InSAR deformation precision monitoring method based on triangulation network guidance and prior regularization

Abstract

The invention discloses an InSAR deformation precision monitoring method based on triangulation network guidance and prior regularization. The method comprises the following steps: obtaining an interferometric phase diagram; performing high-coherence pixel screening; constructing a Delaunay triangulation network with elevation and distance constraints; obtaining residual terrain estimation values and deformation rate estimation values of all high-coherence pixels; constructing an optimization function of the augmented Lagrange function; solving a residual terrain, a differential deformation rate, a Lagrange multiplier vector and a deformation rate; and accurate elevation and deformation rate information of high-coherence pixels such as a digital elevation model and a deformation rate can be obtained. According to the method, the regularization inversion model based on the full-scene InSAR high-coherence pixel and the full-scene high-coherence pixel triangulation network equality constraint is constructed, the full-scene optimization of the inversion result can be realized, the phase noise space transmission is effectively inhibited, and the InSAR high-coherence pixel three-dimensional reconstruction and deformation rate inversion precision is further improved.

Description

Technical Field

[0001] The invention relates to a triangulated network guided and priori regularized InSAR deformation precision monitoring method, belonging to the technical field of interferometric synthetic aperture radar data processing. Background Art

[0002] Synthetic Aperture Radar (SAR) is an all-day, all-weather, active microwave imaging radar with the advantages of high width, high temporal and spatial resolution, and a certain penetration ability to the surface. SAR Interferometry (InSAR), developed on the basis of SAR, is an efficient frontier geodetic technology that can obtain continuous coverage of surface elevation and deformation information with high precision. Persistent scatterers InSAR (PS-InSAR) and distributed scatterers InSAR (DS-InSAR) are among the most advanced time-series InSAR technologies, which can perform high-precision topographic measurement and deformation monitoring of artificial targets such as urban buildings, roads and bridges, and natural scene targets such as bare soil. Its height measurement accuracy can reach sub-meter level, and its deformation monitoring accuracy can reach millimeter / year. It plays an important role in the elevation measurement and precise measurement of ground subsidence in cities, coastal islands and reefs.

[0003] After identifying robust high-coherence targets, PS-InSAR and DS-InSAR technologies construct Delaunay triangulations, use the maximum set coherence criterion to calculate the differential residual elevation and differential deformation rate of connected triangle edges edge by edge, and then solve the residual elevation and deformation rate based on the least squares method weighted by the set coherence coefficient. However, the differential residual elevation error and differential deformation rate error calculated edge by edge will propagate spatially, resulting in a large distribution range of the residual elevation and deformation rate finally solved. Therefore, the traditional method based on edge-by-edge calculation and then spatial solution is difficult to accurately invert the elevation terrain and deformation rate field. The regularization model of the spatial correlation prior of the deformation rate field provides the possibility for the suppression of spatially propagated noise. Therefore, a full-scene prior regularization model based on triangulation guidance can be constructed to suppress the spatial propagation effect of incoherent random phase noise and realize the one-step accurate inversion of the four-dimensional information of InSAR high-coherence pixels. Summary of the invention

[0004] The purpose of the present invention is to address the problem of large fluctuations in the elevation and deformation rate differences of adjacent scatterers caused by time-series InSAR incoherence noise, and propose an InSAR deformation precision monitoring method with triangulation guidance and prior regularization, which effectively suppresses the influence of random noise in low-coherence pixels on the estimation results, and achieves the purpose of accurately reconstructing three-dimensional terrain and deformation rate.

[0005] The above-mentioned purpose of the present invention is achieved by the following technical means:

[0006] The triangulated network guided and prior regularized InSAR deformation precision monitoring method includes the following steps:

[0007] Step 1: Obtain an interferometric phase image sequence, and use reference topographic data to dereference the topographic phase of the interferometric phase image sequence to obtain an interferometric phase map;

[0008] Step 2: Screening high coherence pixels of the interference phase image;

[0009] Step 3: Construct a Delaunay triangulation with elevation and distance constraints;

[0010] Step 4: Solve the maximum set coherence model to obtain the estimated value of the differential elevation between any connected high coherence pixels Estimation of differential deformation rate and the set coherence coefficient

[0011] Step 5: Use the weighted least squares method to obtain the residual terrain estimation value of all high coherence pixels and deformation rate estimate

[0012] Step 6: Construct the optimization function of the augmented Lagrangian function

[0013] Step 7: Use the alternating direction multiplier method to optimize and update the residual terrain h in the regularized inversion model of the full scene high coherence pixel triangulation equality constraint ε , differential deformation rate Lagrange multiplier vector y, and deformation rate v;

[0014] Step 8: Use the iteratively optimized residual terrain h obtained in step 7 ε and deformation rate v, as well as reference terrain data, to obtain an accurate digital elevation model (DEM) and the elevation and deformation rate information of highly coherent pixels such as deformation rate.

[0015] As described above, the elevation and distance constraints in step 3 are: the connecting edge of the elevation difference of adjacent high-coherent pixels is less than the corresponding set threshold, and the connecting edge distance of adjacent high-coherent pixels is less than the corresponding set threshold.

[0016] As mentioned above, the maximization set coherence model in step 4 is based on the following formula:

[0017]

[0018] in, is the set coherence coefficient, is the difference operator of two adjacent pixels x1 and x2, where x1 and x2 refer to two adjacent pixels. represents the differential interference phase of the xth high coherence pixel in the i-th interference phase image generated in step 1, and They represent the residual topography and linear deformation interference phase of the xth high coherence pixel in the i-th interference phase image generated in step 1, M represents the number of interference phase images, j is an imaginary unit, and The difference form of and for:

[0019]

[0020] Among them, b i With t i represents the vertical baseline and observation time of the i-th interferometric phase pattern, θ and λ represent the radar incident angle and wavelength, respectively. ε and v represent the residual terrain and deformation rate of the target respectively, and r is the distance from the satellite to the target.

[0021] As mentioned above in step 5: use the estimated value of the differential elevation of all connected high coherence pixels obtained in step 4 Arrange rows to form differential elevations The differential deformation rate estimates of all connected pixels obtained in step 4 are used Differential deformation rates are formed by row arrangement Coherence coefficient As the weight, the weighted least squares method is used to obtain the residual terrain estimation value of all high coherence pixels based on the following formula and deformation rate estimate

[0022]

[0023] in, A mapping matrix representing highly coherent pixels to connected pixel edges.

[0024] As mentioned above, the Lagrangian function is augmented in step 6 Based on the following formula:

[0025]

[0026] Where W is the diagonal matrix formed by the set coherence coefficients calculated by the connected high coherence pixels in step 4. The * after W indicates the calculation of elements between matrices of the same size. exp is the exponential operator. v is the deformation rate. h ε is the residual terrain, diag{} represents the vector diagonalization operator, Ψ represents the matrix composed of the differential interference phase of all high coherent connected pixels, B represents the matrix composed of all high coherent pixel baseline correlation beam domain elements, and the high coherent pixel baseline correlation beam domain elements are c represents the vector composed of all high coherent pixel time-correlated beam domain elements. The high coherent pixel time-correlated beam domain elements are A represents the triangulated network mapping matrix composed of highly coherent connected pixels, η represents the regularization parameter, is the square of the F norm, ||1 is the 1 norm, μ represents the penalty parameter of the constraint, y represents the Lagrange multiplier vector, <·,·> represents the inner product operation of the vector, Represents the 2-norm squared.

[0027] As mentioned above, in step 7, the regularized inversion model for optimizing and updating the equality constraints of the triangulated network of high-coherence pixels in the whole scene by using the alternating direction multiplier method includes the following steps:

[0028] Step 7.1: Substitute the residual terrain estimate obtained in step 5 into As the residual topography h ε The initial value of the differential deformation rate obtained in step 5 As the differential deformation rate The initial value of the deformation rate estimated in step 5 As the initial value of the deformation rate v, 0 is used as the initial value of the Lagrange multiplier vector y;

[0029] Step 7.2: The differential deformation rate obtained from the previous iterative optimization is The Lagrange multiplier vector y is input into the following formula to solve for the estimate of the residual terrain Updated value of:

[0030]

[0031] Then we can get the estimated residual terrain The updated value of

[0032] Step 7.3: The newly obtained estimated residual terrain As the residual terrain h updated in the current iteration ε, together with the deformation rate v and Lagrange multiplier vector y obtained from the previous iterative optimization, are substituted into the following formula to solve the estimated value of the differential deformation rate: Updated value of:

[0033]

[0034] Step 7.4: Substitute the estimated differential deformation rate obtained in step 7.3 into The updated value of is used as the differential deformation rate of the current iteration The differential deformation rate of the current iteration And the Lagrange multiplier vector y obtained in the previous iterative optimization is substituted into the following formula to obtain the updated value of the Lagrange multiplier vector As the Lagrange multiplier vector y for the current iteration optimization:

[0035]

[0036] Step 7.5: Substitute the latest estimate of the differential deformation rate obtained in step 7.3 into As the differential deformation rate of the current iteration optimization The differential deformation rate of the current iteration optimization Substitute the following formula and use the least squares method to solve the deformation rate The updated value of is used as the deformation rate v of the current iterative optimization.

[0037]

[0038] Step 7.6, iterative optimization executes steps 7.2-7.5 until the maximum number of iterations is reached or the residual topography h obtained from the previous and next two iterations is reached. ε , deformation rate v, Lagrange multiplier vector y, differential deformation rate The difference between the two iterations is less than the corresponding threshold, and the residual terrain h ε , Lagrange multiplier vector y, differential deformation rate Substitute the augmented Lagrangian function If the values ​​of are all less than the corresponding set threshold, the iteration is stopped and the residual terrain h after iterative optimization is output. ε and deformation rate v.

[0039] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned InSAR deformation precision monitoring method when executing the computer program.

[0040] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned InSAR deformation precision monitoring method.

[0041] A computer program product includes a computer program, which implements the steps of the above-mentioned InSAR deformation precision monitoring method when executed by a processor.

[0042] Compared with the prior art, the present invention has the following advantages:

[0043] 1. Accuracy. The present invention creatively constructs a regularized inversion model based on full-scene InSAR high-coherence pixels and full-scene high-coherence pixel triangulation equality constraints, which can achieve full-scene optimal inversion results and effectively suppress the spatial transmission of phase noise, thereby improving the accuracy of InSAR high-coherence pixel three-dimensional reconstruction and deformation rate inversion.

[0044] 2. Effectiveness. The method provided by the present invention is compared with the traditional method through measured satellite data and robustness analysis of key parameters, which verifies the convergence, effectiveness and superiority of the method and shows good application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flow chart of the present invention.

[0046] Figure 2 The four-dimensional information inversion result diagram of InSAR high coherence pixels. Among them, (a) is a schematic diagram of the DEM of the traditional edge-by-edge differential calculation and then weighted least squares inversion, (b) is a schematic diagram of the deformation rate estimation value of the traditional edge-by-edge differential calculation and then weighted least squares inversion; (c) is a schematic diagram of the DEM inverted based on Example 2 of the present invention, and (d) is a schematic diagram of the deformation rate estimation value inverted based on Example 2 of the present invention.

[0047] Figure 3 : It is the uncertainty result diagram of the present invention. Among them, (a) is the uncertainty diagram of the elevation estimation of Example 2 of the present invention, and (b) is the uncertainty diagram of the deformation rate estimation of Example 2 of the present invention.

[0048] Figure 4 The robustness analysis and running time analysis diagrams of the iteration number of the present invention are shown in Figure 1. (a) The linear variation diagram of the augmented Lagrange function with respect to the residual terrain and the differential deformation rate with the iteration number; (b) The linear variation diagram of the running time of the augmented Lagrange function with the iteration number. DETAILED DESCRIPTION

[0049] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail below in conjunction with embodiments. The embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0050] Embodiment 1:

[0051] like Figure 1 As shown in FIG. 1 , the triangulated network guided and prior regularized InSAR deformation precision monitoring method comprises the following steps:

[0052] Step 1: perform image registration on the multi-temporal SAR images to obtain the accurately registered primary and secondary phase images, and then perform conjugate multiplication on the primary and secondary phase images to generate an interferometric phase image sequence; use the coarse-precision reference terrain data to dereference the terrain phase of the interferometric phase image sequence, and then obtain the differential interferometric stack data of the residual terrain and the surface deformation component, that is, the interferometric phase map;

[0053] Step 2, using high coherence pixel screening criteria such as amplitude deviation and coherence threshold to screen the interference phase image and robustly identify high coherence pixels with uneven and sparse distribution;

[0054] Step 3: Using the unevenly sparsely distributed high-coherence pixels in the identified scene, a Delaunay triangulation with elevation and distance constraints is constructed. The constraint criteria are: the connecting edge of the elevation difference between adjacent high-coherence pixels is less than 50 meters, and the connecting edge distance between adjacent high-coherence pixels is less than 1 kilometer.

[0055] Step 4: Perform phase difference on the highly coherent pixels connected by the Delaunay triangulation edge, and obtain the set coherence coefficient of adjacent highly coherent pixels based on the maximum set coherence model. Estimated value of differential elevation by

[0056] and the estimated value of the differential deformation rate The maximum set coherence model is based on the following formula:

[0057]

[0058] In formula (1), is the set coherence coefficient, is the difference operator of two adjacent pixels x1 and x2, where x1 and x2 refer to two adjacent pixels. represents the differential interference phase of the xth high coherence pixel in the i-th interference phase image generated in step 1, then They are and They represent the residual topography and linear deformation interference phase of the xth high coherence pixel in the i-th interference phase image generated in step 1, M represents the number of interference phase images, j is an imaginary unit, and The difference form of and It can be expressed as:

[0059]

[0060] Among them, b i With t i represents the vertical baseline and observation time of the i-th interferometric phase pattern, θ and λ represent the radar incident angle and wavelength, respectively. ε and v represent the residual terrain and deformation rate of the target, respectively, and r is the distance from the satellite to the target. The parameters of the maximum set coherence model can be quickly solved using the grid search method under the CPU parallel and GPU computing strategy, and then the estimated value of the differential elevation between any connected high coherence pixels is obtained. Estimation of differential deformation rate and the set coherence coefficient

[0061] Step 5: Use the estimated values ​​of the differential elevation of all connected high-coherence pixels obtained in step 4 Arrange rows to form differential elevations The differential deformation rate estimates of all connected pixels obtained in step 4 are used Differential deformation rates are formed by row arrangement Coherence coefficient As the weight, the weighted least squares method of formula (3) is used to obtain the residual terrain estimation value of all high coherence pixels. and deformation rate estimate

[0062]

[0063] in, Represents the mapping matrix from high coherence pixels to connected pixel edges. The residual terrain estimation value of all high coherence pixels can be obtained by formula (3): and deformation rate estimate

[0064] Step 6: Based on the prior information of the spatial correlation of the deformation rates of adjacent pixels, a regularized inversion model with equality constraints for the triangulated network of highly coherent pixels in the entire scene is constructed:

[0065]

[0066] Where W is the diagonal matrix formed by the set coherence coefficients calculated by the connected high coherence pixels in step 4. The * after W indicates the calculation of elements between matrices of the same size. exp is the exponential operator. v is the deformation rate. h εis the residual terrain, diag{} represents the vector diagonalization operator, Ψ represents the matrix composed of the differential interference phases of all highly coherent connected pixels, Represents the mapping matrix from highly coherent pixels to connected pixel edges Respectively with deformation rate v, residual topography h ε , matrix Ψ performs matrix multiplication operation, B represents all high coherence pixel baseline correlation beam domain elements (i.e., ), c represents the time-correlated beam domain elements of all high-coherence pixels (i.e., ), A represents the triangulated network mapping matrix composed of highly coherent connected pixels, η represents the regularization parameter, is the square of the F norm,

[0067] 1 is the 1 norm. The equality constraint optimization problem (4) can be transformed into an augmented Lagrange function The optimization function

[0068]

[0069] Among them, μ represents the penalty parameter of the constraint, y represents the Lagrange multiplier vector, and <·,·> represents the inner product operation of the vector. Represents the 2-norm squared.

[0070] Step 7: The residual terrain estimate obtained in step 5 is As the residual topography h ε The initial value of the differential deformation rate obtained in step 5 As the differential deformation rate The initial value of the deformation rate estimated in step 5 As the initial value of the deformation rate v, 0 is used as the initial value of the Lagrange multiplier vector y, and then the alternating direction multiplier method is used to optimize and update the residual terrain h in the regularized inversion model of the full scene high coherence pixel triangulation equality constraint. ε , differential deformation rate Lagrange multiplier vector y, and deformation rate v.

[0071] In step 7 as described above, the alternating direction multiplier method is used to optimize and update the residual terrain h in the regularized model guided by the triangulation network. ε , differential deformation rate Lagrange multiplier vector y, deformation rate v parameter, the specific steps are as follows:

[0072] Step 7.1: Substitute the residual terrain estimate obtained in step 5 into As the residual topography h εThe initial value of the differential deformation rate obtained in step 5 As the differential deformation rate The initial value of the deformation rate estimated in step 5 As the initial value of the deformation rate v, 0 is used as the initial value of the Lagrange multiplier vector y and is input into the optimization problems of the following steps 7.2 and 7.3 to start the first parameter iterative optimization.

[0073] Step 7.2: The differential deformation rate obtained from the previous iterative optimization is The Lagrange multiplier vector y is used as the input of the optimization problem (6) to solve the estimated value of the residual terrain Updated value of:

[0074]

[0075] Then we can get the estimated residual terrain The updated value of .

[0076] Step 7.3: The newly obtained estimated residual terrain As the residual terrain h updated in the current iteration ε , the deformation rate v and Lagrange multiplier vector y obtained in the previous iterative optimization are used as the input of the optimization problem of formula (7) to solve the estimated value of the differential deformation rate Updated value of:

[0077]

[0078] Then we can get the estimated value of differential deformation rate The updated value of .

[0079] Step 7.4: Substitute the estimated differential deformation rate obtained in step 7.3 into The updated value of is used as the differential deformation rate of the current iteration The differential deformation rate of the current iteration The Lagrange multiplier vector y obtained in the previous iterative optimization is used as the input of formula (8) to obtain the updated value of the Lagrange multiplier vector As the Lagrange multiplier vector y for the current iteration optimization:

[0080]

[0081] Step 7.5: Substitute the latest estimate of the differential deformation rate obtained in step 7.3 into As the differential deformation rate of the current iteration optimization The differential deformation rate of the current iteration optimization As the input of equation (9), the deformation rate is solved by the least squares method The updated value of is used as the deformation rate v of the current iterative optimization.

[0082]

[0083] Step 7.6, iterative optimization executes steps 7.2-7.5 until the maximum number of iterations is reached or the residual topography h obtained from the previous and next two iterations is reached. ε , deformation rate v, Lagrange multiplier vector y, differential deformation rate The difference between the two iterations is less than the corresponding set threshold, and the residual terrain h ε , Lagrange multiplier vector y, differential deformation rate Substituting into equation (5) to calculate the augmented Lagrange function If the values ​​of are all less than the corresponding set threshold, the iteration is stopped and the residual terrain h after iterative optimization is output. ε and deformation rate v.

[0084] Step 8: Use the iteratively optimized residual terrain h obtained in step 7 ε and deformation rate v, plus the coarse-precision reference terrain data input in step 1, to obtain the precise elevation and deformation rate information of the high-coherence pixels.

[0085] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0086] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0087] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0088] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0089] Embodiment 2:

[0090] The effectiveness of the InSAR deformation precision monitoring method with triangulation guidance and prior regularization described in Example 1 is verified by using 31 real data of the Capital International Airport area acquired by TerraSAR-X / TanDEM-X. The acquisition time and baseline parameter information of the 31 TerraSAR-X / TanDEM-X images are shown in Table 1. Table 1 is a table of the acquisition time and baseline parameters of the 31 TerraSAR-X / TanDEM-X images.

[0091] Table 1

[0092]

[0093]

[0094] Under the same PS-InSAR data processing flow, the processing results of the traditional edge-by-edge differential calculation followed by weighted least squares method are compared with the results of the triangulated network guided and prior regularized full-scene inversion method of the present invention, illustrating the advantages of the method proposed in the present invention. Figure 2 The four-dimensional information inversion result diagram of InSAR high coherence pixels. Among them, (a) is a schematic diagram of the DEM of the traditional edge-by-edge differential calculation and then weighted least squares inversion, (b) is a schematic diagram of the deformation rate estimation value of the traditional edge-by-edge differential calculation and then weighted least squares inversion; (c) is a schematic diagram of the DEM inverted based on Example 2 of the present invention, and (d) is a schematic diagram of the deformation rate estimation value inverted based on Example 2 of the present invention. The triangulated network guided and prior regularized full-scene inversion method of the present invention has a smaller elevation and deformation rate distribution range, indicating that the spatial error transmission is effectively suppressed. Figure 3 It is the uncertainty result diagram of the present invention. Among them, (a) is the uncertainty diagram of the elevation estimation of Example 2 of the present invention, and (b) is the uncertainty diagram of the deformation rate estimation of Example 2 of the present invention. It can be seen from (a) that 99% of the uncertainty of the elevation estimation of the method of the present invention is within the range of 1 meter, and it can be seen from (b) that the uncertainty of the deformation rate estimation of the present invention is within the range of 1 mm / year, which shows the effectiveness of the present invention. Figure 4 The robustness analysis and running time analysis diagrams of the number of iterations of the present invention are shown in Figure 1. (a) is a linear variation diagram of the augmented Lagrange function with respect to the residual terrain and the differential deformation rate with the number of iterations; (b) is a linear variation diagram of the running time of the augmented Lagrange function with respect to the number of iterations. It can be seen that with the increase in the number of iterations, the augmented Lagrange function with respect to the differential residual elevation and the differential deformation rate first slows down and then remains unchanged, and the running time changes linearly with the number of iterations, indicating the iterative convergence of the triangulated network-guided and a priori regularized InSAR deformation precision monitoring method, and fast convergence results can be obtained with 2 iterations.

[0095] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A triangulated network guided and prior regularized InSAR deformation precision monitoring method, characterized in that: The following steps are involved: Step 1: Obtain an interferometric phase image sequence, and use reference topographic data to dereference the topographic phase of the interferometric phase image sequence to obtain an interferometric phase map; Step 2: Screening high coherence pixels of the interference phase image; Step 3: Construct a Delaunay triangulation with elevation and distance constraints; Step 4: Solve the maximum set coherence model to obtain the estimated value of the differential elevation between any connected high coherence pixels Estimation of differential deformation rate and the set coherence coefficient Step 5: Use the weighted least squares method to obtain the residual terrain estimation value of all high coherence pixels and deformation rate estimate Step 6: Construct the optimization function of the augmented Lagrangian function Step 7: Use the alternating direction multiplier method to optimize and update the residual terrain h in the regularized inversion model of the full scene high coherence pixel triangulation equality constraint ε , differential deformation rate Lagrange multiplier vector y, and deformation rate v; Step 8: Use the iteratively optimized residual terrain h obtained in step 7 ε and deformation rate v, as well as reference terrain data, to obtain an accurate digital elevation model (DEM) and the elevation and deformation rate information of highly coherent pixels such as deformation rate.

2. The triangulated network guided and prior regularized InSAR deformation precision monitoring method according to claim 1, characterized in that: The elevation and distance constraints in step 3 are: the connecting edge of the elevation difference of adjacent high-coherence pixels is less than the corresponding set threshold, and the connecting edge distance of adjacent high-coherence pixels is less than the corresponding set threshold.

3. The triangulated network guided and prior regularized InSAR deformation precision monitoring method according to claim 1, characterized in that: The maximization set coherence model in step 4 is based on the following formula: in, is the set coherence coefficient, is the difference operator of two adjacent pixels x1 and x2, where x1 and x2 refer to two adjacent pixels. represents the differential interference phase of the xth high coherence pixel in the i-th interference phase image generated in step 1, and They represent the residual topography and linear deformation interference phase of the xth high coherence pixel in the i-th interference phase image generated in step 1, M represents the number of interference phase images, j is an imaginary unit, and The difference form of and for: Among them, b i With t i represents the vertical baseline and observation time of the i-th interferometric phase pattern, θ and λ represent the radar incident angle and wavelength, respectively. ε and v represent the residual terrain and deformation rate of the target respectively, and r is the distance from the satellite to the target.

4. The triangulated network guided and prior regularized InSAR deformation precision monitoring method according to claim 3, characterized in that: In step 5: using the estimated values ​​of the differential elevation of all connected high coherence pixels obtained in step 4 Arrange rows to form differential elevations The differential deformation rate estimates of all connected pixels obtained in step 4 are used Differential deformation rates are formed by row arrangement Coherence coefficient As the weight, the weighted least squares method is used to obtain the residual terrain estimation value of all high coherence pixels based on the following formula and deformation rate estimate in, A mapping matrix representing highly coherent pixels to connected pixel edges.

5. The triangulated network guided and prior regularized InSAR deformation precision monitoring method according to claim 4, characterized in that: The augmented Lagrangian function in step 6 Based on the following formula: Where W is the diagonal matrix formed by the set coherence coefficients calculated by the connected high coherence pixels in step 4. The * after W indicates the calculation of elements between matrices of the same size. exp is the exponential operator. v is the deformation rate. h ε is the residual terrain, diag{} represents the vector diagonalization operator, Ψ represents the matrix composed of the differential interference phase of all high coherent connected pixels, B represents the matrix composed of all high coherent pixel baseline correlation beam domain elements, and the high coherent pixel baseline correlation beam domain elements are c represents the vector composed of all high coherent pixel time-correlated beam domain elements. The high coherent pixel time-correlated beam domain elements are A represents the triangulated network mapping matrix composed of highly coherent connected pixels, η represents the regularization parameter, is the square of the F norm, ||1 is the 1 norm, μ represents the penalty parameter of the constraint, y represents the Lagrange multiplier vector, <·,·> represents the inner product operation of the vector, Represents the 2-norm squared.

6. The triangulated network guided and prior regularized InSAR deformation precision monitoring method according to claim 5, characterized in that: In step 7, the regularized inversion model of optimizing and updating the equality constraint of the triangulated network of high coherent pixels in the whole scene by using the alternating direction multiplier method includes the following steps: Step 7.1: Substitute the residual terrain estimate obtained in step 5 into As the residual topography h ε The initial value of the differential deformation rate obtained in step 5 As the differential deformation rate The initial value of the deformation rate estimated in step 5 As the initial value of the deformation rate v, 0 is used as the initial value of the Lagrange multiplier vector y; Step 7.2: The differential deformation rate obtained from the previous iterative optimization is The Lagrange multiplier vector y is input into the following formula to solve for the estimate of the residual terrain Updated value of: Then we can get the estimated residual terrain The updated value of Step 7.3: The newly obtained estimated residual terrain As the residual terrain h updated in the current iteration ε , together with the deformation rate v and Lagrange multiplier vector y obtained from the previous iterative optimization, are substituted into the following formula to solve the estimated value of the differential deformation rate: Updated value of: Step 7.4: Substitute the estimated differential deformation rate obtained in step 7.3 into The updated value of is used as the differential deformation rate of the current iteration The differential deformation rate of the current iteration And the Lagrange multiplier vector y obtained in the previous iterative optimization is substituted into the following formula to obtain the updated value of the Lagrange multiplier vector As the Lagrange multiplier vector y for the current iteration optimization: Step 7.5: Substitute the latest estimate of the differential deformation rate obtained in step 7.3 into As the differential deformation rate of the current iteration optimization The differential deformation rate of the current iteration optimization Substitute the following formula and use the least squares method to solve the deformation rate The updated value of is used as the deformation rate v of the current iterative optimization. Step 7.6, iterative optimization executes steps 7.2-7.5 until the maximum number of iterations is reached or the residual topography h obtained from the previous and next two iterations is reached. ε , deformation rate v, Lagrange multiplier vector y, differential deformation rate The difference between the two iterations is less than the corresponding threshold, and the residual terrain h ε , Lagrange multiplier vector y, differential deformation rate Substitute the augmented Lagrangian function If the values ​​of are all less than the corresponding set threshold, the iteration is stopped and the residual terrain h after iterative optimization is output. ε and deformation rate v.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the InSAR deformation precision monitoring method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the InSAR deformation precision monitoring method according to any one of claims 1 to 6 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the InSAR deformation precision monitoring method described in any one of claims 1 to 6 are implemented.

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