InSAR plane area network adjustment method, device and equipment based on multi-source remote sensing data assistance in plain area and medium

Abstract

This invention discloses a method, apparatus, equipment, and medium for InSAR planar regional network adjustment in plain areas based on multi-source remote sensing data. The method involves acquiring multi-scene dual-station InSAR data from a plain area to generate a DEM in the radar coordinate system; inputting the DEM into an R-D model for direct positioning and establishing a lookup table for transforming the radar coordinate system to the geodetic coordinate system; projecting the SAR intensity map onto the geodetic coordinate system using this lookup table as the target image for planar regional network adjustment; acquiring the simulated DEM intensity map and sentinel intensity map within the SAR image coverage area, while simultaneously searching for overlapping areas between SAR intensity maps and matching control points and tie points; considering the changing trend of SAR positioning errors, constructing a positioning error model, and performing planar regional network adjustment calculations using control points and tie points to solve for the model parameters; and correcting the SAR positioning error scene by scene based on the model parameters. The InSAR planar regional network adjustment method provided by this invention solves the problem of difficulty in finding sufficient control points for refined positioning in plain areas using SAR.

Description

Technical Field

[0001] This invention belongs to the geodetic field of Synthetic Aperture Radar Interferometry (InSAR), specifically relating to a method, apparatus, equipment, and medium for adjusting InSAR planar regional networks in plain areas based on multi-source remote sensing data. Background Technology

[0002] Digital Elevation Models (DEMs) are crucial maps describing the Earth's surface elevation, playing a vital role in geological hazard monitoring, infrastructure planning, and geostrategic analysis. Interferometric Synthetic Aperture Radar (InSAR), with its all-weather, all-time ground penetration mapping capabilities, has become a core remote sensing topographic mapping method.

[0003] Target localization accuracy is a crucial performance indicator for InSAR systems, directly impacting the reliability of InSAR applications in production. The Range-Doppler (RD) model is widely recognized as the imaging model that best reflects the characteristics of Synthetic Aperture Radar (SAR) imaging and is commonly used for SAR target localization. However, due to on-board parameter errors, localization results calculated based on the RD model require further correction of planar localization errors using ground control points. Nevertheless, because plain areas have limited terrain features, it is difficult to find reliable and sufficient ground control points to meet the requirements for refined target localization. Therefore, it is necessary to design a method to ensure the accuracy of SAR target localization in plain areas, thereby guaranteeing the planar localization accuracy of the InSAR DEM within the region. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for adjusting InSAR planar regional networks in plain areas based on multi-source remote sensing data assistance, which can achieve high-precision SAR target positioning in plain areas with a small number of control points.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: A method for adjusting InSAR planar regional networks in plain areas based on multi-source remote sensing data includes the following steps: Step 1: Acquire multi-scene dual-station InSAR data of the plain area, perform registration interferometry and phase height transformation processing, and generate DEM in radar coordinate system; Step 2: Input the DEM into the RD model for direct positioning and establish a lookup table for converting the radar coordinate system to the geodetic coordinate system. Step 3: Use the lookup table to project the SAR intensity map onto the geodetic coordinate system as the target image for planar regional network adjustment; Step 4: Obtain the DEM simulated intensity map and sentinel intensity map within the SAR image coverage area. Simultaneously, search for overlapping areas between SAR intensity maps. Uniformly match control points and tie points using the correlation coefficient method based on Fourier transform, ensuring the correlation coefficient value is higher than [value missing]. ; Step 5: Based on the changing trend of SAR positioning error, with control points as absolute positioning constraints and connection points as relative positioning constraints, construct a planar regional network adjustment model to jointly observe the positioning error of SAR images within the observation area, and solve the model parameters by combining robust estimation and the least squares rule. Step 6: Correct the SAR positioning error scene by scene according to the parameters of the planar regional network adjustment model, then correct the InSAR altimetry error through the elevation regional network adjustment, and finally fuse and embed the single-scene InSAR DEM into a seamless plain area InSAR terrain product with high-precision planar positioning.

[0006] Furthermore, the specific process of generating a DEM using step 1 includes: First, perform registration interferometry between master and slave images; then, extract the topographic phase and perform phase-to-height transformation to reconstruct the topography.

[0007] in, and These are the radar's wavelength and angle of incidence, respectively. Indicates the length of the vertical baseline. The slant range from the center of the SAR satellite antenna to the target point. This indicates an unwound terrain phase.

[0008] Furthermore, the specific process of SAR target localization in step 2 includes: Input the DEM generated in step 1 into the RD model, and calculate the object points in the spatial Cartesian coordinate system corresponding to the image points in the radar coordinate system by direct positioning. :

[0009] in, and These represent the position and velocity of the SAR satellite in a Cartesian coordinate system. and These represent the wavelength and slant range of the SAR satellite, respectively; in addition, and These are the major and minor semi-axises of the Earth's ellipsoid, respectively. The geodetic height of the object point is provided by the DEM. Then, the spatial rectangular coordinates are converted according to the ellipsoid parameters. Convert to geodetic coordinate system :

[0010] in, The first eccentricity of the ellipsoid, Let be the radius of curvature of the ellipsoid's primordial circle. From this, a lookup table for transforming radar coordinates to geodetic coordinates can be established.

[0011] Furthermore, in step 3, the SAR intensity map in the radar coordinate system is projected onto the geodetic coordinate system through bilinear interpolation using the lookup table established in step 2, serving as the target image for planar regional network adjustment. Furthermore, step 4, matching SAR intensity map control points and tie points, includes the following specific steps: With the assistance of DEM simulated intensity maps and sentinel intensity maps from multi-source remote sensing data, the correlation coefficient method based on Fourier transform is used to match the overlapping areas between SAR intensity maps and multi-source remote sensing data, and between SAR intensity maps, in order to extract control points and tie points.

[0012] in, For conjugate multiplication, and These represent the forward Fourier transform and the inverse Fourier transform, respectively. and These are the matching windows for the reference image DEM simulated intensity map and the sentinel intensity map, respectively. This represents the matching window for the images to be registered. and These are the control point offsets provided for the DEM simulated intensity map and the sentinel intensity map, respectively. Since the geometric distortion differences caused by different SAR viewing angles and flight directions are relatively small in the plain area, they can be considered... It is reliable; facing the hilly area, its offset can be determined by... Therefore, the synergy of the two can provide sufficient control points. Furthermore, and These represent the reference matching window and the window to be matched in the overlapping area of ​​the SAR intensity maps, respectively. The offset of the connection point provided for the overlapping area.

[0013] Furthermore, the specific process of establishing the planar regional network adjustment model in step 5 includes: Step 5.1: Construct a positioning error model based on the SAR target positioning error pattern, expressed as:

[0014] in, This is an index for the SAR intensity map. and These represent row and column coordinates, respectively. and These are the positioning error functions in the row and column directions, respectively. and These are the parameters to be determined for the model.

[0015] Step 5.2: Based on the absolute and relative positioning constraints provided by the control points and connection points respectively, construct the planar regional network adjustment model:

[0016] in, and This is an index for the overlapping area of ​​the SAR intensity map. and These are the original row and column numbers, and These are the actual row and column numbers for the registration estimation.

[0017] Step 5.3, combining the offset estimated in Step 4, further derivation leads to the following functional relationship:

[0018] in, and The row and column offsets provided by the control points are respectively controlled. and These are the row and column offsets provided for the connection points, respectively. Therefore, the error equation can be expressed as:

[0019] in, and These are the residual matrices for the control points and the connection points, respectively.

[0020] Step 5.4: Solve for the model parameters by combining robust estimation and the least squares rule, and set the initial weight matrix. Given the identity matrix, solve using the least squares criterion:

[0021] in, This represents the matrix transpose. Subsequently, the initial weight matrix is ​​updated according to the IGGⅢ scheme. for until the model parameters change. Stop iteration when, where The threshold value is set.

[0022]

[0023] in, To standardize the residuals, and The elimination factor has the following value ranges: and .

[0024] Furthermore, the specific process of step 6 includes: The SAR positioning error is corrected scene by scene based on the parameters of the planar regional network adjustment model; then, the InSAR altimetry system error is corrected through the elevation regional network adjustment; finally, the data is fused and embedded into a seamless plain area InSAR terrain product with high-precision planar positioning.

[0025] A multi-source remote sensing data-assisted InSAR planar regional network adjustment device for plain areas includes: The data preprocessing module is used to: perform registration interferometry and phase height transformation on the acquired dual-station InSAR data of the plain area to obtain the DEM in the radar coordinate system; The SAR initial target localization module is used to: directly locate the target using the RD model generated by InSAR DEM; establish a lookup table for transforming the radar coordinate system to the geodetic coordinate system; and project the SAR intensity map in the radar coordinate system to the geodetic coordinate system as the target image for planar regional network adjustment. The planar regional network adjustment module is used to: acquire multi-source remote sensing data within the coverage area of ​​SAR imagery, search for overlapping areas between SAR intensity maps, and match control points and tie points using the correlation coefficient method based on Fourier transform; consider positioning errors caused by inaccurate SAR satellite parameters, and establish a planar regional network adjustment model using absolute and relative positioning constraints provided by control points and tie points; solve for model parameters by combining robust estimation and the least squares method, and correct positioning errors scene by scene. The fusion mosaic module is used to: correct the altimetry system error of different InSAR data acquisition strips using elevation regional network adjustment; and fuse and mosaic single-scene terrain products to obtain InSAR terrain products for a large-scale plain area.

[0026] An electronic device includes a memory and a processor; wherein the memory is used to store computer program instructions, and the processor is used to execute the computer program instructions in the memory, specifically performing the method described in any of the above technical solutions.

[0027] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any of the above technical solutions.

[0028] Beneficial effects

[0029] This invention presents a planar regional network adjustment method for InSAR in plain areas assisted by multi-source remote sensing data, achieving reliable planar positioning accuracy for SAR images in plain areas. First, multi-scene dual-station InSAR data from the plain area is acquired, and registration interferometry and phase-to-height transformation are performed to generate a DEM in the radar coordinate system. Then, based on this DEM, direct positioning using the RD model is performed, establishing a lookup table for transforming the radar coordinate system to the geodetic coordinate system. Next, the SAR intensity map is projected onto the geodetic coordinate system as the target image for planar regional network adjustment. Then, the simulated DEM intensity map and sentinel intensity map within the SAR image coverage area are acquired, and overlapping areas between SAR intensity maps are searched. Control points and tie points are matched using the correlation coefficient method based on Fourier transform. Next, a positioning error model is constructed based on the changing trend of SAR positioning error, and the model parameters are solved through planar regional network adjustment using control points and tie points. Finally, the SAR positioning error is corrected scene by scene using the model parameters, and the InSAR terrain product for the plain area is fused and mosaicked using elevation regional network adjustment.

[0030] The beneficial effects of this method are as follows: It constructs an InSAR planar network adjustment technique for plain areas, which overcomes the limitation of existing refined SAR target positioning methods in which it is difficult to match reliable and sufficient ground control points in plain areas; it selects multi-source remote sensing data as reference images according to different terrain scenes in plain areas, ensuring the uniform distribution of control points, and further utilizes the observation information of control points with the connection points as relative positioning constraints, thereby achieving a robust improvement in the planar positioning accuracy of SAR images in plain areas. Attached Figure Description

[0031] Figure 1 This is a flowchart of the method described in an embodiment of the present invention.

[0032] Figure 2 The coverage area of ​​the plain area InSAR image interferometry selected for this invention is the range of the area covered.

[0033] Figure 3 This image shows the fusion results of InSAR topographic products in the plain area and their differences from the reference DEM. Figure 3 (a) Shows the results of InSAR terrain product fusion in the plains area. Figure 3 (b) shows the difference between the InSAR DEM and the reference DEM.

[0034] Figure 4 This is a comparison diagram between the method of the present invention and the method using a single reference image for refined localization. Wherein, Figure 4 (a) and (c) are respectively Figure 2 The A-1 and C-1 interferometric plots show the differences between the terrain products obtained through refined positioning using the method of this invention and the reference DEM. Figure 4(b) and (d) are the differences between the terrain products obtained by refining the positioning using only the sentinel intensity map and the DEM simulation intensity map for the corresponding interferometric pair and the reference DEM, respectively.

[0035] Figure 5 This is a histogram illustrating the improvement in planar positioning accuracy achieved by the method of this invention. Figure 5 (a) indicates the longitude direction. Figure 5 (b) represents the latitude direction. Detailed Implementation

[0036] The embodiments of the present invention will be described in detail below. These embodiments are based on the technical solutions of the present invention and provide detailed implementation methods and specific operation processes to further explain the technical solutions of the present invention.

[0037] To better illustrate the methods and steps of this invention, the invention is further described in detail using LT-1 dual-station L-band InSAR data, spaceborne ICESat-2 data, Sentinel and DEM simulated intensity maps located in the southern plains of Henan Province, China. Note that the specific implementation described herein is only for explaining the invention and is not intended to limit the invention.

[0038] This embodiment provides a method for adjusting InSAR planar regional networks in plain areas based on multi-source remote sensing data, referring to... Figure 1 As shown, it includes the following steps: Step 1: Acquire multi-scene dual-station InSAR data for the plains area. This implementation example selects LT-1 dual-station InSAR data located in the southern plains of Henan Province, China. Three adjacent orbits were used, each containing three InSAR scenes, for a total of nine interferometric pairs. The experimental area location is as follows: Figure 2 As shown, the base map is an optical image. It can be seen that the surface cover type in this experimental area is mainly cultivated land, with minimal topographic relief except for small hills in the southwest. Registration interferometry was performed on the LT-1 dual-station InSAR data; then, the topographic phase was extracted and phase-height conversion was performed to reconstruct the topography.

[0039] in, and These are the radar's wavelength and angle of incidence, respectively. Indicates the length of the vertical baseline. The slant range from the center of the SAR satellite antenna to the target point. This indicates an unwound terrain phase.

[0040] Step 2: Input the DEM generated in Step 1 into the RD model, and calculate the object points in the spatial Cartesian coordinate system corresponding to the image points in the radar coordinate system by direct positioning. :

[0041] in, and These represent the position and velocity of the SAR satellite in a Cartesian coordinate system. and These represent the wavelength and slant range of the SAR satellite, respectively; in addition, and These are the major and minor semi-axises of the Earth's ellipsoid, respectively. The geodetic height of the object point is provided by the DEM. Then, the spatial rectangular coordinates are converted according to the ellipsoid parameters. Convert to geodetic coordinate system :

[0042] in, The first eccentricity of the ellipsoid, Let be the radius of curvature of the ellipsoid's primordial circle. From this, a lookup table for transforming radar coordinates to geodetic coordinates can be established.

[0043] Step 3: Using the lookup table established in Step 2, the SAR intensity map in the radar coordinate system is projected onto the geodetic coordinate system through bilinear interpolation, serving as the target image for planar regional network adjustment. Step 4: With the assistance of the multi-source remote sensing data DEM simulated intensity map and sentinel intensity map, the SAR intensity map is matched with the overlapping areas between the multi-source remote sensing data and the SAR intensity map using the correlation coefficient method based on Fourier transform, in order to extract control points and tie points.

[0044] in, For conjugate multiplication, and These represent the forward Fourier transform and the inverse Fourier transform, respectively. and These are the matching windows for the reference image DEM simulated intensity map and the sentinel intensity map, respectively. This represents the matching window for the images to be registered. and These are the control point offsets provided for the DEM simulated intensity map and the sentinel intensity map, respectively. Since the geometric distortion differences caused by different SAR viewing angles and flight directions are relatively small in the plain area, they can be considered... It is reliable; facing the hilly area, its offset can be determined by... Therefore, the synergy of the two can provide sufficient control points. Furthermore, and These represent the reference matching window and the window to be matched in the overlapping area of ​​the SAR intensity maps, respectively. The offset of the connection point provided for the overlapping area.

[0045] Step 5: Establish the planar regional network adjustment model. The specific process includes: Step 5.1: Construct a positioning error model based on the SAR target positioning error pattern, expressed as:

[0046] in, This is an index for the SAR intensity map. and These represent row and column coordinates, respectively. and These are the positioning error functions in the row and column directions, respectively. and These are the parameters to be determined for the model.

[0047] Step 5.2: Based on the absolute and relative positioning constraints provided by the control points and connection points respectively, construct the planar regional network adjustment model:

[0048] in, and This is an index for the overlapping area of ​​the SAR intensity map. and These are the original row and column numbers, and These are the actual row and column numbers for the registration estimation.

[0049] Step 5.3, combining the offset estimated in Step 4, further derivation leads to the following functional relationship:

[0050] in, and The row and column offsets provided by the control points are respectively controlled. and These are the row and column offsets provided for the connection points, respectively. Therefore, the error equation can be expressed as:

[0051] in, and These are the residual matrices for the control points and the connection points, respectively.

[0052] Step 5.4: Solve for the model parameters by combining robust estimation and the least squares rule, and set the initial weight matrix. Given the identity matrix, solve using the least squares criterion:

[0053] in, This represents the matrix transpose. Subsequently, the initial weight matrix is ​​updated according to the IGGⅢ scheme. for until the model parameters change. Stop iteration when, where The threshold value is set.

[0054]

[0055] in, To standardize the residuals, and The elimination factor has the following value ranges: and .

[0056] Step 6: Correct SAR positioning errors scene by scene based on the parameters of the planar regional network adjustment model; then, correct InSAR altimetry system errors through elevation regional network adjustment; finally, fuse and embed them into a seamless plain area InSAR terrain product with high-precision planar positioning.

[0057] Figure 3 (a) shows the fusion result of InSAR topographic products in the plain area, and the difference between it and the reference DEM is shown in the figure below. Figure 3 As shown in (b). Figure 3 (b) shows no obvious positioning anomalies and no significant jumps between different orbits, qualitatively indicating good image planar positioning accuracy and that altimeter system errors have been eliminated. To more clearly highlight the advantages of this embodiment in using multi-source remote sensing data to assist in InSAR planar regional network adjustment in plain areas, Figure 4 The comparison results between the method of the present invention and the method using a single reference image for refined localization are presented. Among them, Figure 4 (a) and (b) are the difference maps between the terrain products obtained by refining the positioning using the method of the present invention and the reference DEM, based on the A-1 and C-1 interferometric pairs of this embodiment. Figure 4 (b) and (d) are the differences between the terrain products obtained by refining the localization using only the sentinel intensity map and the DEM simulation intensity map for the corresponding interferometric pair, and the reference DEM, respectively. (Comparison) Figure 4 As shown in (a) and (b), due to the difference in perspective between heterogeneous SAR intensity maps, relying solely on the sentinel intensity map cannot match reliable control points in terrain-undulating areas, leading to positioning anomalies and obvious positioning drift signals; in contrast... Figure 4As shown in (c) and (d), in farmland areas with almost no topographic relief, relying solely on DEM intensity maps is insufficient for refining SAR image planar positioning. Significant positioning drift signals appear at river locations, and the abnormal planar positioning leads to incorrect estimations of altimetry system errors. The above comparison demonstrates the advantages of using multi-source remote sensing data to assist in InSAR planar regional network adjustment in planar areas. To quantitatively evaluate the accuracy of the method of this invention, 314 checkpoints were selected in the experimental area to assess positioning errors in the longitude and latitude directions. The results are as follows: Figure 5 As shown in (a) and (b). From Figure 5 It can be seen that the RMSE of pixels offset in the longitude direction decreased from 6.19 to 0.24, and the MAE decreased from 6.17 to 0.19; the RMSE of pixels offset in the latitude direction decreased from 1.02 to 0.31, and the MAE decreased from 0.66 to 0.18. This proves the effectiveness of the present invention.

[0058] The above embodiments are preferred embodiments of this application. Those skilled in the art can make various changes or improvements based on them. Without departing from the overall concept of this application, these changes or improvements should fall within the scope of protection claimed in this application.

Claims

1. A method for adjusting InSAR planar regional networks in plain areas based on multi-source remote sensing data, characterized in that, Includes the following steps: Step 1: Acquire multi-scene dual-station InSAR data of the plain area, perform registration interferometry and phase height transformation processing, and generate DEM in radar coordinate system; Step 2: Input the DEM into the RD model for direct positioning and establish a lookup table for converting the radar coordinate system to the geodetic coordinate system. Step 3: Use the lookup table to project the SAR intensity map onto the geodetic coordinate system as the target image for planar regional network adjustment; Step 4: Obtain the DEM simulated intensity map and sentinel intensity map within the coverage area of ​​the SAR image, and at the same time search for the overlapping area between the SAR intensity maps. Use the correlation coefficient method based on Fourier transform to match control points and tie points respectively. Step 5: Considering the changing trend of SAR positioning error, construct a planar regional network adjustment model with control points as absolute positioning constraints and connection points as relative positioning constraints, and solve the model parameters by combining robust estimation and the least squares rule. Step 6: Correct the SAR positioning error based on the parameters of the planar regional network adjustment model, then correct the InSAR altimetry system error through the elevation regional network adjustment, and finally fuse and mosaic the InSAR terrain product of the plain area.

2. The method according to claim 1, characterized in that, Step 2 includes the following specific steps: First, input the DEM generated in step 1 into the RD model, and calculate the object points in the spatial rectangular coordinate system corresponding to the image points in the radar coordinate system. : in, and These represent the position and velocity of the SAR satellite in a Cartesian coordinate system. and These represent the wavelength and slant range of the SAR satellite, respectively; in addition, and These are the major and minor semi-axises of the Earth's ellipsoid, respectively. The geodetic height of the object point is provided by the DEM; then, the spatial rectangular coordinates are converted to the geodetic coordinate system according to the ellipsoid parameters, and a lookup table for converting the radar coordinate system to the geodetic coordinate system is established.

3. The method according to claim 1, characterized in that, Step 3 includes the following specific steps: Using the lookup table established in step 2, the SAR intensity map in the radar coordinate system is projected onto the geodetic coordinate system as the target image for planar regional network adjustment.

4. The method according to claim 1, characterized in that, Step 4, matching the control points and connection points of the planar regional network adjustment, includes the following specific processes: With the assistance of DEM simulated intensity maps and sentinel intensity maps from multi-source remote sensing data, the correlation coefficient method based on Fourier transform is used to match the overlapping areas between SAR intensity maps and multi-source remote sensing data, and between SAR intensity maps, in order to extract control points and tie points. in, For conjugate multiplication, and These represent the forward Fourier transform and the inverse Fourier transform, respectively. and These are the matching windows for the reference image DEM simulated intensity map and the sentinel intensity map, respectively. This represents the matching window for the images to be registered. and The control point offsets provided by the DEM simulated intensity map and the sentinel intensity map are respectively; since the geometric distortion difference caused by the different SAR viewing angle and flight direction in the plain area is small, it can be considered that... It is reliable; facing the hilly area, its offset can be determined by... Provided; therefore, the synergy of the two can provide sufficient control points; furthermore, and These represent the reference matching window and the window to be matched in the overlapping area of ​​the SAR intensity maps, respectively. The offset of the connection point provided for the overlapping area.

5. The method according to claim 1, characterized in that, Step 5, establishing the planar regional network adjustment model, includes the following steps: Step 5.1, construct the SAR positioning error model, expressed as: in, This is an index for the SAR intensity map. and These represent row and column coordinates, respectively. and These are the positioning error functions in the row and column directions, respectively. and These are the parameters to be determined for the model; Step 5.2: Based on the absolute and relative positioning constraints provided by the control points and connection points respectively, construct the planar regional network adjustment model: in, and This is an index for the overlapping area of ​​the SAR intensity map. and These are the original row and column numbers, and These are the actual row and column numbers for the registration estimation; Step 5.3, combining the offset estimated in Step 4, further derivation leads to the following functional relationship: in, and The row and column offsets provided by the control points are respectively controlled. and These are the row and column offsets provided for the connection points, respectively; therefore, the error equation can be expressed as: in, and These are the residual matrices for the control points and the connection points, respectively. Step 5.4: Solve for the model parameters by combining robust estimation and the least squares rule, and set the initial weight matrix. Given the identity matrix, solve using the least squares criterion: in, This indicates the matrix transpose; subsequently, the initial weight matrix is ​​updated according to the IGGⅢ scheme. for until the model parameters change. Stop iteration when, where The set threshold; in, To standardize the residuals, and The elimination factor has the following value ranges: and .

6. The method according to claim 1, characterized in that, Step 6 includes the following specific steps: First, the SAR positioning error is corrected based on the parameters of the planar regional network adjustment model estimated in step 5; then, the InSAR altimetry system error is corrected through elevation regional network adjustment; finally, the InSAR terrain products of the plain area are fused and mosaicked.

7. A device for adjusting InSAR planar regional networks in plain areas based on multi-source remote sensing data, characterized in that, include: The data preprocessing module is used to: perform registration interferometry and phase height transformation on the acquired dual-station InSAR data of the plain area to obtain the DEM in the radar coordinate system; The SAR initial target localization module is used to: directly locate the target using the RD model generated by InSAR DEM; establish a lookup table for transforming the radar coordinate system to the geodetic coordinate system; and project the SAR intensity map in the radar coordinate system to the geodetic coordinate system as the target image for planar regional network adjustment. The planar regional network adjustment module is used to: acquire multi-source remote sensing data within the coverage area of ​​SAR imagery, search for overlapping areas between SAR intensity maps, and match control points and tie points using the correlation coefficient method based on Fourier transform; consider positioning errors caused by inaccurate SAR satellite parameters, and establish a planar regional network adjustment model using absolute and relative positioning constraints provided by control points and tie points; solve for model parameters by combining robust estimation and the least squares method, and correct positioning errors scene by scene. The fusion mosaic module is used to: correct the altimetry system error of different InSAR data acquisition strips using elevation regional network adjustment; and fuse and mosaic single-scene terrain products to obtain InSAR terrain products for a large-scale plain area.

8. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, the processor causes the processor to implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

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