048
And
Figure GDA0003258966540000049
and (5) dividing the site.
It should be noted that: the size of the sliding window is determined by the resolution of the selected SAR image, and the parameters can be adjusted according to the selected other satellite-borne and airborne SAR data.
5) Repeating the step 4), traversing each spatial pixel in the whole image size until a statistical homography sample of each spatial pixel is obtained;
6) and adopting a Lee filter, replacing the pixels in the original directional window of the filter with the statistical identically distributed samples obtained by each spatial pixel, filtering the SAR image, and outputting a result. FIG. 3 is a diagram illustrating the results of an original noise image and various filtering methods; fig. 3 (a) is the original noise image, fig. 3 (b) is the result of regular window filtering, fig. 3 (c) is the result of conventional Lee filtering, and fig. 3 (d) is the result of filtering according to the present invention.
In order to quantitatively analyze the filtering effect of the statistical co-distributed sample selection of the present invention in the SAR image in (d) of fig. 3, the accuracy of the filtering result in the present invention is evaluated by using the speckle noise intensity index SSI and the resolution retention index SNR, and compared with the original noise image in (a) of fig. 3, the regular window filtering result in (b) of fig. 3, and the conventional Lee filtering result in (c) of fig. 3, and table 1 shows the accuracy comparison results of different filtering methods.
It should be noted that: both regular window filtering and conventional Lee filtering employ a size of 7 x 7. In the evaluation index, the smaller the speckle noise intensity index SSI is, the better the noise suppression effect is; the larger the resolution retention SNR value, the better the edge retention effect.
TABLE 1 comparison of accuracy of different filtering methods
Figure GDA0003258966540000051
As can be seen from table 1 and fig. 3, the statistical homography sample selection of the present invention has a better effect than the regular window and the conventional Lee filtering method: in the filtering result obtained by the method, the boundary of the ground object is clearer and more continuous, the areas with similar scattering mechanisms are better distinguished, and the final result is closer to the real ground surface.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (4)

1. A statistical same-distribution spatial pixel selection method based on a time sequence SAR image is characterized by comprising the following steps:
1) preprocessing an original SAR image data sequence to obtain a single-view SAR intensity image sequence;
2) registering the preprocessed single-view SAR intensity image sequence to obtain a registered single-view SAR intensity image sequence;
3) under the assumption that the registered single-view SAR intensity image sequence obeys exponential statistical distribution, acquiring a rejection region of likelihood ratio test, specifically: for time samples y obeying exponential statistical distribution1,y2,…,ynAnd the time sample of the center reference pixel { x }1,x2,…,xn-obtaining a simplified version of the likelihood ratio test under a null hypothesis, which is set to the assumption that the two time samples have the same distribution:
Figure FDA0003258966530000011
where Λ is the equivalent reduced statistic of the likelihood ratio test,
Figure FDA0003258966530000012
and
Figure FDA0003258966530000013
respectively represent the average of two time samples; f (2n,2n) represents F distribution with the degree of freedom of (2n,2n), and n represents the number of time series SAR images;
4) setting a rectangular sliding window of size m x m, m being the number of pixels and m being an odd number, comparing the time samples { y } of each spatial pixel i one by one in a window of size m x m1 i,y2 i,…,yn iAnd the time sample of the center reference pixel { x }1,x2,…,xnThe statistical similarity of 1,2, …, m × m, under the condition of a given significance level α, when the estimation result of the equivalent simplified statistic Λ of the likelihood ratio test falls in the rejection domain, the null-flipping assumption is carried out, that is, two spatial pixels are not similar, and the spatial pixel is a heterogeneous pixel with the central reference pixel; otherwise, the assumption of zero is accepted, two pixels are the same distribution pixels, and the discrimination formula is as follows:
Figure FDA0003258966530000014
or
Figure FDA0003258966530000015
Wherein
Figure FDA0003258966530000016
And
Figure FDA0003258966530000017
respectively represent the upper part of the distribution of F (2n,2n)
Figure FDA0003258966530000018
And
Figure FDA0003258966530000019
the position of the branch point is divided into two parts,
setting a null hypothesis that two time samples have the same distribution, if the likelihood ratio test under the given significance level alpha overturns the null hypothesis, the spatial pixel and the central reference pixel are heterogeneous pixels; otherwise, the likelihood ratio test accepts a null hypothesis, namely that the spatial pixel and the central reference pixel are the same distribution pixel;
5) repeating the step 4), traversing each spatial pixel in the whole image size until a statistical homography sample of each spatial pixel is obtained;
6) and (3) adopting a filter, replacing the pixels in the original directional window of the filter with the statistical identically distributed samples of each spatial pixel, filtering the original SAR image, and outputting a result.
2. The method for selecting spatial pixels with statistical homography based on time sequence SAR image as claimed in claim 1, wherein the step 1) is to obtain the sequence of single view SAR intensity images by a complex modulo squaring method.
3. The statistical co-distribution spatial pixel selection method based on time series SAR images as claimed in claim 1, characterized in that intensity maximum cross-correlation algorithm is adopted for registration in step 2).
4. The method for selecting spatial pixels in statistical co-distribution based on time series SAR images as claimed in claim 1, wherein the filter in step 6) is Lee filter.
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