Cross-sensor long-time-sequence InSAR deformation monitoring method and device, medium and product

Abstract

The invention discloses a cross-sensor long-time-sequence InSAR deformation monitoring method and device, a medium and a product, and relates to the field of synthetic aperture radar interferometry, the method comprises the following steps: obtaining image data sets of a plurality of SAR sensors in a monitoring area, and sequentially carrying out differential interference and time sequence deformation calculation to obtain an LOS direction deformation result; geocoding all results to unify a coordinate system; screening high-coherence point targets and constructing an observation geometry unified analysis model; time information is obtained by combining the images, and a long-time-sequence earth surface deformation resolving function model after observation geometric correction is established; and further introducing a truncated singular value decomposition operator, constructing a cross-sensor long-time-sequence deformation resolving analysis model, and finally resolving to obtain a high-precision and long-time-span earth surface deformation product in the monitoring area. According to the method, the problems of rank deficiency and solution failure caused by time shortage and observation geometric difference in multi-platform InSAR data joint solution are effectively solved, and the precision and applicability of long-time-sequence deformation monitoring are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of synthetic aperture radar interferometry, and in particular to a cross-sensor long-time-series InSAR deformation monitoring method, device, medium and product. BACKGROUND

[0002] In recent years, with the development of space-borne synthetic aperture radar (SAR) sensors and time-series interferometric SAR (InSAR / TS-InSAR) technology, InSAR technology has become one of the important tools for high-precision ground deformation monitoring due to its advantages of surface measurement, all-weather, non-contact, high resolution, high precision, etc., and has important application value and scientific significance in understanding the spatio-temporal distribution and evolution of the overall ground deformation field and geophysical mechanism modeling interpretation. In view of the current rich historical SAR archive data and continuous reception of new data, it has important scientific value and broad application prospects to jointly solve the historical ground deformation and its future trend by combining massive data of multiple SAR sensors. However, there are significant differences in observation parameters between different sensors and different image sets, which makes it difficult to interfere and is a bottleneck problem restricting the joint observation of long-time-series ground by multiple sensor SAR data sets, hindering the production and application of TS-InSAR ground deformation monitoring products.

[0003] The current TS-InSAR technology based on a single sensor mainly uses the SAR data set obtained by a single sensor to solve the deformation time series of the monitoring area within a certain time range, and has become mature. Although the single-sensor TS-InSAR technology can basically meet the needs of short-term ground deformation monitoring, with the increasing demand of the scientific community and business departments for revealing and backtracking analysis of long-term ground deformation evolution in recent years, the overall high-precision solution of long-time-series ground deformation by cross-sensor InSAR has broad application prospects. Due to the significant differences in imaging geometry parameters and acquisition time of different sensors, the combination of multiple sensor InSAR data sets for long-time-series deformation solution needs to solve the problems of observation geometry difference and rank deficiency of time series solution equation in cross-sensor time series deformation solution. This leads to the current TS-InSAR technology being difficult to solve long-time-series ground deformation by combining multiple platform data sets. Due to the lack of long-time-series InSAR deformation monitoring technology, there are few related cases reported and applied.

[0004] Therefore, there is an urgent need for a long-time-series InSAR deformation monitoring method that can effectively fuse multi-source SAR data, overcome time gaps and observation geometry differences, and have mathematical robustness. SUMMARY

[0005] The application aims to provide a cross-sensor long-time-series InSAR deformation monitoring method, device, medium and product method, device, medium and product, which can realize seamless fusion of multi-platform SAR data and high-precision long-time-series deformation inversion by constructing an observation geometry unified model and a robust solving model based on truncated singular value decomposition.

[0006] To achieve the above-mentioned purpose, the application provides the following solutions. In a first aspect, the application provides a cross-sensor long-time-series InSAR deformation monitoring method, comprising: obtaining a SAR image data set observed by a plurality of SAR sensors in a monitoring area; performing differential interference processing and time-series deformation solving on the SAR image data set by using a time-series InSAR algorithm to obtain radar line-of-sight (LOS) deformation results of high-coherence-point targets in a monitoring period corresponding to each sensor; unifying the deformation results of each sensor; unifying the deformation results of each sensor geocoding the deformation results and the corresponding parameter information files to the same geographical coordinate system defined by an external DEM; based on the unified coordinates filtering high-coherence-point targets with long-term stable surfaces in the monitoring area according to surface interference coherence, and constructing an observation geometry unified analytical model according to the incidence angles and flight azimuth angles of each high-coherence point; constructing a long-time-series surface deformation solving function model of the high-coherence-point targets in a specific direction based on the observation geometry correction and the acquisition time information of each SAR image data set; introducing a truncated singular value decomposition (TSVD) operator to construct a cross-sensor long-time-series deformation solving analytical model, and robustly solving the solving function model; jointly applying the observation geometry unified analytical model, the solving function model and the cross-sensor long-time-series deformation solving analytical model to all high-coherence-point targets to solve and output a long-time-series surface deformation product of the monitoring area.

[0007] Optionally, the deformation result is a projection of the real three-dimensional displacement of the surface in the direction of the line of sight of the sensor, and the specific expression is as follows: ; wherein, is the deformation result; is the deformation result; and respectively represent the radar incidence angle and the flight azimuth angle of the SAR sensor at the corresponding high-coherence-point position; represents the vertical deformation component of the corresponding high-coherence-point position.​ This represents the east-west deformation component corresponding to the highly coherent point. This represents the north-south deformation component corresponding to the high coherence point.

[0008] Optionally, the parameter information file includes: the location information of each highly coherent point target in each sensor dataset and the corresponding specific observation geometric parameter file, including the incident angle parameter file, the flight azimuth angle parameter file, the solution residual file, and the coherence file.

[0009] Optionally, the selection of the high-coherence point target is based on the amplitude deviation index. and composite coherence index To be determined jointly; The method for calculating the amplitude deviation index is as follows: , and These are the mean and standard deviation of the amplitude of a given pixel, respectively. The method for calculating the composite coherence index is as follows: , To calculate the number of homogeneous pixels surrounding the pixel to be calculated, and The first to form an interference pair and The composite phase value of the scene image at that pixel location.

[0010] Optionally, based on unified coordinates By combining the deformation results and their corresponding parameter information files with the surface interferometry of the monitoring area, long-term stable high-coherence target points on the surface are selected. Based on the incident angle and flight azimuth angle at each high-coherence point, a unified analytical model of observation geometry is constructed, specifically including: When there are within the same observation period S ,and When there are two observations with significant differences, the observation geometric unified analytical model is: ; in, For the first i The deformation obtained from the observation and These represent the radar incident angle and flight azimuth angle of the SAR sensor at the corresponding high coherence point, respectively. This represents the vertical deformation component corresponding to the highly coherent point. This represents the east-west deformation component corresponding to the highly coherent point. This represents the north-south deformation component corresponding to the high coherence point; When there are only two observations with different imaging geometries within a certain observation period, the unified analytical model for observation geometry is: ; When only one sensor is observing during a certain observation period, the unified analytical model for observation geometry is: .

[0011] Optionally, the vertical deformation after observation geometry unification can be used. For example, the expression of the long-term surface deformation solution function model is as follows: ; in, The coefficient matrix is ​​composed of observations from multiple sensors; The deformation rate between all adjacent observations to be solved; For generated from multi-sensor datasets Vertical deformation results of multiple temporal interference pairs.

[0012] Optionally, the vertical deformation after observation geometry unification can be used. For example, the expression of the analytical model for solving long-term deformation across sensors is as follows: ; in, The deformation rate between all adjacent observations to be solved; Coefficient matrix The truncated singular value decomposition matrix; For generated from multi-sensor datasets Vertical deformation results of multiple temporal interference pairs; and These are the left singular vector and the right singular vector, respectively, and they are orthogonal matrices; To truncate the operator, middle The subsequent diagonal elements are 0; r Let be the rank of the matrix; These are the singular values ​​at the diagonal elements. This indicates transpose.

[0013] In a second aspect, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the cross-sensor long-time-series InSAR deformation monitoring method described in any one of the above.

[0014] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the cross-sensor long-time-series InSAR deformation monitoring method described above.

[0015] In a fourth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the cross-sensor long-time-series InSAR deformation monitoring method of any one of the above.

[0016] According to the specific embodiments provided in the present application, the present application has the following technical effects: The present application provides a cross-sensor long-time-series InSAR deformation monitoring method, device, medium and product. Firstly, the present application theoretically breaks through the technical bottleneck of multi-sensor InSAR long-time-series surface deformation joint solution, proposes a unified imaging geometry model for multi-platform InSAR observation, and perfects the multi-platform InSAR long-time-series deformation fine monitoring theory. Secondly, compared with the existing time-series InSAR technology, the present application can establish a long-time-series surface deformation solution function model and an analytical model based on a truncated singular value decomposition operator according to the time distribution characteristics of the multi-platform data set, realize a more rigorous mathematical multi-sensor long-time-series deformation rigorous solution, and the cross-platform data set monitoring scheme is more flexible, which significantly improves the time span of the time-series InSAR surface deformation monitoring. Finally, with the continuous improvement of the types and quantity of SAR sensors and the InSAR monitoring demand, the cross-sensor long-time-series InSAR deformation monitoring method provided by the present application will provide theoretical and technical support for the full use of massive SAR data, promote the perfection and development of InSAR technology theory, stimulate the application potential of SAR data and InSAR technology in the field of surface deformation monitoring, and has great scientific and engineering value and significance. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 A flowchart of a cross-sensor long-time-series InSAR deformation monitoring method provided by an embodiment of the present application is shown in the figure. Figure 2 A schematic diagram of the time baseline and spatial baseline distribution of a multi-sensor SAR data set simulated by an embodiment of the present application is shown in the figure. Figure 3 A schematic diagram of the surface time-series deformation and the time-series deformation solved by the traditional least square operator simulated by an embodiment of the present application is shown in the figure. Figure 4 A schematic diagram of the surface time-series deformation and the time-series deformation solved by the method provided by the present application simulated by an embodiment of the present application is shown in the figure. Figure 5A structural schematic diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0020] The above purposes, features and advantages of the present application will be more apparent and understandable. The present application will be described in further detail below with reference to the drawings and specific embodiments.

[0021] In an exemplary embodiment, as shown in Figure 1 A cross-sensor long-time-series InSAR deformation monitoring method is provided, which is executed by a computer device, specifically, can be executed by a terminal or a server, or both of them. In the embodiments of the present application, the method comprises the following steps: Step 1: Obtain a SAR image data set observed by multiple SAR sensors in a monitoring area.

[0022] Wherein, the number of sensors is greater than or equal to 2.

[0023] Step 2: Perform differential interference processing and time-series deformation calculation on the SAR image data set using a time-series InSAR algorithm to obtain radar line-of-sight (LOS) deformation results of high-coherence point targets in the monitoring period corresponding to each sensor.

[0024] Specifically, a SAR image data set observed by M SAR sensors (M≥2) in a monitoring area is obtained, and differential interference processing and time-series deformation calculation are performed on each data set using a time-series InSAR algorithm to obtain radar line-of-sight (LOS) one-dimensional deformation time-series results of high-coherence point targets in the monitoring period corresponding to each sensor. The LOS deformation is the projection of the real three-dimensional displacement (vertical U, east-west E, north-south N) of the ground in the direction of the sensor line of sight.

[0025] Wherein, the LOS deformation result is a deformation time series of the corresponding monitoring period of multiple SAR sensors, which is a one-dimensional projection of the three-dimensional deformation of the ground along the vertical (U), east-west (E) and north-south (N) real displacement directions along the LOS direction of the high-coherence point targets in the corresponding period, represented as: ; In the formula, and​ These represent the radar incident angle and flight azimuth angle of the SAR sensor at the corresponding high coherence point, respectively. This represents the vertical deformation component corresponding to the highly coherent point. This represents the east-west deformation component corresponding to the highly coherent point. This represents the north-south deformation component corresponding to the high coherence point.

[0026] Step 3: Connect each sensor The deformation results and their corresponding parameter information files are uniformly geocoded and converted to the geographic coordinate system defined by the same external DEM.

[0027] Step 4: Based on unified coordinates By combining the deformation results and their corresponding parameter information files with the surface interferometry coherence within the monitoring area, long-term stable high coherence point targets on the surface are screened, and a unified analytical model of observation geometry is constructed based on the incident angle and flight azimuth angle at each high coherence point.

[0028] The parameter information file includes: the location information of each highly coherent target in each sensor dataset and the corresponding specific observation geometric parameter file, including the incident angle parameter file, the flight azimuth angle parameter file, the solution residual file, and the coherence file.

[0029] Among them, the selection of high-coherence point targets is based on the selection of high-coherence point targets within each sensor dataset, according to the amplitude deviation index of each observation point. and composite coherence index To be determined jointly; The method for calculating the amplitude deviation index is as follows: , and These are the mean and standard deviation of the amplitude of a given pixel, respectively. The method for calculating the composite coherence index is as follows: , To calculate the number of homogeneous pixels surrounding the pixel to be calculated, and The first to form an interference pair and The composite phase value of the scene image at that pixel location.

[0030] The method for constructing a unified analytical model of observation geometry firstly relies on the flight characteristics of SAR satellites along the North and South Poles and side-looking imaging characteristics. It then statistically analyzes the differences and patterns in observation geometry corresponding to high-coherence points in various sensor datasets. Based on this, a unified analytical model of observation geometry for high-coherence point targets is established, as detailed below: When there are S (S>2) observations with significant differences within the same observation period, the established unified analytical model for observation geometry is as follows: ; wherein, is the deformation obtained from the i th observation, and denote the radar incidence angle and flight azimuth angle of the SAR sensor at the corresponding high-coherence point location, respectively; denotes the vertical deformation component of the corresponding high-coherence point location; denotes the east-west direction deformation component of the corresponding high-coherence point location; denotes the north-south direction deformation component of the corresponding high-coherence point location; When there are only two observations with different imaging geometries in a certain observation time period, such as ascending and descending track observations, the above model ignores the contribution of the north-south direction deformation component in each LOS direction observation, and the observation geometry unified analytical model is: ; When there is only one sensor observation in a certain observation time period, the above model simultaneously ignores the contributions of the north-south direction and east-west direction deformation components in each LOS direction observation, and the observation geometry unified analytical model is: .

[0031] Step 5: Based on the acquisition time information of each SAR image data set, a long-time sequence surface deformation calculation function model of the observation geometry corrected high-coherence point target in a specific direction is constructed.

[0032] Suppose that the multi-sensor data set contains K+1 SAR images with different acquisition times, L multi-temporal interferometric pairs are generated, and for multi-sensor deformation in a certain direction after observation geometry unification, a long-time sequence deformation calculation model is established, taking the vertical direction as an example: ; wherein, is a coefficient matrix composed of multi-sensor observations, each row identifies the time interval between adjacent two SAR images in the multi-sensor image set, and each column identifies the observation of an interferometric pair; for the coefficient value of each row, the value in the time interval of the interferometric pair is the number of days in the interval, and the remaining coefficient values are 0; is the deformation rate to be calculated between all adjacent observations; is the vertical deformation result of the L multi-temporal interferometric pairs generated by the multi-sensor data set.

[0033] Step 6: A TSVD operator is introduced to construct a cross-sensor long-time sequence deformation calculation analytical model, and the calculation function model is robustly solved.

[0034] Taking the vertical deformation after observation geometry unification For example, the expression of the analytical model for solving long-term deformation across sensors is as follows: ; in, The deformation rate between all adjacent observations to be solved; Coefficient matrix The truncated singular value decomposition matrix; For generated from multi-sensor datasets Vertical deformation results of multiple temporal interference pairs; and These are the left singular vector and the right singular vector, respectively, and they are orthogonal matrices; To truncate the operator, middle The subsequent diagonal elements are 0; r Let be the rank of the matrix; These are the singular values ​​at the diagonal elements. This indicates transpose.

[0035] Step 7: The unified analytical model of observation geometry, the solution function model, and the cross-sensor long-term deformation solution analytical model are jointly applied to all highly coherent point targets to solve and output the long-term deformation product of the land surface in the monitoring area.

[0036] Thus, this application establishes the long-term deformation results in a specific direction after the geometric unification of observations at a multi-sensor high-coherence point target; all multi-sensor deformation observations in the same direction that can be obtained within the monitoring area can be solved sequentially using the above method to obtain the long-term surface deformation product of the monitoring area.

[0037] To more clearly illustrate the method and its advantages in this application, the following will further explain the method provided in this application with reference to some figures. After obtaining the long-term deformation monitoring results of multi-temporal InSAR on the Earth's surface, the subsequent long-term deformation calculation is performed point-by-point based on highly coherent point targets. In view of the characteristics of this application, taking the long-term deformation calculation of a single point on the Earth's surface as an example, this application will be demonstrated and analyzed with specific embodiments. This embodiment uses the Logistic model (a typical mathematical model for simulating nonlinear deformation) to simulate the nonlinear long-term deformation of a single point on the Earth's surface, such as... Figure 3 and Figure 4 The black solid line in the middle represents the original observation data model for this embodiment. The observation dataset for this embodiment was simulated by setting the actual observation time periods and intervals of three satellites: ERS-2, ENVISAT ASAR, and Sentinel-1A. Gaussian white noise with a mean of 0 and a root mean square error of 5 cm was added to the observation dataset to simulate real observations. The spatiotemporal baseline information of the simulated multi-sensor SAR dataset is shown below. Figure 2The data set is calculated by using the traditional least square method and the method of the application respectively, and the long time series deformation results as shown in Figure 3 and Figure 4 are obtained. The root mean square error between the calculation results and the simulated true value is calculated respectively, and the root mean square error of the traditional method and the method of the application is 12.2 cm and 6.5 cm respectively. Compared with the traditional method, the accuracy of the method of the application in calculating the long time series deformation across sensors is improved by 46.7%, and the long time series result is more consistent with the true value curve. The accuracy of the long time series deformation calculated by the method of the application is mainly affected by the added Gaussian white noise, which verifies the excellent performance of the method of the application in calculating the long time series deformation across sensors.

[0038] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and its internal structure diagram can be as shown in Figure 5 The computer device includes a processor, a memory, an input / output interface (I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store cross-sensor long time series InSAR deformation monitoring data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a cross-sensor long time series InSAR deformation monitoring method.

[0039] Those skilled in the art can understand that Figure 5 the structure shown in the figure is only a block diagram of part of the structure related to the scheme of the application, and does not constitute a limitation on the computer device to which the scheme of the application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program, and the processor executes the computer program to implement the steps in each of the method embodiments.

[0040] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the steps in each of the method embodiments.

[0041] In an example embodiment, a computer program product is provided, including a computer program which, when executed by a processor, implements the steps of any of the above method embodiments.

[0042] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0043] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. 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 above-mentioned embodiments. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0044] The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0045] Any combination of the technical features of the above embodiments can be made, and for the sake of brevity, not all possible combinations are described in the above description. However, it should be understood that the scope of the specification includes all possible combinations of the technical features.

[0046] The principles and implementations of the present application are described in the specific examples, and the above examples are only used to help understand the method and core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for long-term InSAR deformation monitoring across sensors, characterized in that, The cross-sensor long-time InSAR deformation monitoring method includes: Acquire SAR image datasets obtained from observations by multiple SAR sensors within the monitoring area; The SAR image dataset is processed using the temporal InSAR algorithm for differential interferometry and temporal deformation calculation to obtain the radar line of sight of high-coherence point targets within the monitoring period corresponding to each sensor. Deformation results; Each sensor The deformation results and their corresponding parameter information files are uniformly geocoded and converted to the geographic coordinate system defined by the same external DEM; Based on unified coordinates By combining the deformation results and their corresponding parameter information files with the surface interferometry coherence in the monitoring area, long-term stable high coherence point targets on the surface are screened, and a unified analytical model of observation geometry is constructed based on the incident angle and flight azimuth angle at each high coherence point. Based on the acquisition time information of each SAR image dataset, a long-term time series surface deformation solution function model for highly coherent point targets in a specific direction is constructed after observation geometric correction. A truncated singular value decomposition (TSVD) operator is introduced to construct an analytical model for solving long-term deformation across sensors, and the solution function model is robustly solved. The unified analytical model of observation geometry, the solution function model, and the cross-sensor long-term deformation solution analytical model are jointly applied to all highly coherent point targets to calculate and output the long-term deformation product of the land surface in the monitoring area.

2. The cross-sensor long-time InSAR deformation monitoring method according to claim 1, characterized in that, The The deformation result is the projection of the true three-dimensional displacement of the Earth's surface onto the sensor's line-of-sight direction, and the specific expression is as follows: ； in, for Deformation results; and These represent the radar incident angle and flight azimuth angle of the SAR sensor at the corresponding high coherence point, respectively. This represents the vertical deformation component corresponding to the highly coherent point. This represents the east-west deformation component corresponding to the highly coherent point. This represents the north-south deformation component corresponding to the high coherence point.

3. The cross-sensor long-time InSAR deformation monitoring method according to claim 1, characterized in that, The parameter information file includes: the location information of each highly coherent point target in each sensor dataset and the corresponding specific observation geometric parameter file, including the incident angle parameter file, the flight azimuth angle parameter file, the solution residual file, and the coherence file.

4. The cross-sensor long-time InSAR deformation monitoring method according to claim 1, characterized in that, The selection of the high coherence point target is based on the amplitude deviation index. and composite coherence index To be determined jointly; The method for calculating the amplitude deviation index is as follows: , and These are the mean and standard deviation of the amplitude of a given pixel, respectively. The method for calculating the composite coherence index is as follows: , To calculate the number of homogeneous pixels surrounding the pixel to be calculated, and The first to form an interference pair and The composite phase value of the scene image at that pixel location.

5. The cross-sensor long-time InSAR deformation monitoring method according to claim 1, characterized in that, Based on unified coordinates By combining the deformation results and their corresponding parameter information files with the surface interferometry of the monitoring area, long-term stable high-coherence target points on the surface are selected. Based on the incident angle and flight azimuth angle at each high-coherence point, a unified analytical model of observation geometry is constructed, specifically including: When there are within the same observation period S ,and When there are two observations with significant differences, the observation geometric unified analytical model is: ； in, For the first i The deformation obtained from the observation and These represent the radar incident angle and flight azimuth angle of the SAR sensor at the corresponding high coherence point, respectively. This represents the vertical deformation component corresponding to the highly coherent point. This represents the east-west deformation component corresponding to the highly coherent point. This represents the north-south deformation component corresponding to the high coherence point; When there are only two observations with different imaging geometries within a certain observation period, the unified analytical model for observation geometry is: ； When only one sensor is observing during a certain observation period, the unified analytical model for observation geometry is: 。 6. The cross-sensor long-time InSAR deformation monitoring method according to claim 1, characterized in that, Vertical deformation after observation geometry unification For example, the expression of the long-term surface deformation solution function model is as follows: ； in, The coefficient matrix is ​​composed of observations from multiple sensors; The deformation rate between all adjacent observations to be solved; For generated from multi-sensor datasets Vertical deformation results of multiple temporal interference pairs.

7. The cross-sensor long-time InSAR deformation monitoring method according to claim 1, characterized in that, Vertical deformation after observation geometry unification For example, the expression of the analytical model for solving long-term deformation across sensors is as follows: ； in, The deformation rate between all adjacent observations to be solved; Coefficient matrix The truncated singular value decomposition matrix; For generated from multi-sensor datasets Vertical deformation results of multiple temporal interference pairs; and These are the left singular vector and the right singular vector, respectively, and they are orthogonal matrices; To truncate the operator, middle The subsequent diagonal elements are 0; r Let be the rank of the matrix; These are the singular values ​​at the diagonal elements. This indicates transpose.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the cross-sensor long-time InSAR deformation monitoring method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the cross-sensor long-time InSAR deformation monitoring method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the cross-sensor long-time InSAR deformation monitoring method as described in any one of claims 1-7.

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