Radar interferogram processing method and device fused with GNSS spatial interpolation result, and medium

Abstract

The invention relates to the technical field of deformation monitoring, and particularly discloses a radar interferogram processing method and device fusing a GNSS spatial interpolation result, and a medium, and the method comprises the following steps: carrying out the refined processing of each error term in GNSS displacement time series data, screening an InSAR interferogram according to the length of a space-time baseline and the number of effective pixel points, and carrying out the calculation of the InSAR interferogram; the method comprises the following steps: acquiring a GNSS spatial interpolation result, enabling the GNSS interpolation result to perform weighted fusion after time alignment with an InSAR interferogram, performing small baseline set time sequence analysis processing on the processed InSAR interferogram, and improving the precision and reliability of a deformation fusion result. According to the method, the InSAR interferogram error is corrected based on the GNSS spatial interpolation result, and the atmospheric error in the InSAR interferogram is corrected by using the GNSS spatial interpolation result and the InSAR interferogram weight fusion, so that the precision of correcting the InSAR interferogram by the GNSS data is higher, and the stability of the displacement time sequence result is better.

Description

Technical Field

[0001] The present invention relates to the field of deformation monitoring technology, and in particular to a radar interference pattern processing method, device and medium integrating GNSS spatial interpolation results. Background Art

[0002] The rapid development and application of GNSS and InSAR technologies have accumulated a wealth of data sources for deformation monitoring, making it possible to carry out large-scale, high-precision ground deformation monitoring efficiently. In order to reduce the impact of medium and long wavelength errors such as atmospheric errors and improve the accuracy and reliability of InSAR displacement time series results, some scholars have taken advantage of the high accuracy and fixed position of GNSS, and the atmospheric errors can be weakened by combining observations. Using GNSS data as a reference, the atmospheric errors between GNSS data and InSAR interferogram data are calculated, and then surface fitting and correction are performed. After time series analysis and processing, high-precision deformation time series results are obtained.

[0003] Most of the existing studies are based on the premise that the difference between GNSS and InSAR interferogram data mainly comes from the influence of atmospheric errors on InSAR data and the errors are distributed in a planar manner. When the GNSS stations are evenly distributed and the errors in the interferogram are spatially homogeneous over a large range, the fusion accuracy is high. However, when the InSAR interferogram is affected by medium and long wavelength errors and presents obvious spatial heterogeneity, the fusion results are prone to spatial aggregation of errors in local areas, resulting in low accuracy of displacement time series results in some time periods. However, with the large-scale engineering construction, groundwater extraction, mineral resource development and other human activities in the process of urbanization, geological disasters caused by ground deformation are becoming more and more serious. It is necessary to conduct high-precision, high-temporal and high-spatial resolution settlement monitoring to reduce the safety hazards caused by geological disasters caused by ground deformation. Therefore, it is necessary to combine the advantages of high precision and high temporal resolution of GNSS and high spatial resolution of InSAR to develop a highly applicable GNSS and InSAR deformation data fusion method to overcome the problems of high labor intensity, unstable accuracy and low work efficiency of traditional measurement methods in deformation monitoring projects.

[0004] In summary, for large-scale, high-precision surface deformation monitoring, radar interferogram processing methods, equipment and media that integrate GNSS spatial interpolation results are urgently needed. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a radar interferogram processing method, device and medium integrating GNSS spatial interpolation results. The specific technical solutions are as follows:

[0006] The radar interferogram processing method integrating GNSS spatial interpolation results is characterized by comprising the following steps:

[0007] S1: Acquire the GNSS displacement timing, perform error correction on the GNSS displacement timing, and obtain the GNSS displacement timing after error correction;

[0008] S2: The error-corrected GNSS displacement time series is transferred to the line-of-sight direction of InSAR, and the GNSS displacement time series data in the line-of-sight direction is spatially interpolated to obtain the GNSS spatial interpolation result;

[0009] S3: Screen the InSAR interferogram, align, unwrap and weightedly fuse the GNSS spatial interpolation result and the InSAR interferogram in turn to obtain the fused InSAR interferogram;

[0010] S4: Construct the observation equation, use the smoothing operator to constrain the deformation rate of InSAR pixels in adjacent time periods, use the least squares inverse calculation to obtain the deformation rate estimate, and continuously integrate the deformation rate estimate over time to obtain the deformation values ​​of all independent moments.

[0011] Optionally, in S1, the error terms in the GNSS displacement time series are divided into gross error terms, step terms and observation noise, and different processing strategies are used to perform error correction processing respectively, wherein:

[0012] For gross error items, they are directly eliminated after being identified by the mean positive or negative N times of the mean error;

[0013] For the step term, the deformation characteristic function is used to fit and obtain the function fitting result. Then, the difference between the function fitting result and the GNSS displacement time series is traversed twice from front to back and from back to front in chronological order. The time point of the step occurrence is determined according to the size of the difference value and the continuity of the change of the size, and repair is performed based on the time point of the step occurrence.

[0014] In view of the observation noise, the deformation characteristic function is used to perform least square fitting on the GNSS displacement time series, and then weighted processing is performed according to the preset weight threshold.

[0015] Optionally, in S2, the process of transferring the error-corrected GNSS displacement time series to the InSAR line of sight is as follows:

[0016] The starting time of the GNSS displacement time series is aligned with the starting time of the InSAR interferogram, and the E, N, and U displacement time series of the GNSS station are transferred to the line of sight according to the radar incident angle and the radar heading azimuth.

[0017] Optionally, in S2, the process of obtaining the GNSS spatial interpolation result is as follows:

[0018] Kriging interpolation is used to perform unbiased estimation for discrete points with uneven spatial distribution, and a GNSS spatial interpolation result with the same spatial resolution as the InSAR interferogram is obtained.

[0019] Optionally, in S3, the InSAR interferogram is screened based on the spatiotemporal baseline length and the number of valid pixels.

[0020] Optionally, in S3, the process of weighted fusion of the GNSS spatial interpolation result and the InSAR interferogram is as follows:

[0021] According to the difference between the GNSS spatial interpolation result and the InSAR interferogram that does not participate in the modeling, multiple weight combinations are circulated within the weight range to obtain the weight corresponding to the minimum error as the optimal weight. Based on the optimal weight, weighted fusion is performed to obtain the fused InSAR interferogram.

[0022] Optionally, in S4, the observation equation is constructed as follows:

[0023] Based on the fact that the displacement value at each InSAR pixel is the result of continuous accumulation of multiple uniform deformations in time, the observation equation is constructed in combination with the fused InSAR interferogram.

[0024] Optionally, in S4, the smoothing factor is determined using a grid search method.

[0025] In addition, the present invention also discloses a computer device, including a memory and a processor;

[0026] The memory is used to store a computer program executable on the processor;

[0027] The processor is used to implement the steps of the radar interference pattern processing method as described above when executing the computer program.

[0028] In addition, the present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the radar interference pattern processing method as described above are implemented.

[0029] The application of the technical solution of the present invention has the following beneficial effects:

[0030] The present invention discloses a radar interferogram processing method based on fusion correction of InSAR interferogram with GNSS spatial interpolation results. The spatial distribution of GNSS data and the displacement correlation of each station are taken into account in the process of data gridding through Kriging interpolation, so that the interpolation result is closer to the actual situation of data evolution. The present invention obtains the best GNSS and InSAR data weight ratio by comprehensively considering different weight combinations, and evaluates the quality of both GNSS spatial interpolation results and InSAR interferogram data through weighted fusion, thereby suppressing the influence of low-quality GNSS data on the fusion result. The accuracy of the correction result of the existing model depends on the spatial distribution of GNSS stations. When the error in the InSAR interferogram is manifested as a low-frequency signal with relatively gentle spatial variation and the spatial distribution of GNSS stations is relatively evenly covered in the error area, this method can achieve better results. However, when the distribution of GNSS stations is relatively sparse, it is difficult for the existing method to achieve high-precision InSAR interferogram error correction.

[0031] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 is an overall flow chart of a radar interference pattern processing method in a preferred embodiment of the present invention;

[0034] Figure 2 1 is a comparison diagram of the LOS displacement time series and model fusion results of the CLIM station and the NRRD station in the preferred embodiment of the present invention, wherein (1) represents the comparison diagram of the LOS displacement time series and model fusion results of the CLIM station, (2) represents the comparison diagram of the LOS displacement time series and model fusion results of the NRRD station, the abscissa represents the year date value, the ordinate represents the LOS displacement in mm, the black solid line represents the LOS displacement value of the GNSS station, and the gray dotted line with error bars is the InSAR deformation time series result obtained by fusion. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0036] Example:

[0037] Please refer to Figure 1 The embodiment of the present invention discloses a radar interferogram processing method, device and medium that fuses GNSS spatial interpolation results. The method takes into account the spatial distribution of GNSS data and the displacement correlation of each station based on Kriging interpolation, considers the accuracy comparison of GNSS spatial interpolation results and InSAR interferogram data based on relative weights, and provides a method, device and medium that fuses GNSS spatial interpolation results and InSAR interferograms, serving the needs of deformation monitoring projects for high-precision deformation monitoring results. The method includes the following steps:

[0038] S1: Acquire the GNSS displacement timing, perform error correction on the GNSS displacement timing, and obtain the GNSS displacement timing after error correction;

[0039] S2: The error-corrected GNSS displacement time series is transferred to the line of sight (LOS) direction of InSAR, and the GNSS displacement time series data in the line of sight direction is spatially interpolated to obtain the GNSS spatial interpolation result;

[0040] S3: Screen the InSAR interferogram, align, unwrap and weightedly fuse the GNSS spatial interpolation result and the InSAR interferogram in turn to obtain the fused InSAR interferogram;

[0041] S4: Construct the observation equation, use the smoothing operator to constrain the deformation rate of InSAR pixels in adjacent time periods, use the least squares inverse calculation to obtain the deformation rate estimate, and continuously integrate the deformation rate estimate over time to obtain the deformation values ​​of all independent moments.

[0042] Specifically, in S1, the error terms in the GNSS displacement time series are divided into gross error terms, step terms and observation noise, and different processing strategies are used to perform error correction respectively, where:

[0043] For gross error items, they are directly eliminated after being identified by the mean positive or negative N times of the mean error;

[0044] For the step term (the step term is a continuous numerical jump in the displacement time series caused by external event interference), the deformation characteristic function is used to fit and obtain the function fitting result. Then, the difference between the function fitting result and the GNSS displacement time series is traversed twice from front to back and from back to front in chronological order. The time point of the step occurrence is determined according to the size of the difference value and the continuity of the change of the size, and repair is performed based on the time point of the step occurrence. The expression is as follows:

[0045] D=a0+a1×t+a2×sin(2πf1t)+b2×cos(2πf1t)+α3×sin(2πf2t)+b3×cos(2πf2t)+a4×sin(2πf3t)+b4×cos(2πf3t)+ε;

[0046] Where D is the GNSS station displacement time series (D = D1, D2, ..., D m ), t is the observation time, a0 and a1 are the constant term coefficient and the velocity term coefficient respectively; a2 and b2 are the annual cycle term coefficients; a3 and b3 are the semi-annual cycle term coefficients; a4 and b4 are the seasonal cycle term coefficients; f1, f2 and f3 represent the annual cycle term frequency (1 / 365), the semi-annual cycle term frequency (1 / 182.5) and the seasonal cycle term frequency (1 / 91.25) respectively; ε is the residual error of the deformation characteristic function fitting, which obeys the normal distribution with a mean of 0;

[0047] In view of the observation noise, the deformation characteristic function is used to perform least square fitting on the GNSS displacement time series, and then weighted processing is performed according to the preset weight threshold. If the weight corresponding to the fitted deformation characteristic function is higher than the weight threshold, the observed data is directly used; if the fitting weight is lower than the weight threshold, the weighted average of the observed data and the function fitting result is used for replacement;

[0048]

[0049] In the formula, D' is the weighted processing result, D is the displacement observation value, is the fitting result of the deformation characteristic function, p is the fitting weight corresponding to the observed data, p1 is the error rejection weight threshold, p2 is the error detection weight threshold, and the weight threshold can be set according to the data quality. In this embodiment, the error rejection weight threshold p1 is set to 0.4, and the error detection weight threshold p2 is set to 0.8.

[0050] Specifically, in S2, the process of converting the error-corrected GNSS displacement time series to the InSAR line of sight is as follows:

[0051] The starting time of the GNSS displacement time series is aligned with the starting time of the InSAR interferogram, and the E, N, and U displacement time series of the GNSS station are transferred to the line of sight according to the radar incident angle θ and the radar heading azimuth α (starting from the north direction and positive in the clockwise direction).

[0052] Specifically, in S2, the process of obtaining the GNSS spatial interpolation result is as follows:

[0053] Based on the fact that the closer the spatial locations of GNSS stations are, the more similar their characteristic values ​​are, Kriging interpolation is used to perform unbiased estimation for discrete points with uneven spatial distribution, and the GNSS spatial interpolation result with the same spatial resolution as the InSAR interferogram is obtained.

[0054] Specifically, in S3, the InSAR interferograms are screened based on the spatiotemporal baseline length and the number of valid pixels.

[0055] Specifically, in S3, the process of weighted fusion of GNSS spatial interpolation results and InSAR interferograms is as follows:

[0056] According to the difference between the GNSS spatial interpolation result and the InSAR interferogram that does not participate in the modeling, multiple weight combinations are circulated within the weight range to obtain the weight corresponding to the minimum error as the optimal weight. Based on the optimal weight, weighted fusion is performed to obtain the fused InSAR interferogram.

[0057] Furthermore, in order to reduce the amount of computation of weight calculation, this embodiment first sets a relatively sparse set of weights, performs weighted fusion operations in sequence, determines the weight range corresponding to the minimum error, and then encrypts the second set of weights within the range for weighted fusion operations. The optimal weights finally selected are determined based on the minimum error. The expression of weighted fusion is as follows:

[0058] D fusion =P1*D GNSS +P2*D InSAR ;

[0059] Where D fusion , D GNSS , D InSAR They represent the result of weighted fusion, the result of GNSS spatial interpolation, and the result of InSAR interferogram unwrapping respectively. P1 and P2 represent the optimal weights of the two types of data. The specific values ​​are determined within multiple value ranges based on the minimum difference between the fusion result and the GNSS data.

[0060] Specifically, in S4, the process of constructing the observation equation is as follows:

[0061] Based on the fact that the displacement value at each InSAR pixel is the result of continuous accumulation of multiple uniform deformations in time, the observation equation is constructed by combining the fused InSAR interferogram. The expression of the observation equation is as follows:

[0062]

[0063] Among them, t represents the length of observation time, λ1 and λ2 represent time and space smoothing factors, and L s , L t represents the time and space smoothing operator, V represents the estimated deformation rate within the corresponding time span, It represents the LOS deformation value corresponding to the fused InSAR interferogram, 0 represents the zero matrix, and ∫Vdt represents the deformation value of all independent moments obtained by continuously integrating the deformation rate over time.

[0064] Specifically, in S4, the smoothing factor is determined by using a grid search method.

[0065] The method of this embodiment is used to conduct experiments to verify the accuracy and stability of the method of this embodiment, as follows:

[0066] The method of this embodiment is applied to a deformation monitoring project in a certain place. The GNSS single-day displacement time series data released by MAGNET and the Sentinel satellite interferogram released by LICSAR are used as experimental data. Some GNSS stations are selected not to participate in the fusion modeling process. The deformation rate estimation of the GNSS station to the LOS direction is compared with the model deformation rate result to verify the accuracy of the method in deformation rate estimation; the displacement time series of the GNSS station to the LOS direction is compared with the model displacement time series result to verify the stability of the method in revealing the continuous evolution of the InSAR pixel displacement time series.

[0067] like Figure 2 The figure shows the comparison of LOS displacement (black line) and fusion result (grey line) of CLIM and NRRD stations, where (1) shows the comparison of LOS displacement time series and model fusion result of CLIM station, (2) shows the comparison of LOS displacement time series and model fusion result of NRRD station. The horizontal axis shows the year date value, and the vertical axis shows LOS displacement in mm. The black solid line shows the LOS displacement value of GNSS station, and the grey dotted line with error bar is the InSAR deformation time series result obtained by fusion.

[0068] At both GNSS stations, the LOS direction showed an obvious continuous subsidence trend, with a subsidence rate of about 30 mm / yr. The InSAR deformation rate change trend and numerical value obtained by the fusion model are consistent with those of the GNSS station. The temporal evolution characteristics of the InSAR deformation time series can accurately match the LOS displacement time series of the GNSS station, and most of the differences are within 5 mm. The RMS values ​​of the errors of CLIM and NRRD are -1.7 mm and 5.1 mm, respectively, indicating that the model fusion results have good effects in both deformation rate estimation and displacement time series temporal evolution process interpretation. The gray vertical line represents the standard deviation of the time series processing results within the vicinity of the InSAR pixel point, reflecting the spatial distribution discreteness of the InSAR deformation time series results. As time goes by, the vertical line becomes longer and longer, indicating that the spatial distribution discreteness of the data increases, and the noise level of the displacement time series results becomes more significant, but the final numerical value is still around 10 mm, which will not have a significant impact on the settlement monitoring results. In addition, the fusion method proposed in this paper can effectively utilize the publicly released GNSS displacement time series and InSAR interferogram data, and has strong adaptability to the fusion of GNSS and InSAR data.

[0069] The embodiment of the present invention also discloses a computer device, including a memory and a processor;

[0070] The memory is used to store a computer program executable on the processor;

[0071] The processor is used to implement the steps of the above-mentioned radar interference pattern processing method when executing the computer program.

[0072] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0073] The computer device may be a computing device such as a mobile phone, a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory. For example, the computer device may also include an input / output device, a network access device, a bus, etc.

[0074] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0075] The memory can be used to store the computer program and / or module, and the processor implements the computer program by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0076] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0077] In addition, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned radar interference pattern processing method are implemented.

[0078] Compared with the prior art, the method of the present invention is based on the fusion correction of InSAR interferograms based on the GNSS spatial interpolation results. The spatial distribution of GNSS data and the displacement correlation of each station are taken into account through Kriging interpolation. The method of obtaining the best GNSS and InSAR data weight ratio by comprehensively considering different weight combinations suppresses the influence of low-quality GNSS data on the fusion results. The accuracy of the atmospheric error correction of InSAR interferograms with existing GNSS data depends on the spatial distribution of GNSS stations. When the error in the InSAR interferogram is manifested as a low-frequency signal with relatively gentle spatial variation and the spatial distribution of GNSS stations is relatively uniformly covered in the error area, the traditional method can achieve better results. However, when the distribution of GNSS stations is relatively sparse, it is difficult for the traditional method to achieve high-precision InSAR interferogram error correction.

[0079] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A radar interferogram processing method integrating GNSS spatial interpolation results, characterized in that: The following steps are involved: S1: Acquire the GNSS displacement timing, perform error correction on the GNSS displacement timing, and obtain the GNSS displacement timing after error correction; S2: The error-corrected GNSS displacement time series is transferred to the line-of-sight direction of InSAR, and the GNSS displacement time series data in the line-of-sight direction is spatially interpolated to obtain the GNSS spatial interpolation result; S3: Screen the InSAR interferogram, align, unwrap and weightedly fuse the GNSS spatial interpolation result and the InSAR interferogram in turn to obtain the fused InSAR interferogram; S4: Construct the observation equation, use the smoothing operator to constrain the deformation rate of InSAR pixels in adjacent time periods, use the least squares inverse calculation to obtain the deformation rate estimate, and continuously integrate the deformation rate estimate over time to obtain the deformation values ​​of all independent moments.

2. The radar interference pattern processing method according to claim 1, characterized in that: In S1, the error terms in the GNSS displacement time series are divided into gross error terms, step terms and observation noise, and different processing strategies are used to correct the errors respectively, where: For gross error items, they are directly eliminated after being identified by the mean positive or negative N times of the mean error; For the step term, the deformation characteristic function is used to fit and obtain the function fitting result. Then, the difference between the function fitting result and the GNSS displacement time series is traversed twice from front to back and from back to front in chronological order. The time point of the step occurrence is determined according to the size of the difference value and the continuity of the change of the size, and repair is performed based on the time point of the step occurrence. In view of the observation noise, the deformation characteristic function is used to perform least square fitting on the GNSS displacement time series, and then weighted processing is performed according to the preset weight threshold.

3. The radar interference pattern processing method according to claim 2, characterized in that: In S2, the process of converting the error-corrected GNSS displacement time series to the InSAR line of sight is as follows: The starting time of the GNSS displacement time series is aligned with the starting time of the InSAR interferogram, and the E, N, and U displacement time series of the GNSS station are transferred to the line of sight according to the radar incident angle and the radar heading azimuth.

4. The radar interference pattern processing method according to claim 3, characterized in that: In S2, the process of obtaining the GNSS spatial interpolation result is as follows: Kriging interpolation is used to perform unbiased estimation for discrete points with uneven spatial distribution, and a GNSS spatial interpolation result with the same spatial resolution as the InSAR interferogram is obtained.

5. The radar interference pattern processing method according to claim 4, characterized in that: In S3, the InSAR interferograms are screened based on the length of the spatiotemporal baseline and the number of valid pixels.

6. The radar interference pattern processing method according to claim 5, characterized in that: In S3, the process of weighted fusion of GNSS spatial interpolation results and InSAR interferograms is as follows: According to the difference between the GNSS spatial interpolation result and the InSAR interferogram that does not participate in the modeling, multiple weight combinations are circulated within the weight range to obtain the weight corresponding to the minimum error as the optimal weight. Based on the optimal weight, weighted fusion is performed to obtain the fused InSAR interferogram.

7. The radar interference pattern processing method according to claim 6, characterized in that: In S4, the process of constructing the observation equation is as follows: Based on the fact that the displacement value at each InSAR pixel is the result of continuous accumulation of multiple uniform deformations in time, the observation equation is constructed in combination with the fused InSAR interferogram.

8. The radar interference pattern processing method according to claim 7, characterized in that: In S4, the smoothing factor is determined using a grid search method.

9. A computer device, characterized in that: including memory and processor; The memory is used to store a computer program executable on the processor; The processor is used to implement the steps of the radar interference pattern processing method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the radar interference pattern processing method according to any one of claims 1 to 8 are implemented.

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