Multi-source InSAR (Interferometric Synthetic Aperture Radar) data space-time fusion method and system based on Kalman filtering

Abstract

The invention discloses a multi-source InSAR data spatio-temporal fusion method and system based on Kalman filtering, and the method comprises the steps: unifying spatial references of InSAR deformation data of different satellite platforms, and carrying out the matching of homonymy coherent points; based on the unified spatial reference, establishing a rate displacement matrix fused with a time sequence by using an InSAR time sequence data set, and calculating displacements at all moments; and performing dynamic fusion on the multi-source InSAR data by using a Kalman filtering algorithm to obtain a dynamic evolution time sequence of the observation target. According to the invention, through unifying the multi-source InSAR space-time reference and introducing Kalman filtering dynamic fusion, the limitations of data missing, poor coherence, sensor deviation and the like of a single platform are overcome, and the precision, time resolution and continuity of surface deformation monitoring are significantly improved. The method realizes optimal integration of heterogeneous observation information, has adaptive weighting and error suppression capabilities, provides an extensible technical normal form for engineering deformation monitoring, and has relatively high practical value and popularization prospect.

Description

Technical Field

[0001] This invention relates to the field of surface deformation monitoring technology, and in particular to a spatiotemporal fusion method and system for multi-source InSAR data based on Kalman filtering. Background Technology

[0002] To obtain high-precision information on land surface deformation, current technologies mainly rely on SAR data from a single satellite platform. However, their inherent limitations are becoming increasingly apparent: First, the long satellite revisit period results in significant gaps in the monitoring data in the time domain, making it difficult to capture the dynamic evolution of land surface deformation. Second, the observation accuracy and reliability of a single data source continue to decline due to factors such as spatiotemporal incoherence, atmospheric delay disturbances, and sensor system biases. Third, the fixed revisit period limits the temporal resolution, failing to meet the needs of high-frequency monitoring. These shortcomings collectively lead to discontinuous deformation time series and insufficient accuracy, hindering the practical application of land surface deformation monitoring.

[0003] To overcome the limitations of a single data source, multi-source InSAR data fusion has become an inevitable trend. Kalman filtering (KF), as a dynamic data processing method, can take into account the temporal correlation of data, extract the optimal state estimate from multi-source uncertain data, and has verified its superiority in fields such as navigation and positioning. However, its direct application to multi-source InSAR data still faces severe challenges: First, the flight attitude, system parameters, and imaging characteristics of different satellite platforms vary significantly, resulting in deformations acquired by each platform being in the radar line-of-sight (LOS) direction with different incident angles, making it difficult to unify the spatial orientation of deformation components; Second, the interferograms generated by different image sets do not strictly correspond in pixel-level spatial position, and the number and spatial distribution of effective deformation pixels on each platform are also inconsistent, making it difficult to align the spatiotemporal reference. The spatiotemporal inconsistencies among these multi-source data prevent existing methods from fully utilizing InSAR observation information from multiple platforms, orbits, and time phases. This results in insufficient accuracy, limited temporal resolution, and poor continuity in surface deformation monitoring. There is an urgent need to develop a new multi-source InSAR data fusion method that can systematically solve the problems of unifying spatiotemporal benchmarks and dynamic fusion, in order to make up for the deficiencies of single data and significantly improve monitoring accuracy and dynamism. Summary of the Invention

[0004] This invention provides a spatiotemporal fusion method and system for multi-source InSAR data based on Kalman filtering, in order to address the technical problems of insufficient accuracy, temporal resolution, and continuity in monitoring surface deformation using SAR data from a single satellite platform, as well as the difficulty in unifying spatiotemporal references and fully leveraging the advantages of multi-source data to improve monitoring performance in multi-source InSAR data fusion.

[0005] A spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering includes:

[0006] S1. Unify the spatial reference benchmark for InSAR deformation data from different satellite platforms, including projecting LOS deformation onto the vertical direction and matching corresponding coherent points;

[0007] S2. Based on the unified spatial reference benchmark, by collecting InSAR time series datasets with temporal overlap, establish the velocity-displacement matrix of the fused time series and calculate the displacement at each time step.

[0008] S3. The Kalman filter algorithm is used to dynamically fuse multi-source InSAR data to obtain the dynamic evolution time series of the observed target.

[0009] A spatiotemporal fusion system for multi-source InSAR data based on Kalman filtering includes:

[0010] The spatiotemporal reference unification module is used to unify the spatial reference reference for InSAR deformation data from different satellite platforms, including projecting LOS deformation onto the vertical direction and matching corresponding coherent points.

[0011] The velocity-displacement matrix construction and displacement calculation module, based on a unified spatial reference benchmark, collects InSAR time series datasets with temporal overlap, establishes a velocity-displacement matrix of fused time series, and calculates the displacement at each time step.

[0012] The Kalman filter dynamic fusion module uses the Kalman filter algorithm to dynamically fuse multi-source InSAR data to obtain the dynamic evolution time series of the observed target.

[0013] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering.

[0014] This invention fundamentally solves the problem of inconsistent spatiotemporal references caused by platform heterogeneity in multi-source InSAR data by constructing a systematic technical framework of "spatiotemporal reference unification ~ velocity displacement matrix construction ~ Kalman filter dynamic fusion," achieving theoretically optimal integration of heterogeneous observation information. Its core advantages are:

[0015] On the one hand, by using projection transformation and grid matching, the LOS-oriented deformation of multiple platforms is forcibly normalized to a unified vertical spatial reference frame, eliminating systematic biases caused by differences in incident angles, pixel spatial misalignment, and grid mismatch, thus laying a rigorous mathematical and physical foundation for subsequent fusion. On the other hand, the optimal estimation theory of Kalman filtering is innovatively introduced into InSAR time series analysis. Its dynamic assimilation mechanism is used to adaptively weight and fuse multi-source heterogeneous observations, which not only fully considers the state correlation and evolution law of data in the time domain, but also adjusts the gain matrix in real time according to the observation noise characteristics at each moment, achieving a triple optimal trade-off between accuracy, time, and spatial resolution. This progressive processing flow of "unifying the benchmark first, then building the correlation, and finally refining dynamically" not only overcomes the defects of error accumulation and loss of details in traditional static fusion methods, but also achieves dynamic capture and noise suppression of the continuous evolution process of surface deformation through a recursive prediction-correction mechanism, significantly improving the robustness and reliability of monitoring results. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of an application environment for a multi-source InSAR data spatiotemporal fusion method based on Kalman filtering in one embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram of a multi-source InSAR data spatiotemporal fusion system based on Kalman filtering in one embodiment of the present invention;

[0019] Figure 3 This is a map showing the spatial coverage of the study area and multi-source SAR data, as well as the distribution of GNSS checkpoints, in one embodiment of the present invention.

[0020] Figure 4 This is a comparison diagram of the LOS of various satellite platforms in the study area to the surface deformation rate in one embodiment of the present invention;

[0021] Figure 5 This is a Kalman filter fusion of the vertical surface deformation rate and typical regional detail map of the study area in one embodiment of the present invention;

[0022] Figure 6 This is a comparison diagram of the time interval distribution between fusion and single-platform monitoring in the study area according to one embodiment of the present invention;

[0023] Figure 7This is a comparison chart showing the verification of deformation monitoring accuracy based on GNSS in a study area according to one embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The present invention provides a spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering, which can be applied to a spatiotemporal fusion system for multi-source InSAR data based on Kalman filtering.

[0026] In one embodiment, such as Figure 1 As shown, a spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering is provided, and this method is applied to... Figure 1 Taking the server in the example, the following steps are included:

[0027] S1. Unify the spatial reference benchmark for InSAR deformation data from different satellite platforms, including projecting LOS deformation onto the vertical direction and matching corresponding coherent points. Understandably, due to inherent differences in flight attitude, system parameters, and imaging characteristics of different satellite platforms, the InSAR deformation data acquired by each platform are inherently heterogeneous and cannot be directly fused. This heterogeneity manifests in three aspects: First, all deformation values ​​are in the radar line-of-sight (LOS) direction with different incident angles, resulting in inconsistencies in the spatial orientation of the deformation components detected by different platforms; second, although the data is geocoded, the interferograms generated from different image sets do not strictly correspond in pixel-level spatial location, resulting in spatial registration bias; finally, the number and spatial distribution of effective deformation pixels on each platform are also inconsistent, causing data grid mismatch. If these differences in spatiotemporal references are not eliminated before fusion, they will directly lead to systematic deviations or even erroneous calculations in the fusion results. Therefore, it is necessary to transform multi-source data into a completely consistent spatiotemporal reference framework by means of unifying the deformation direction (projecting to the vertical direction) and matching coherent points with the same name (constructing a unified spatial grid) in order to ensure the feasibility, rationality and accuracy of fusion.

[0028] In one embodiment, step S1 further includes the following sub-steps:

[0029] S101. Project the line-of-sight (LOS) deformation of radars on each platform uniformly to the vertical direction:

[0030] Based on the characteristic that spaceborne InSAR is more sensitive to vertical deformation than horizontal deformation, horizontal deformation is set to be negligible.

[0031] Using satellite incident angle The projection formula is used to transform the LOS deformation of all satellite platforms into vertical deformation; the expression of the projection formula is:

[0032]

[0033] in, Indicates vertical deformation. This indicates LOS-oriented deformation.

[0034] After the projection transformation is completed, the deformation data of all satellite platforms are based on the vertical deformation, and time series fusion is performed using the vertical deformation.

[0035] S102. Construct a spatial grid and match corresponding coherent points from different satellite platforms based on the spatial grid:

[0036] Based on data from the first satellite platform, a regular spatial grid was constructed as a baseline framework;

[0037] The deformation monitoring results of other satellite platforms are matched to the spatial grid using geographic coordinates, and the number of pixels of other platforms falling within the spatial grid is counted.

[0038] Only grid cells with valid deformable pixels on all satellite platforms are retained to ensure spatiotemporal consistency and data integrity.

[0039] By matching the grid, the deformation monitoring results from multiple platforms are converted into a unified spatial grid, which provides spatially consistent input data for subsequent time series fusion processing.

[0040] S2. Based on a unified spatial reference standard, a velocity-displacement matrix for the fused time series is established by collecting InSAR time series datasets with temporal overlap, and the displacement at each time point is calculated. Understandably, due to differences in the time intervals and observation periods for acquiring InSAR data from different satellite platforms, even with a unified spatial reference standard, the data from each platform remain discontinuous and inconsistent in the temporal dimension. To comprehensively utilize these temporally overlapping datasets and accurately obtain the changes in surface deformation over time, a mathematical model that can correlate observational data from different time points—that is, a velocity-displacement matrix for the fused time series—is needed.

[0041] In one embodiment, step S2 further includes the following sub-steps:

[0042] S201. Define parameters for multi-source time series data:

[0043] Two InSAR time-series datasets with temporal overlap were collected, denoted as dataset 1 and dataset 2, respectively. Dataset 1 was acquired on the [missing information - likely a date or time period]. The cumulative displacement of each node is denoted as . The second dataset is in the first... The cumulative displacement of each node is denoted as . The fusion of the first With the The time interval and deformation rate corresponding to the node are respectively and ; Set the fusion displacement at the th The cumulative transformation of each node .

[0044] Establish index mapping relationships:

[0045] Indicates the first data set, the first... Each node corresponds to a fused displacement. Index in Indicates the second dataset, the first... Each node corresponds to a fused displacement. The index in.

[0046] S202. Based on multi-source time series data parameters, construct a set of correlation equations between cumulative displacement and velocity:

[0047] (1)

[0048] (2)

[0049] Equation (1) is a linear relationship between the cumulative displacement of each node and the fusion sequence rate for the first dataset; Equation (2) is a linear relationship between the cumulative displacement of each node and the fusion sequence rate for the second dataset.

[0050] S203. Based on the correlation equations between cumulative displacement and velocity, construct the velocity-displacement matrix and solve it in a matrix manner to obtain the displacement at each time step:

[0051] By combining the equations relating cumulative displacement and velocity, a velocity-displacement matrix is ​​constructed from the fused time series:

[0052]

[0053] Where L represents the cumulative displacement vector of the first dataset, and D represents the cumulative displacement vector of the second dataset. The coefficient matrix represents the time intervals. Let represent the velocity vector to be determined.

[0054] Solving the generalized inverse matrix using Singular Value Decomposition (SVD) Then the velocity vector is calculated. ;

[0055] Based on the obtained velocity vector With time interval Calculate the cumulative deformation value of each node in the fusion sequence at each time step. This provides an initial displacement estimate for subsequent Kalman filter fusion.

[0056] S3. The Kalman filter algorithm is used to dynamically fuse multi-source InSAR data to obtain a dynamic evolution time series of the observed target. Understandably, after unifying the spatiotemporal reference and constructing the velocity-displacement matrix, a preliminary framework for multi-source data fusion is established. However, uncertainties remain due to uneven sampling times, significant differences in accuracy, and complex noise characteristics among observations from different platforms. Using simple weighted averaging or static fusion methods cannot fully utilize the state correlation of the data in the time domain, nor can it adaptively adjust based on the observation quality at each moment, easily leading to error accumulation and loss of detail. The Kalman filter, as a dynamic data assimilation algorithm, fully utilizes the state correlation of the data in the time domain through a "prediction-correction" recursive mechanism. It extracts the optimal state estimate from these noisy and uncertain multi-source data and comprehensively considers state transition laws and observation information quality to perform optimal weighted fusion of multi-source heterogeneous observations: it uses a process model to predict the state at the next moment, and then dynamically calculates the gain matrix based on the new observation values ​​to correct the prediction bias, thereby effectively suppressing noise, filling data gaps, and refining the estimation results iteratively over time. This mechanism not only enables the dynamic integration of InSAR observation information from multiple platforms, multiple orbits, and multiple time phases, but also captures the continuous evolution of surface deformation, ultimately outputting high-precision, high-temporal-resolution, and high-reliability deformation time series, significantly improving the dynamic performance and accuracy of monitoring.

[0057] In one embodiment, step S3 further includes the following sub-steps:

[0058] S301. Constructing the Kalman filter state-space model:

[0059] The observations of the Kalman filter are defined as displacements. The state vector is defined as The expression for the Kalman filter state-space model is:

[0060]

[0061] in, Indicates the first Based on a moment The predicted value at time -1 Indicates the first The optimal estimate at time -1. Let be the state transition matrix.

[0062] Understandably, the Kalman filter algorithm defines observations as displacements, while the state vector is set according to the actual physical meaning of surface deformation, typically including key parameters such as displacement and its rate of change. At each time step, the algorithm predicts the state based on the state estimate from the previous time step and the observations at the current time step, using parameters such as the state transition matrix and the observation matrix. This prediction process takes into account the dynamic characteristics of the system and can reasonably infer the current state based on historical information.

[0063] Represents the predicted covariance matrix. This represents the estimation of the covariance matrix. This represents the process noise covariance matrix.

[0064] Understandably, the prediction covariance matrix is ​​updated synchronously to characterize the uncertainty of state evolution.

[0065] Represents the gain matrix. Represents the observation matrix. This represents the observation noise covariance matrix, reflecting the accuracy of InSAR observations.

[0066] Intuitively, the Kalman gain is calculated using the actual observations at the current moment, combined with the predicted values ​​and the predicted covariance matrix. The Kalman gain is a coefficient that balances the importance of the predicted and observed values; it automatically adjusts their weights in the final state estimation based on the reliability of the observations and the accuracy of the predictions. Using the Kalman gain, the algorithm corrects the predicted values ​​to obtain the optimal state estimate and the updated covariance matrix at the current moment.

[0067] This represents the updated and corrected optimal estimate. Represents the observed value; This represents the updated and corrected covariance matrix.

[0068] Understandably, using actual observations Compared with predicted observations The residuals are used to correct the state predictions. The estimated covariance matrix is ​​then updated. This quantifies the uncertainty of the current estimate.

[0069] S302. Dynamic estimation and correction are performed using the Kalman filter state-space model, outputting... The optimal estimate after time correction As a result of the fusion.

[0070] S303, and proceed to the next time step to repeat S301-S302, to achieve dynamic assimilation and continuous fusion of multi-source InSAR observation data, and obtain the dynamic evolution time series of the observed target.

[0071] Understandably, this process repeats continuously. As new observational data arrives, the Kalman filter algorithm continuously predicts and corrects, gradually advancing to the next time step. By dynamically assimilating and continuously fusing multi-source InSAR data over the entire time series, a precise time series depicting the dynamic evolution of the observed target over time is finally obtained. This time series accurately reflects the changes in surface deformation at different times, providing reliable data support for surface deformation monitoring and analysis. It effectively solves problems such as insufficient monitoring accuracy from a single data source, limited temporal resolution, and difficulty in unifying spatiotemporal benchmarks in multi-source data fusion, significantly improving the practicality and accuracy of surface deformation monitoring.

[0072] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0073] In one embodiment, the present invention also provides a spatiotemporal fusion system for multi-source InSAR data based on Kalman filtering, implemented using the aforementioned spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering, such as... Figure 2 As shown, the multi-source InSAR data spatiotemporal fusion system based on Kalman filtering includes:

[0074] The spatiotemporal reference unification module 100 is used to unify the spatial reference reference for InSAR deformation data from different satellite platforms, including projecting LOS deformation onto the vertical direction and matching corresponding coherent points.

[0075] The velocity-displacement matrix construction and displacement calculation module 200, based on a unified spatial reference benchmark, collects InSAR time series datasets with temporal overlap, establishes a velocity-displacement matrix of fused time series, and calculates the displacement at each time step.

[0076] The Kalman filter dynamic fusion module 300 uses the Kalman filter algorithm to dynamically fuse multi-source InSAR data to obtain the dynamic evolution time series of the observed target.

[0077] Specific limitations regarding the Kalman filter-based multi-source InSAR data spatiotemporal fusion system can be found in the above description of the limitations of the Kalman filter-based multi-source InSAR data spatiotemporal fusion method, and will not be repeated here. Each module in the aforementioned Kalman filter-based multi-source InSAR data spatiotemporal fusion system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independent of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.

[0078] In one specific embodiment, taking three types of SAR data covering the Daxing Airport area (the study area) as an example, the spatiotemporal fusion method of multi-source InSAR data based on Kalman filtering of this invention is applied to verify and analyze the technical effect of this invention. The study area is located within the spatial overlap region of the Sentinel-1 ascending orbit, Sentinel-1 descending orbit, and TerraSAR-X descending orbit images, and the three types of SAR data also have effective overlap in the temporal domain, providing a basic condition for multi-source data fusion. Simultaneously, eight GNSS stations are deployed within this study area to obtain vertical displacement as independent check data, and their spatial distribution is as follows: Figure 3 (a) As shown in the orange triangle.

[0079] like Figure 4 As shown in (a, b, c, d), Figure 4 (a) illustrates the surface deformation rate of Sentinel-1 as it ascends to orbit. Figure 4 (b) shows the surface deformation rate of Sentinel-1 during its descent. Figure 4 (c) Demonstrating the surface deformation rate during TerraSAR-X's descent. Figure 4 (d) illustrates the airport foundation treatment methods, indicating significant differences in the original LOS to InSAR surface deformation rates of each platform; the vertical surface deformation rate obtained after fusion using the method of this invention; such as Figure 5 As shown in (a, b, c), the fine deformation features of key areas such as the maintenance area, cargo area, and terminal building are clearly identified. Figure 6 As shown, 98.8% of the time intervals for fusion deformation monitoring are within 12 days, and are continuously distributed from 1 to 12 days. Compared with single-platform data (ascending orbit Sentinel 1 is fixed at 12 days, descending orbit Sentinel 1 is 12 days and accounts for 75%, and descending orbit TerraSAR-X, although 11 days accounts for 36.5%, most exceed 20 days), it shows that the method of the present invention significantly improves the monitoring time resolution and continuity.

[0080] like Figure 7The accuracy verification results shown indicate that the root mean square error (RMSE) of both the fused time series and GNSS check data is less than 8 mm. The RMSE of the fused time series is reduced by 21% compared to Sentinel-1 and by 24% compared to TerraSAR-X, which fully verifies that the present invention significantly improves the accuracy and reliability of InSAR deformation monitoring.

[0081] In one embodiment, a computer device is provided, which may be a server. The computer device includes a processor, memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for a Kalman-filter-based multi-source InSAR data spatiotemporal fusion system. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a Kalman-filter-based multi-source InSAR data spatiotemporal fusion method.

[0082] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the multi-source InSAR data spatiotemporal fusion method based on Kalman filtering described in the above embodiment, for example... Figure 1 As shown, to avoid repetition, it will not be described again here. Alternatively, when this computer program is executed by the processor, it implements the functions of each module / unit in this embodiment of the multi-source InSAR data spatiotemporal fusion system based on Kalman filtering, for example... Figure 2 The spatiotemporal fusion function of multi-source InSAR data based on Kalman filtering, as shown, will not be described again here to avoid repetition.

[0083] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0085] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering, characterized in that, include: S1. Unify the spatial reference benchmark for InSAR deformation data from different satellite platforms, including projecting LOS deformation onto the vertical direction and matching corresponding coherent points; S2. Based on the unified spatial reference benchmark, by collecting InSAR time series datasets with temporal overlap, establish the velocity-displacement matrix of the fused time series and calculate the displacement at each time step. S3. The Kalman filter algorithm is used to dynamically fuse multi-source InSAR data to obtain the dynamic evolution time series of the observed target.

2. The spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering according to claim 1, characterized in that, Step S1 includes the following sub-steps: S101. Project the line-of-sight (LOS) deformation of radars on each platform uniformly to the vertical direction: Based on the characteristic that spaceborne InSAR is more sensitive to vertical deformation than horizontal deformation, horizontal deformation is set to be negligible. Using satellite incident angle The projection formula is used to transform the LOS deformation of all satellite platforms into vertical deformation; the expression of the projection formula is: ； in, Indicates vertical deformation. Indicates LOS-directed deformation; After the projection transformation is completed, the deformation data of all satellite platforms are based on the vertical deformation, and time series fusion is performed using the vertical deformation. S102. Construct a spatial grid and match corresponding coherent points from different satellite platforms based on the spatial grid: Based on data from the first satellite platform, a regular spatial grid was constructed as a baseline framework; The deformation monitoring results of other satellite platforms are matched to the spatial grid using geographic coordinates, and the number of pixels of other platforms falling within the spatial grid is counted. Only grid cells with valid deformable pixels on all satellite platforms are retained to ensure spatiotemporal consistency and data integrity. By matching the grid, the deformation monitoring results from multiple platforms are converted into a unified spatial grid, which provides spatially consistent input data for subsequent time series fusion processing.

3. The spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering according to claim 2, characterized in that, Step S2 includes the following sub-steps: S201. Define parameters for multi-source time series data: Two InSAR time-series datasets with temporal overlap were collected, denoted as dataset 1 and dataset 2, respectively. Dataset 1 was acquired on the [missing information - likely a date or time period]. The cumulative displacement of each node is denoted as . The second dataset is in the first The cumulative displacement of each node is denoted as . The fusion of the first With the The time interval and deformation rate corresponding to the node are respectively and ; Set the fusion displacement at the th The cumulative transformation of each node ; Establish index mapping relationships: Indicates the first data set, the first... Each node corresponds to a fused displacement. Index in Indicates the second dataset, the first... Each node corresponds to a fused displacement. Index in; S202. Based on multi-source time series data parameters, construct a set of correlation equations between cumulative displacement and velocity: （1） （2） Equation (1) is a linear relationship between the cumulative displacement of each node and the fusion sequence rate established for the first dataset; Equation (2) is a linear relationship between the cumulative displacement of each node and the fusion sequence rate established for the second dataset. S203. Based on the correlation equations between cumulative displacement and velocity, construct the velocity-displacement matrix and solve it in a matrix manner to obtain the displacement at each time step: By combining the equations relating cumulative displacement and velocity, a velocity-displacement matrix is ​​constructed from the fused time series: ； Where L represents the cumulative displacement vector of the first dataset, and D represents the cumulative displacement vector of the second dataset. The coefficient matrix represents the time intervals. This represents the velocity vector to be determined. Solving the generalized inverse matrix using Singular Value Decomposition (SVD) Then the velocity vector is calculated. ; Based on the obtained velocity vector With time interval Calculate the cumulative deformation value of each node in the fusion sequence at each time step. This provides an initial displacement estimate for subsequent Kalman filter fusion.

4. The spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering according to claim 3, characterized in that, Step S3 includes the following sub-steps: S301. Constructing the Kalman filter state-space model: The observations of the Kalman filter are defined as displacements. The state vector is defined as The expression for the Kalman filter state-space model is: ； in, Indicates the first Based on a moment The predicted value at time -1 Indicates the first The optimal estimate at time -1. This is the state transition matrix; Represents the predicted covariance matrix. This represents the estimation of the covariance matrix. Represents the process noise covariance matrix; Represents the gain matrix. Represents the observation matrix. Represents the observation noise covariance matrix; This represents the updated and corrected optimal estimate. Represents the observed value; This represents the updated and corrected covariance matrix; S302. Dynamic estimation and correction are performed using the Kalman filter state-space model, outputting... The optimal estimate after time correction As a result of the fusion; S303, and proceed to the next time step to repeat S301-S302, to achieve dynamic assimilation and continuous fusion of multi-source InSAR observation data, and obtain the dynamic evolution time series of the observed target.

5. A spatiotemporal fusion system for multi-source InSAR data based on Kalman filtering, characterized in that, The method employs the spatiotemporal fusion of multi-source InSAR data based on Kalman filtering as described in any one of claims 1-4, characterized in that it includes: The spatiotemporal reference unification module is used to unify the spatial reference reference for InSAR deformation data from different satellite platforms, including projecting LOS deformation onto the vertical direction and matching corresponding coherent points. The velocity-displacement matrix construction and displacement calculation module, based on a unified spatial reference benchmark, collects InSAR time series datasets with temporal overlap, establishes a velocity-displacement matrix of fused time series, and calculates the displacement at each time step. The Kalman filter dynamic fusion module uses the Kalman filter algorithm to dynamically fuse multi-source InSAR data to obtain the dynamic evolution time series of the observed target.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the spatiotemporal fusion method for multi-source InSAR data based on Kalman filtering according to any one of claims 1 to 4.

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