Interference correction method and system for multi-station sar coherent change detection

Abstract

The application provides a method and system for out-of-coherence disturbance correction for multi-station SAR coherent change detection, and the method comprises the following steps: collecting stack data sets of two passes by multi-station SAR, and performing image registration on SAR images collected by different stations at different times; using echo signal energy equalization criteria to correct amplitude errors of the registered SAR images; using non-local filtering and strongest scattering mechanism separation method to accurately estimate noise phase; correcting out-of-coherence disturbance errors of the coherence coefficients calculated from the accurately preprocessed data, and correctly indicating change and non-change information; and the function of improving the accuracy and correction speed of multi-baseline InSAR phase errors is realized. The application corrects the out-of-coherence disturbance factors irrelevant to change information without relying on ground control points, precise orbit parameters, terrain information and traditional complex interference phase processing steps, and the amplitude and phase correction speed is faster and the accuracy is higher, and after the correction of the coherence disturbance factors, the details are clearer and the false alarm rate is lower.

Description

Technical Field

[0001] This invention belongs to the field of synthetic aperture radar data processing technology, specifically relating to a method and system for phase loss interference dynamic correction for multi-station SAR coherence change detection. Background Technology

[0002] Coherent Change Detection (CCD) utilizes the coherence of Synthetic Aperture Radar (SAR) images before and after phase changes as the information source for change detection. Since changed areas exhibit low coherence or relatively unchanged areas exhibit incoherence, CCD technology can detect subtle changes in a scene by leveraging the low coherence of changed areas and the high coherence of unchanged areas. It has been successfully applied in target detection, urban planning, soil moisture measurement, environmental monitoring, and disaster assessment.

[0003] Coherence statistics can characterize scene changes, providing the basis for CCD observations. Typically, CCD technology acquires two SAR images of the same scene at different times and uses coherence statistics to detect changed areas. Several statistics have been proposed, such as classical coherence estimators, alternative coherence estimators, non-coherent change detection (NCCD) estimators, complex reflection change detection metrics, and weighted coherence estimators, to describe the differences between changed and unchanged areas. However, these classical coherence statistics are highly sensitive to slowly changing, uninteresting targets, making them difficult to apply to CCDs. For slowly changing targets, such as growing vegetation, canopies, and sand dunes affected by wind and rain, their coherence decreases due to subtle scattering changes. Furthermore, for unchanged targets with weak scattering intensity or large residual phase errors during processing, they exhibit low coherence characteristics due to their low signal-to-noise ratio scattering. Therefore, it is difficult to accurately distinguish changed targets from low-coherence interference areas, especially when there is no prior information and the coherence coefficient map is generated only from two consecutively observed SAR images. This leads to a large number of false alarms and misreports in change detection.

[0004] Recently, with the development of distributed SAR satellite technology, multi-station SAR has increased the number of CCD observations by expanding the spatial dimension, enabling the rapid collection of required repeated observation SAR data and timely monitoring of developments. However, there is still a lack of coherent statistics specifically designed for multi-station SAR change detection. The prior information that observations within the same time period should maintain complete coherence provides favorable conditions for phase-loss interference correction in multi-station SAR. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method and system for phase loss interference dynamic correction for multi-station SAR coherent change detection, so as to improve the accuracy and correction speed of multi-baseline InSAR phase error.

[0006] The technical solution adopted by this invention to solve the above-mentioned technical problems is: a phase loss interference dynamic correction method for multi-station SAR coherence change detection, comprising the following steps: S1: Register the multi-station SAR complex image stack to obtain aligned multi-station SAR complex image data; S2: Perform amplitude error correction on the aligned multi-station SAR complex image data to obtain multi-station SAR complex image data with similar signal strength; S3: Perform phase error correction on multi-station SAR complex image data with similar signal strength to obtain finely preprocessed multi-station SAR image data; S4: Perform coherence calculation and phase loss interference correction on the multi-station SAR fine preprocessed image data to obtain the corrected multi-station SAR coherence change detection indicator.

[0007] According to the above scheme, the specific steps in step S1 are as follows: Multi-station SAR images were acquired from two flights, and image registration was performed using the maximum coherence coefficient criterion to obtain aligned multi-station SAR complex image data.

[0008] According to the above scheme, the specific steps in step S2 are as follows: S21: Select permanent scatterer pixels based on the amplitude deviation criterion; S22: Based on the criterion that the pixel values ​​of permanent scatterers with the same name are similar in the multi-station SAR stack, calculate the normalized energy factor for all images. Using the formula The multi-station SAR complex image stack data after amplitude correction was calculated. .

[0009] According to the above scheme, the specific steps in step S3 are as follows: The nonlocal similarity weights of multi-station SAR images are calculated pixel by pixel, the weighted coherence matrix is ​​calculated, the interferometric phase corresponding to the strongest scattering mechanism is separated by the singular value decomposition method, the interferometric phase is removed from the multi-station SAR complex data, and the multi-station SAR fine preprocessed image data with amplitude error corrected and noise phase preserved is obtained.

[0010] Furthermore, in step S3, the specific steps are as follows: S31: Take a 3×3 small image patch and calculate the average correlation between adjacent image patches and the reference image patch: (1) The adaptive weights used to measure the similarity of adjacent image patches within the coherence calculation window and to construct the coherence matrix calculation; in equation (1) This indicates that the non-local image patch is related to the numbered... The adjacent pixels pixel vector, This represents the center pixel number of the reference, and the window size used for coherence matrix estimation is [value missing]. , This indicates the scalar conjugate operation. This indicates taking the modulus of a vector, therefore The value ranges from 0 to 1; S32: Construct a weighting coefficient based on the similarity between samples at the center pixel positions of image patches. (2) In formula (2) For the set coherence threshold, when The time weighting coefficient is 1; S33: Substitute the weighting coefficients into the coherence matrix calculation formula to obtain the coherence matrix of the center pixel within the coherence matrix calculation window. : (3) In formula (3) Indicates the reference pixel number is Window size is The neighboring area numbered as pixel vectors, This indicates that a vector is transposed using the conjugate operation. S34: Perform singular value decomposition on the coherence matrix and select the phase part of the eigenvector corresponding to the largest eigenvalue as the deterministic phase in the multi-station SAR complex data.

[0011] Furthermore, step S3 further includes the following step: S35: Traverse each pixel in the image, execute steps S31 to S34, and obtain the deterministic phase values ​​of all bistatic SAR image stack data. Using formulas Obtain multi-station SAR complex data after amplitude and phase correction.

[0012] According to the above scheme, the specific steps in step S4 are as follows: The coherence coefficients of the multi-station SAR fine preprocessed image data are calculated pairwise. The logarithm of the obtained coherence coefficients is taken, and the deterministic decoherence factor and the logarithmic form of the corrected coherence change detection coefficient are obtained by using Huber regression analysis to solve the log-linear equation, thus obtaining the corrected coherence change indicator.

[0013] Furthermore, in step S4, the specific steps are as follows: S41: According to the traditional coherence calculation formula: (4) Calculate the pairwise coherence coefficients of multi-station SAR fine preprocessed image data; S42: Construct a multi-station SAR comprehensive coherence model based on the InSAR comprehensive coherence model: (5) in Indicates the number is Determining the components of multi-station SAR phase-loss interference. ; Indicates the number of stations. This represents the coherence coefficient, which reflects the actual changes in the scene, and is the solution to be found. Represents the random component of phase-disrupted disturbances; S43: Take the logarithm of both sides of equation (5) to convert the product model into a summation model: (6) in The logarithmic form representing the random component of phase-disrupted disturbance; S44: Using a robust Huber regression M-estimator, solve for the corrected coherence coefficient in equation (6). and deterministic phase loss interference dynamic factor ; S45: Traverse each pixel of the image and execute steps S41 to S45 to obtain the corrected coherent change statistics.

[0014] Phase-loss interference dynamic correction system for multi-station SAR coherence variation detection The image registration submodule is used for multi-station SAR complex image stack registration to obtain aligned multi-station SAR complex image data. The amplitude correction submodule is used to correct the amplitude error of the aligned multi-station SAR complex image data to obtain multi-station SAR complex image data with similar signal strength. The phase correction submodule is used to correct the phase error of multi-station SAR complex image data with similar signal strength, so as to obtain multi-station SAR fine preprocessed image data with amplitude error corrected and noise phase preserved. The coherence technology and phase-disruption interference correction submodule is used to calculate the pairwise coherence coefficients of multi-station SAR fine preprocessed image data and perform phase-disruption interference correction on the coherence coefficients associated with change detection to obtain a corrected coherence change indicator.

[0015] A computer memory storing a computer program executable by a computer processor, the computer program executing a phase-loss interference correction method for multi-station SAR coherence change detection.

[0016] The beneficial effects of this invention are as follows: 1. The present invention relates to a method and system for phase-disruption interference correction for multi-station SAR coherence change detection. This method involves acquiring stacked datasets from two flights using multi-station SAR, registering SAR images acquired at different stations and time phases, correcting amplitude errors in the registered SAR images using the echo signal energy equalization criterion, accurately estimating noise phase using nonlocal filtering and the strongest scattering mechanism separation method, and correcting phase-disruption interference errors in the coherence coefficients calculated from the precisely preprocessed data to correctly indicate changes and non-changes. This method improves the accuracy and speed of phase error correction in multi-baseline InSAR.

[0017] 2. This invention provides a method to accurately calculate the noise phase without relying on ground control points, precise orbit parameters and terrain information, or traditional complex interferometric phase processing steps. It eliminates the need for manual amplitude calibration, precise orbit and terrain auxiliary information, and complex interferometric phase preprocessing, thereby improving the accuracy of amplitude and phase error correction and providing a reliable data foundation for coherent estimation.

[0018] 3. This invention corrects the phase-disruption interference dynamic factor that is unrelated to the change information, improves the accuracy of coherent change indication, and provides a reliable observation basis for multi-station SAR coherent change detection.

[0019] 4. The present invention has faster amplitude and phase correction speed and higher accuracy. After correction of phase interference factors, the details of coherent change indication are clearer and the false alarm rate is lower.

[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of an embodiment of the present invention.

[0023] Figure 2 This is a flowchart illustrating an embodiment of the present invention.

[0024] Figure 3 This is a multi-station SAR dataset according to an embodiment of the present invention.

[0025] Figure 4 This is a diagram of the simultaneous multi-station SAR coherence correction process according to an embodiment of the present invention.

[0026] Figure 5 This is a diagram illustrating the multi-station SAR coherence correction process at different time phases according to an embodiment of the present invention.

[0027] Figure 6 These are two magnified views of local areas during the multi-station SAR coherence correction process at different time phases in an embodiment of the present invention.

[0028] Figure 7 This is a diagram showing the multi-station SAR coherence error correction results according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0030] Example 1 See Figure 1 The specific steps of the phase-loss interference dynamic correction method for multi-station SAR coherence change detection are as follows: S1: Acquire multi-station SAR images from two flights and perform image registration processing to obtain aligned multi-station SAR composite image data; S2: Perform amplitude error correction on the aligned multi-station SAR complex image data to obtain multi-station SAR complex image data with similar signal strength; S3: Calculate the non-local similarity weight in the multi-station SAR image pixel by pixel, calculate the weighted coherence matrix, use the singular value decomposition method to separate the interferometric phase corresponding to the strongest scattering mechanism, remove it from the multi-station SAR complex data, and thus obtain multi-station SAR fine preprocessed image data with amplitude error corrected and noise phase preserved. S4: The multi-station SAR fine preprocessing image data is used to calculate the pairwise coherence coefficients, and the logarithm of the calculated coherence coefficients is taken. Using the Huber regression analysis method, the deterministic decoherence factor and the logarithmic form of the corrected coherence change detection coefficient in the log-linear equation are solved, and finally the corrected coherence change indicator is obtained.

[0031] This embodiment first performs rapid preprocessing on multi-station SAR images to achieve accurate coherence estimation; then, through the constructed phase interference correction model and robust Huber regression method, it obtains the corrected coherence statistics that truly reflect scene change information, reduces the background false alarm rate, improves the detail features of changing targets, and provides a reliable observation basis for multi-station SAR CCD.

[0032] Example 2 The steps in this embodiment are the same as in Embodiment 1, except that each step is applied to a specific instance. Specifically, it includes the following steps: S1: Acquire multi-station SAR images from two flights, and perform image registration processing on the stacked SAR images using the maximum coherence coefficient criterion to obtain aligned multi-station SAR complex image stack data. ; S2: Perform amplitude error correction on the aligned multi-station SAR complex image data to obtain multi-station SAR complex image data with similar signal strengths. The specific steps are as follows: S21: Select permanent scatterer pixels based on the amplitude deviation criterion; S22: Based on the criterion that the pixel values ​​of permanent scatterers with the same name are similar in the multi-station SAR stack, calculate the normalized energy factor for all images. Using the formula The multi-station SAR complex image stack data after amplitude correction was calculated. .

[0033] S3: Calculate the nonlocal similarity weights in the multi-station SAR image pixel by pixel, calculate the weighted coherence matrix, and use the singular value decomposition method to separate the interferometric phase corresponding to the strongest scattering mechanism. Remove this phase from the multi-station SAR complex data to obtain finely preprocessed multi-station SAR image data with amplitude error corrected and noise phase preserved. The specific steps are as follows: S31: Take 3×3 small image patches, and use the coherence coefficient criterion to measure the similarity between image patches, using the following formula. (1) The average correlation between adjacent image patches and the reference image patch is calculated to measure the similarity between adjacent image patches within the coherence calculation window, and is used for adaptive weight construction during coherence matrix calculation. In Equation (1) This indicates that the non-local image patch is related to the numbered... The adjacent pixels pixel vector, This represents the center pixel number of the reference, and the window size used for coherence matrix estimation is [value missing]. , This indicates the scalar conjugate operation. This indicates taking the modulus of a vector, therefore The value ranges from 0 to 1.

[0034] S32: Based on the image patch similarity calculated in step S31, construct the similarity between samples at the center pixel positions of the image patches, using the following formula: (2) As weighting coefficients, they are used for adaptive estimation of the coherence matrix. In equation (2) The set coherence threshold is typically around 0.75, indicating that when... The time weighting coefficient is 1.

[0035] S33: Substitute the weight coefficients from step S32 into the following coherence matrix calculation formula. (3) Obtain the coherence matrix of the center pixel within the coherence matrix calculation window. In equation (3) Indicates the reference pixel number is Window size is The neighboring area numbered as pixel vectors, This indicates that a vector is subjected to a conjugate transpose operation.

[0036] S34: Perform singular value decomposition on the calculated coherence matrix, and select the phase part of the eigenvector corresponding to the largest eigenvalue as the deterministic phase in the multi-station SAR complex data.

[0037] S35: Traverse each pixel in the image, execute steps S31 to S34, and thus obtain the deterministic phase values ​​of all bistatic SAR image stack data. Next, using the formula Obtain multi-station SAR complex data after amplitude and phase correction.

[0038] S4: The coherence coefficients of the multi-station SAR fine preprocessed image data are calculated pairwise, and the logarithm of the calculated coherence coefficients is taken. Using the Huber regression analysis method, the deterministic decoherence factor and the logarithmic form of the corrected coherence change detection coefficient in the log-linear equation are solved, and finally the corrected coherence change indicator is obtained.

[0039] S41: According to the traditional coherence calculation formula (4) The coherence coefficients of any pairwise coherence data in the finely preprocessed multi-station SAR image data were calculated.

[0040] S42: Based on the InSAR integrated coherence model, the following multi-station SAR integrated coherence model is constructed. (5) in Indicates the number is Determining the components of multi-station SAR phase-loss interference ( ; (Indicates the number of stations). This represents the coherence coefficient, which reflects the actual changes in the scene, and is the solution to be found. This represents the random component of phase-disrupted disturbances.

[0041] S43: Take the logarithm of both sides of equation (5) to transform the original complex product model into a summation model. (6) in The logarithmic form of the random component of phase-disrupted disturbance.

[0042] S44: Using a robust Huber regression M estimator, solve for the corrected coherence coefficient in equation (6). and deterministic phase loss interference dynamic factor .

[0043] S45: Traverse each pixel in the image and execute steps S41 to S45 to obtain the corrected coherent change statistics.

[0044] This embodiment acquires stacked datasets from two flights using multi-station SAR, performs image registration on SAR images acquired from different stations at different times, corrects amplitude errors in the registered SAR images using the echo signal energy balance criterion, accurately estimates noise phase using nonlocal filtering and the strongest scattering mechanism separation method, and corrects phase-disruption interference dynamic errors in the coherence coefficients calculated from the precisely preprocessed data, correctly indicating changes and non-changes. This achieves the function of improving the accuracy and correction speed of multi-baseline InSAR phase error.

[0045] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0046] Example 3 This embodiment uses the steps of Embodiment 1 to perform phase-loss interference correction on the coherence coefficients obtained from five dual-station SAR data sets. Six coherence coefficient images are selected to illustrate the process of amplitude and phase correction and phase-loss interference correction using the present invention. Figure 2 This is a multi-station SAR dataset from an embodiment of the present invention; Figure 3This is a diagram of the simultaneous phase multi-station SAR coherence correction process according to an embodiment of the present invention. The first column is the uncorrected coherence coefficient diagram, the second column is the coherence coefficient diagram after amplitude and phase correction, and the third column is the coherence coefficient diagram after amplitude, phase, and deterministic phase loss interference correction. It can be seen that after amplitude, phase, and deterministic phase loss interference correction of the present invention, the simultaneous phase interference is effectively corrected. Figure 4 These are diagrams illustrating the multi-station SAR coherence correction process at different time phases according to an embodiment of the present invention. Figure 5 These are two magnified images of the multi-station SAR coherence correction results at different time phases in this embodiment of the invention. It can be seen that after the amplitude, phase, and deterministic phase loss interference dynamic correction of this invention, the coherence coefficient of the low coherence background region is effectively corrected, and the details of the changing target become clearer. Figure 6 The image shows the final result of the multi-station SAR coherence error correction obtained by this invention. It can be seen that the details of the changes are clearer, demonstrating the effectiveness of this invention in correcting phase loss interference factors.

[0047] Example 4 This embodiment is used to implement the principle of the above method embodiment to construct a phase-loss interference dynamic correction system for multi-station SAR coherence change detection, including an image registration submodule, an amplitude correction submodule, a phase correction submodule, a coherence calculation and phase-loss interference dynamic correction submodule; The image registration submodule is used for multi-station SAR complex image stack registration to obtain aligned multi-station SAR complex image data; The amplitude correction submodule is used to correct the amplitude error of the aligned multi-station SAR complex image data to obtain multi-station SAR complex image data with similar signal strength. The phase correction submodule is used to correct the phase error of multi-station SAR complex image data with similar signal strength, so as to obtain multi-station SAR fine preprocessed image data with amplitude error corrected and noise phase preserved. The coherence technology and phase-disruption interference correction submodule is used to calculate pairwise coherence coefficients of multi-station SAR fine preprocessed image data and perform phase-disruption interference correction on the coherence coefficients associated with change detection to obtain a corrected coherence change indicator.

[0048] Each submodule is mainly used to implement the various steps of the method implementation, which will not be elaborated here.

[0049] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0050] This embodiment also includes a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the program is executed by the processor, the processor performs the steps of the phase loss interference dynamic correction method for multi-station SAR coherence change detection.

[0051] This embodiment also provides a computer-readable storage medium storing executable instructions that, when executed by a processor, enable the processor to implement a phase-loss interference dynamic correction method for multi-station SAR coherence change detection.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0053] Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This application is described with reference to the flowchart of the method and computer program product according to Embodiment 1 and the block diagram of the device (system) according to Embodiment 3. It should be understood that each step or block in the flowchart or block diagram, as well as combinations of steps or blocks in the flowchart or block diagram, can be implemented by computer program instructions.

[0055] These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes or boxes Figure 1 A phase-loss interference correction system for multi-station SAR coherence change detection, specifying the functions in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes or boxes Figure 1 The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes or boxes Figure 1 The steps of the phase-loss interference correction method for multi-station SAR coherence change detection specified in one or more boxes.

[0058] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for phase-loss interference dynamic correction for multi-station SAR coherence change detection, characterized in that: Includes the following steps: S1: Register the multi-station SAR complex image stack to obtain aligned multi-station SAR complex image data; S2: Perform amplitude error correction on the aligned multi-station SAR complex image data to obtain multi-station SAR complex image data with similar signal strength; S3: Perform phase error correction on multi-station SAR complex image data with similar signal strength to obtain finely preprocessed multi-station SAR image data; S4: Perform coherence calculation and phase loss interference correction on the multi-station SAR fine preprocessed image data to obtain the corrected multi-station SAR coherence change detection indicator.

2. The method for phase loss interference dynamic correction for multi-station SAR coherence change detection according to claim 1, characterized in that: The specific steps in step S1 are as follows: Multi-station SAR images were acquired from two flights, and image registration was performed using the maximum coherence coefficient criterion to obtain aligned multi-station SAR complex image data.

3. The method for phase loss interference dynamic correction for multi-station SAR coherence change detection according to claim 1, characterized in that: The specific steps in step S2 are as follows: S21: Select permanent scatterer pixels based on the amplitude deviation criterion; S22: Based on the criterion that the pixel values ​​of permanent scatterers with the same name are similar in the multi-station SAR stack, calculate the normalized energy factor for all images. Using the formula The multi-station SAR complex image stack data after amplitude correction was calculated. .

4. The method for phase loss interference dynamic correction for multi-station SAR coherence change detection according to claim 1, characterized in that: The specific steps in step S3 are as follows: The nonlocal similarity weights of multi-station SAR images are calculated pixel by pixel, the weighted coherence matrix is ​​calculated, the interferometric phase corresponding to the strongest scattering mechanism is separated by the singular value decomposition method, the interferometric phase is removed from the multi-station SAR complex data, and the multi-station SAR fine preprocessed image data with amplitude error corrected and noise phase preserved is obtained.

5. The method for phase loss interference dynamic correction for multi-station SAR coherence change detection according to claim 4, characterized in that: The specific steps in step S3 are as follows: S31: Take a 3×3 small image patch and calculate the average correlation between adjacent image patches and the reference image patch: （1） The adaptive weights used to measure the similarity of adjacent image patches within the coherence calculation window and to construct the coherence matrix calculation; in equation (1) This indicates that the non-local image patch is related to the numbered... The adjacent pixels of the first pixel vector, This represents the center pixel number of the reference, and the window size used for coherence matrix estimation is [value missing]. , This indicates the scalar conjugate operation. This indicates taking the modulus of a vector, therefore The value ranges from 0 to 1; S32: Construct a weighting coefficient based on the similarity between samples at the center pixel positions of image patches. （2） In formula (2) For the set coherence threshold, when The time weighting coefficient is 1; S33: Substitute the weighting coefficients into the coherence matrix calculation formula to obtain the coherence matrix of the center pixel within the coherence matrix calculation window. : （3） In formula (3) Indicates the reference pixel number is Window size is The neighboring area numbered as pixel vectors, This indicates that a vector is transposed using the conjugate operation. S34: Perform singular value decomposition on the coherence matrix and select the phase part of the eigenvector corresponding to the largest eigenvalue as the deterministic phase in the multi-station SAR complex data.

6. The method for phase loss interference dynamic correction for multi-station SAR coherence change detection according to claim 5, characterized in that: Step S3 further includes the following step: S35: Traverse each pixel in the image, execute steps S31 to S34, and obtain the deterministic phase values ​​of all bistatic SAR image stack data. Using formulas Obtain multi-station SAR complex data after amplitude and phase correction.

7. The method for phase-loss interference dynamic correction for multi-station SAR coherence change detection according to claim 1, characterized in that: The specific steps in step S4 are as follows: The coherence coefficients of the multi-station SAR fine preprocessed image data are calculated pairwise. The logarithm of the obtained coherence coefficients is taken, and the deterministic decoherence factor and the logarithmic form of the corrected coherence change detection coefficient are obtained by using Huber regression analysis to solve the log-linear equation, thus obtaining the corrected coherence change indicator.

8. The method for phase-loss interference dynamic correction for multi-station SAR coherence change detection according to claim 7, characterized in that: The specific steps in step S4 are as follows: S41: According to the traditional coherence calculation formula: （4） Calculate the pairwise coherence coefficients of multi-station SAR fine preprocessed image data; S42: Construct a multi-station SAR comprehensive coherence model based on the InSAR comprehensive coherence model: （5） in Indicates the number is Determining the components of multi-station SAR phase-loss interference. ; Indicates the number of stations. This represents the coherence coefficient, which reflects the actual changes in the scene, and is the solution to be found. Represents the random component of phase-disrupted disturbances; S43: Take the logarithm of both sides of equation (5) to convert the product model into a summation model: （6） in The logarithmic form representing the random component of phase-disrupted disturbance; S44: Using a robust Huber regression M-estimator, solve for the corrected coherence coefficient in equation (6). and deterministic phase loss interference dynamic factor ; S45: Traverse each pixel of the image and execute steps S41 to S45 to obtain the corrected coherent change statistics.

9. A phase-loss interference dynamic correction system for multi-station SAR coherent change detection, characterized in that: The image registration submodule is used for multi-station SAR complex image stack registration to obtain aligned multi-station SAR complex image data. The amplitude correction submodule is used to correct the amplitude error of the aligned multi-station SAR complex image data to obtain multi-station SAR complex image data with similar signal strength. The phase correction submodule is used to correct the phase error of multi-station SAR complex image data with similar signal strength, so as to obtain multi-station SAR fine preprocessed image data with amplitude error corrected and noise phase preserved. The coherence technology and phase-disruption interference correction submodule is used to calculate the pairwise coherence coefficients of multi-station SAR fine preprocessed image data and perform phase-disruption interference correction on the coherence coefficients associated with change detection to obtain a corrected coherence change indicator.

10. A computer memory, characterized in that: It contains a computer program that can be executed by a computer processor, which performs the phase-loss interference correction method for multi-station SAR coherence change detection as described in any one of claims 1 to 8.

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