CN112269175B - Distributed scatterer selection method combined with spearman coefficient - Google Patents
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- 238000001514 detection method Methods 0.000 claims description 16
- 238000001276 Kolmogorov–Smirnov test Methods 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9021—SAR image post-processing techniques
- G01S13/9023—SAR image post-processing techniques combined with interferometric techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
Abstract
The invention discloses a method for selecting a distributed scatterer by combining a spearman coefficient, which ensures the effectiveness of the method for selecting the distributed scatterer (DS point) by introducing the spearman coefficient and increases the quality and the quantity of the selected DS points; meanwhile, the method can effectively inhibit the problem of miss-selection and miss-selection in the traditional DS point selection and inspection method, improves the precision of selecting points and also increases the number of DS points to be selected.
Description
Technical Field
The invention relates to the field of distributed scatterer selection, in particular to a method for selecting a distributed scatterer by combining a Szelman coefficient.
Background
The synthetic aperture radar interferometry (InSAR) technology is a space earth observation technology generated in the 60 th year of the 20 th century, can realize all-weather earth observation all day by day without being limited by illumination and weather conditions, and can also penetrate the earth surface and vegetation to acquire subsurface information. The permanent scatterer can still keep high coherence in a long time interval, can fully utilize interference image pairs with long base line distance, improves the data utilization rate to the maximum extent, and is not influenced by space-time uncorrelation and atmospheric effects. Thus, permanent scatterer synthetic aperture radar interferometry (PSInSAR) technology has achieved significant success in surface deformation monitoring. However, in a complex natural environment or due to complex and changeable ground surface conditions caused by engineering construction, the PSInSAR has a limitation.
In 2011, ferrett et al proposed a SqueeSAR algorithm that strictly distinguishes between permanent scatterers (hereinafter abbreviated PS) and distributed scatterers (hereinafter abbreviated DS) from the physical layer, overcoming the limitation that PSInSAR cannot effectively participate in the solution at low density in the natural earth surface area. The method is different from the method that a PS target is used as a main scatterer in a resolution unit, has strong scattering property, extremely high signal-to-noise ratio and high coherence coefficient, the DS target has moderate signal-to-noise ratio, low coherence coefficient and low phase stability, and is mainly because the DS does not contain the strong scatterer, the corresponding backward scattering capability is weaker, the influence of space-time interference is easy, and the probability of erroneously extracting pixels with low signal-to-noise ratio level is larger. The main methods for extracting the distributed scatterers at present are non-parametric hypothesis testing methods, such as Kolmogorov-Smirnov (KS) test, anderson-Darling (AD) test, baungarter-Wei beta-Schindler (BWS) test, and a fast homogeneous point selection method (FaSHPS algorithm) for converting the traditional hypothesis testing problem into a confidence interval estimation method, and the like, and the results still have the problems of missed detection errors, false extraction errors and the like. How to solve the shortcomings of the above method is a great challenge for the industry.
Disclosure of Invention
In order to improve the number and the precision of the distributed scatterer selection, the invention aims to provide a distributed scatterer selection method combining the Spermann coefficient for inhibiting the miss-selection and the miss-selection in the traditional DS point selection inspection method, improving the selection precision of the distributed scatterer and increasing the number of the selected DS points so as to overcome the defects in the prior art.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a method of constructing a distributed scatterer selection in combination with a spearman coefficient, the method comprising the steps of:
step 1: collecting SAR images after N-view multi-view processing, and performing clipping and registration pretreatment;
step 2: defining a detection window, carrying out KS (K-means) detection in the detection window, checking whether two pixels have consistency in time sequence pixel by pixel, and recording a hypothesis test result;
step 3: defining a detection window, simulating the noise influence degree by using a Szelman coefficient in the detection window, quantitatively detecting the noise interference degree pixel by pixel, and recording the result;
step 4: constructing a comprehensive measure to determine a consistent pixel set according to the hypothesis test result obtained in the step 2 and the noise interference degree simulation result obtained in the step 3;
step 5: performing adjacency checking on the selected consistent pixel set, searching 8 neighborhoods, reserving points adjacent to the central pixel, and selecting DS (DSC) as alternative points with the number of the adjacent points being higher than a set threshold value;
step 6: and screening DSC by using the coherence coefficient to obtain a DS point.
Further, in the method for selecting a distributed scatterer with a combined spearman coefficient according to the present invention, step 1 specifically includes the following steps:
step 1.1: collecting data according to the SAR image;
step 1.2: and selecting one of the N-scene images as a main image, and registering the rest images to a main image space, wherein the registration precision is sub-pixel level.
Further, in the method for selecting a distributed scatterer with a combined spearman coefficient according to the present invention, step 2 specifically includes the following steps:
step 2.1: opening a window with the size of 11 x 11 for each pixel of the selected image;
step 2.2: traversing pixel by pixel, and carrying out KS test to judge whether two pixels have consistency, wherein the KS test calculation formula is as follows:
wherein F is an overall distribution, X 1 、X 2 ，…，X k For independent co-distributed samples, N is the number of images selected, where X (1) ≤X 2 ≤…≤X (k) Sequences obtained after reordering from small to large;
step 2.3: setting zero assumption and alternative assumption for judging consistency of two pixels, recording a test result, and calculating the following formula;
wherein i, j respectively represent two different pixel points, F N i (x)、F N j (x) Respectively instead of their empirical distribution function.
Further, in the method for selecting a distributed scatterer with a combined spearman coefficient of the present invention, step 3 specifically includes the following steps:
step 3.1: opening a window of 3*3 size on the selected image pixel by pixel;
step 3.2: and quantitatively detecting noise image factors pixel by pixel, simulating the noise image degree by using a spearman coefficient, wherein the spearman coefficient rho is calculated according to the following formula:
where i is a certain pair of variables, n is the number of grades, and d is the grade difference of the pair of variables.
Step 3.3: and recording the affected result by combining the spatial weight.
Further, in the method for selecting a distributed scatterer with a combined spearman coefficient of the present invention, step 4 specifically includes the following steps:
step 4.1: combining the hypothesis test result recorded in the step 2 and the noise interference degree result recorded in the step 3;
step 4.2: traversing pixel by pixel in a 3*3 window, setting a threshold condition, recording a missing point influenced by accidental noise of an image, and eliminating a noise point of hypothesis test;
step 4.3: and determining a consistent pixel set and recording a result.
Further, in the method for selecting a distributed scatterer with a combined spearman coefficient of the present invention, step 5 specifically includes the following steps:
step 5.1: performing adjacency checking on the selected consistent pixel set, and reserving points adjacent to the central pixel;
step 5.2: and setting a number threshold, and selecting DSC as the adjacent points with the number higher than the set threshold.
Further, in the method for selecting a distributed scatterer with a combined spearman coefficient of the present invention, step 6 specifically includes the following steps:
the coherence coefficient value of each DSC is calculated, and an empirical coherence threshold is set, assuming that DSC with coherence higher than the threshold is the desired DS point.
According to the invention, the spearman coefficient is introduced, so that the effectiveness of a distributed scatterer selection method is ensured, and the quality and the number of selected distributed scatterer points are increased; meanwhile, the problem of miss-selection and miss-selection in the traditional distributed scatterer point selection and inspection method can be effectively restrained, and the precision of point selection is improved.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method for selecting a distributed scatterer with a combined Szellman coefficient according to an embodiment of the present invention;
FIG. 2 is a Google image and average amplitude plot according to an embodiment of the present invention;
fig. 3 is a simulation result of the spearman coefficient (hereinafter referred to as SCC) characterization used in the present invention at different noise values.
FIG. 4 is an example of the results of selecting 20-scene Sentinel-1A SLC data for a distributed diffuser in Yangquan city of Shanxi province, china.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present invention, a detailed description of embodiments of the present invention will be made with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present invention provides a flowchart of a method for selecting a distributed scatterer with a spearman coefficient, the method comprising the steps of:
step 1: the SAR image after the N-scene multi-view processing is collected, and is subjected to cutting and registration preprocessing, and the data adopted in the embodiment is 20-scene sentel-1A SLC data, but the invention is not limited to the data. The pretreatment method is as follows;
step 1.1: establishing an interested region in an SAR image range, and collecting data according to the SAR image;
step 1.2: and selecting one of the N-scene images as a main image, registering the rest images into a main image space, wherein the registration precision is sub-pixel level, and ensuring the pixel-to-pixel one correspondence of different images.
Step 2: defining a detection window, carrying out KS (K-means) detection in the detection window, checking whether two pixels have consistency in time sequence pixel by pixel, and recording a hypothesis test result;
step 2.1: opening a window with the size of 11 x 11 for each pixel of the selected image;
step 2.2: traversing pixel by pixel, and carrying out KS test to judge whether two pixels have consistency, wherein the KS test calculation formula is as follows:
wherein F is an overall distribution, X 1 、X 2 ，…，X k For independent co-distributed samples, N is the number of images selected, where X (1) ≤X 2 ≤…≤X (k) The sequence obtained after the rearrangement from small to large.
Step 2.3: setting zero assumption and alternative assumption for judging consistency of two pixels, recording a test result, and calculating the following formula;
wherein i, j respectively represent two different pixel points, F N i (x)、F N j (x) Respectively instead of their empirical distribution function.
Step 3: defining a detection window, simulating the noise influence degree by using a Szelman coefficient in the detection window, quantitatively detecting the noise interference degree pixel by pixel, and recording the result;
step 3.1: opening a window of 3*3 size on the selected image pixel by pixel;
step 3.2: and quantitatively detecting noise image factors pixel by pixel, simulating the noise image degree by using a spearman coefficient, wherein the spearman coefficient rho is calculated according to the following formula:
where i is a certain pair of variables, n is the number of grades, and d is the grade difference of the pair of variables.
Step 3.3: and calculating the spearman coefficients of the center pixel and pixels in four directions (upper left, right, lower right and lower right) of the neighborhood, and recording the result of the influence degree of noise on the center pixel by combining the spatial weight P.
Step 4: constructing a comprehensive measure to determine a consistent pixel set according to the hypothesis test result obtained in the step 2 and the noise interference degree simulation result obtained in the step 3;
step 4.1: combining the hypothesis test result recorded in the step 2 and the noise interference degree result recorded in the step 3;
step 4.2: traversing pixel by pixel in a 3*3 window, setting a threshold condition, and checking the noise influence degree by pixel, wherein the number of points which pass the hypothesis test in the threshold condition window is set to be n & gt3 in the embodiment, but the invention is not limited to the above;
step 4.3: and updating and recording the missed points affected by accidental noise of the image and the noise points erroneously selected in the removed hypothesis test in the consistent pixel set, and determining the consistent pixel set and recording the result.
Step 5: performing adjacency checking on the selected consistent pixel set, searching 8 neighborhoods, reserving points adjacent to the central pixel, and selecting DS alternative points (DSC) with the number of the adjacent points being higher than a set threshold value;
step 5.1: performing adjacency checking on the selected consistent pixel set, and reserving points adjacent to the central pixel;
step 5.2: the set number threshold is selected as DSC, and the number of adjacent points is higher than the set threshold, and the set threshold is n > 80 in the embodiment, but the invention is not limited thereto.
Step 6: and screening DSC by using the coherence coefficient to obtain a DS point.
Step 6.1: the coherence coefficient value of each DSC is calculated, and an empirical coherence threshold is set, assuming that DSC with coherence higher than the threshold is the desired DS point.
The effects of the present invention are further described below with reference to examples.
1. Example content
The results of the experiments of the present invention are shown in fig. 3 and 4. The invention adopts 20-scene Sentinel-1A SLC data of Yangquan city of Shanxi province of China, the image size is 500 x 500, and fig. 2 is a Google image graph and a corrected amplitude graph, wherein the Google image graph comprises the types of ground features such as buildings, rivers, cultivated lands and the like.
2. Results and analysis
Fig. 3 is a simulation result of the spearman coefficient (hereinafter referred to as SCC) characterization used in the present invention at different noise values. The phase of the X point is added with noise with average value of 0 and standard deviation of 0.3, 0.5, 0.7 and 0.9 radian respectively. Meanwhile, noise with the mean value of 0 and the standard deviation of sigma Y is added to the phase of the Y point respectively, so that sigma Y is gradually increased from 0 to 1. For each σy, 5000 simulations were performed, respectively, to obtain 5000 SCC values, the mean and variance of which were calculated and plotted as error plots. The smaller the noise of the reference point X, the larger the corresponding SCC value, and on the premise that the noise of X is fixed, the larger the noise of the adjacent point Y, the smaller the average value of SCC and the larger the standard deviation. The SCC may be used as a metric to characterize the magnitude of phase noise at two pixels.
FIG. 4 is a graph of an example of the results of selecting a 20-scene Sentinel-1A SLC data distributed scatterer in Yangquan, shanxi province, china, wherein 4 (a) is a graph of the intermediate results of selecting consistent pixels for hypothesis testing in accordance with the present invention; FIG. 4 (b) is a graph of the results of the present invention for modeling the degree of noise exposure using the Szelman coefficients; fig. 4 (c) is a graph of DS point results of the method for selecting a distributed scatterer with a combined spearman coefficient according to the present invention, and as can be seen from fig. 4 (c), the method for selecting a distributed scatterer with a combined spearman coefficient can better identify a distributed scatterer under a complex natural subsurface, and meanwhile, compared with a conventional inspection method, the experimental result shows that the number of DS points is increased by 2.33%.
The invention discloses a distributed scatterer selection method combining with a Szechwan coefficient, which is characterized in that the effectiveness of the distributed scatterer selection method is ensured and the quality and the quantity of the selected distributed scatterers are increased by introducing the Szechwan coefficient on the basis of a traditional hypothesis test method; meanwhile, the problem of miss-selection and miss-selection in the traditional distributed scatterer selection and inspection method can be effectively restrained, and the precision of point selection is improved.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are all within the protection of the present invention.
Claims (6)
1. A method for selecting a distributed scatterer in combination with a spearman coefficient, the method comprising the steps of:
step 1: collecting SAR images after N-view multi-view processing, and performing clipping and registration pretreatment;
step 2: defining a detection window, carrying out KS (K-means) detection in the detection window, checking whether two pixels have consistency in time sequence pixel by pixel, and recording a hypothesis test result;
step 3: defining a detection window, simulating the noise influence degree by using a Szelman coefficient in the detection window, quantitatively detecting the noise interference degree pixel by pixel, and recording the result;
the specific steps of the step 3 are as follows:
step 3.1: opening a window of 3*3 size on the selected image pixel by pixel;
step 3.2: and quantitatively detecting noise image factors pixel by pixel, simulating the noise image degree by using a spearman coefficient, wherein the spearman coefficient rho is calculated according to the following formula:
ρ＝1-(6∑d_i^2)/(n(n^2-1))
wherein i is a certain pair of variables, n is the number of grades, and d is the grade difference number of the pair of variables;
step 3.3: recording the affected result by combining the space weight;
step 4: constructing a comprehensive measure to determine a consistent pixel set according to the hypothesis test result obtained in the step 2 and the noise interference degree simulation result obtained in the step 3;
step 5: performing adjacency checking on the selected consistent pixel set, searching 8 neighborhoods, reserving points adjacent to the central pixel, and selecting DS alternative points with the number of the adjacent points being higher than a set threshold value;
step 6: and screening DSC by using the coherence coefficient to obtain a DS point.
2. The method for selecting a distributed scatterer in combination with a spearman coefficient of claim 1,
the specific steps of the step 1 are as follows:
step 1.1: collecting data according to the SAR image;
step 1.2: and selecting one of the N-scene images as a main image, and registering the rest images to a main image space, wherein the registration precision is sub-pixel level.
3. The method for selecting a distributed scatterer in combination with a spearman coefficient of claim 1,
the specific steps of the step 2 are as follows:
step 2.1: opening a window with the size of 11 x 11 for each pixel of the selected image;
step 2.2: traversing pixel by pixel, and carrying out KS test to judge whether two pixels have consistency, wherein the KS test calculation formula is as follows:
wherein F is an overall distribution, X 1 、X 2 ，…，X k For independent co-distributed samples, N is the number of images selected, where X (1) ≤X 2 ≤…≤X (k) Sequences obtained after reordering from small to large;
step 2.3: setting zero assumption and alternative assumption for judging consistency of two pixels, recording the test result, and calculating the following formula:
wherein i, j respectively represent two different pixel points, F N i (z)、F N j (z) each represents its empirical distribution function.
4. The method for selecting a distributed scatterer with a combined spearman coefficient according to claim 1, wherein the specific steps of step 4 are:
step 4.1: combining the hypothesis test result recorded in the step 2 and the noise interference degree result recorded in the step 3;
step 4.2: traversing pixel by pixel in a 3*3 window, setting a threshold condition, recording a missing point influenced by accidental noise of an image, and eliminating a noise point of hypothesis test;
step 4.3: and determining a consistent pixel set and recording a result.
5. The method for selecting a distributed scatterer with a combined spearman coefficient according to claim 1, wherein the specific steps of step 5 are:
step 5.1: performing adjacency checking on the selected consistent pixel set, and reserving points adjacent to the central pixel;
step 5.2: and setting a number threshold, and selecting DSC as the adjacent points with the number higher than the set threshold.
6. The method for selecting a distributed scatterer with a combined spearman coefficient according to claim 1, wherein the specific steps of step 6 are:
the coherence coefficient value of each DSC is calculated, and an empirical coherence threshold is set, assuming that DSC with coherence higher than the threshold is the desired DS point.
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