Methods, devices, and equipment for monitoring surface deformation based on parameter search interval optimization

Abstract

This invention relates to the field of data processing technology and discloses a method, apparatus, and equipment for monitoring surface deformation based on parameter search interval optimization. The method, based on the location information of multiple buildings in a geographic coordinate system and multiple SAR images, determines at least one building-related region and at least one non-building region within a target area. It then determines a first residual elevation search interval for the building-related region based on the roof elevation value of at least one target building within the building-related region; determines a second residual elevation search interval for the non-building region; and performs temporal InSAR phase calculation based on the first residual elevation search interval, the second residual elevation search interval, a preset deformation rate search interval, and multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area. This significantly improves data processing efficiency while ensuring the accuracy of the temporal InSAR phase calculation and obtaining accurate surface deformation rate and elevation fields.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method, apparatus, and equipment for monitoring land deformation based on parameter search interval optimization. Background Technology

[0002] Temporal synthetic aperture radar interferometry (InSAR) is a remote sensing technology that uses multi-time-point SAR satellite imagery and interferometric phase analysis to achieve long-term monitoring of millimeter-level minute surface deformations, used for surface deformation monitoring. When conducting temporal InSAR measurements, a large number of SAR images are typically registered, differentially interferometrically processed, and the flat and topographic phases are removed before modeling the residual phase. The model usually includes two core physical parameters: linear deformation rate and residual elevation. Finally, a global search interval and step size are preset for these two parameters, and a grid search method is used to traverse all possible parameter combinations. The optimal solution is selected by evaluating coherence indices, thereby achieving surface deformation monitoring. In related technologies, the overall residual elevation search interval for the study area is generally determined based on the user's prior knowledge and experience. However, this method of determining the residual elevation search interval based on human experience is difficult to balance between accuracy and computational efficiency. In densely populated urban areas or areas with large topographic relief, if the parameter search interval for residual elevation is set too narrowly, it will lead to calculation errors because it cannot cover the actual residual elevation. If it is set too wide to avoid errors, it will greatly increase the number of parameter combinations, resulting in a sharp increase in computation time. At the same time, this method is highly subjective, and the experience differences of different operators will lead to inconsistent interval settings, and it is difficult to adapt to the elevation characteristics of different areas. When facing complex areas (such as areas with large differences in the elevation of high-rise buildings and areas with abrupt changes in terrain), the experience-preset interval lacks flexibility. It cannot accurately match the actual elevation characteristics and is prone to missing the actual parameters or redundant calculations, ultimately affecting the reliability and overall efficiency of the surface deformation monitoring results. Summary of the Invention

[0003] This invention provides a method, apparatus, and equipment for monitoring surface deformation based on parameter search interval optimization. This addresses the problem in related technologies where the residual elevation search interval is determined by human experience, which cannot accurately match actual elevation characteristics and is prone to missing real parameters or redundant calculations, ultimately affecting the reliability and overall efficiency of the solution results.

[0004] In a first aspect, the present invention provides a method for monitoring surface deformation based on parameter search interval optimization. The method includes: acquiring multiple SAR images, satellite orbit data, location information of multiple buildings in a geographic coordinate system, and radar imaging geometric parameters of the SAR images of a target area; determining at least one building-related area and at least one non-building area of ​​the target area based on the location information of the multiple buildings in the geographic coordinate system, the multiple SAR images, the satellite orbit data, and the radar imaging geometric parameters of the SAR images; determining a first residual elevation search interval for the building-related area based on the roof elevation value of at least one target building in the building-related area; determining a second residual elevation search interval for the non-building area; and performing time-series InSAR phase calculation based on the first residual elevation search interval, the second residual elevation search interval, a preset deformation rate search interval, and the multiple SAR images to obtain the surface deformation rate field and the residual elevation field of the target area.

[0005] The surface deformation monitoring method based on parameter search interval optimization provided by this invention integrates prior geographic information about buildings, first dividing the target area into building-related areas and non-building areas. Then, for building-related areas with large elevation differences, it dynamically and accurately determines the local search interval of residual elevation parameters, i.e., the first residual elevation search interval, based on the roof elevation value of the target building. This transforms the global and broad absolute parameter search in related technologies into a localized error interval search based on prior knowledge, eliminating reliance on human experience and avoiding the real error caused by overly narrow interval settings in related technologies. It avoids the problems of incomplete residual elevation coverage and calculation errors, while also avoiding the drawbacks of excessively wide intervals leading to a surge in the number of parameter combinations and a dramatic increase in computation time. At the same time, it eliminates the inconsistency in interval settings caused by differences in the experience of different operators, effectively adapts to the elevation characteristics of complex areas such as densely populated urban high-rise areas and areas with large terrain undulations, and significantly reduces the parameter search space. Ultimately, while ensuring the accuracy of time-series InSAR phase calculation and obtaining accurate surface deformation rate fields and residual elevation fields, it significantly improves data processing efficiency and enhances the reliability of surface deformation monitoring results and the intelligence and automation level of the overall technical solution.

[0006] In one optional implementation, the step of determining at least one building-associated region and at least one non-building region of a target area based on the location information of multiple buildings in a geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images includes: mapping each building onto each SAR image based on satellite orbit data, radar imaging geometric parameters, and the location information of multiple buildings, and determining the pixel region of each building in each SAR image; performing semantic region segmentation on the corresponding SAR image based on the pixel region of the building in each SAR image, and determining at least one building-associated region and at least one non-building region of the target area.

[0007] The method provided by this optional implementation combines satellite orbit data and radar imaging geometric parameters to accurately map buildings onto SAR images and determine pixel areas. Based on this, semantic region division is carried out, eliminating the subjectivity of human experience-based division and ensuring that the boundary definition of building-related areas and non-building areas is accurate and objective. This provides an accurate spatial division basis for subsequent differentiated setting of residual elevation search intervals and avoids the drawbacks of the traditional "one-size-fits-all" global division.

[0008] In one optional implementation, the step of performing time-series InSAR phase calculation based on a first residual elevation search interval, a second residual elevation search interval, a preset deformation rate search interval, and multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area includes: preprocessing multiple SAR images to obtain a differential interferometric atlas; and determining the surface deformation rate field and residual elevation field of the target area based on the first residual elevation search interval of building-associated areas, the second residual elevation search interval of non-building-associated areas, the preset deformation rate search interval, and the differential interferometric atlas.

[0009] The method provided in this optional implementation first obtains a differential interferometric atlas by preprocessing SAR images, providing a complete and effective phase data foundation for phase calculation. Then, it combines the differential residual elevation search interval between building-related areas and non-building areas with a preset deformation rate interval to carry out time-series InSAR phase calculation. This not only accurately matches the elevation characteristics of ground features in different areas, avoiding the calculation errors or surge in computational load that are prone to occur in traditional global intervals, but also improves the calculation accuracy of the surface deformation rate field and residual elevation field by relying on the comprehensive phase information of the differential interferometric atlas. At the same time, it speeds up data processing efficiency by reducing the parameter search space, ultimately ensuring the reliability and accuracy of the surface deformation monitoring results in the target area.

[0010] In one optional implementation, the step of determining the first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area further includes: if the building association area is associated with multiple buildings of different heights, traversing the roof elevation values ​​of each building in a pre-constructed three-dimensional building model to determine the first roof elevation value of the first target building and the second roof elevation value of the second target building, wherein the first target building is the tallest building among the multiple buildings of different heights and the second target building is the shortest building among the multiple buildings of different heights; and determining the first residual elevation search interval of the building association area based on the first roof elevation value, the second roof elevation value, and an error tolerance value.

[0011] The method provided in this optional implementation first obtains a differential interferometric atlas by preprocessing SAR images, providing a complete and effective phase data foundation for phase calculation. Then, it combines the differential residual elevation search interval between building-related areas and non-building areas with a preset deformation rate interval to carry out time-series InSAR phase calculation. This not only accurately matches the elevation characteristics of ground features in different areas, avoiding the calculation errors or surge in computational load that are prone to occur in traditional global intervals, but also improves the calculation accuracy of the surface deformation rate field and residual elevation field by relying on the comprehensive phase information of the differential interferometric atlas. At the same time, it speeds up data processing efficiency by reducing the parameter search space, ultimately ensuring the reliability and accuracy of the surface deformation monitoring results in the target area.

[0012] In one alternative implementation, the step of determining the second residual elevation search interval of the non-building area includes: obtaining an initial residual elevation search interval and determining the initial residual elevation search interval as the second residual elevation search interval of the non-building area; or, determining the second residual elevation search interval of the non-building area based on the ground surface elevation value of the non-building area, wherein the ground surface elevation value is determined according to the three-dimensional building model.

[0013] The method provided by this optional implementation offers two flexible solutions for determining the second residual elevation search interval of non-building areas, adapting to different data conditions and monitoring needs: the method of directly using the initial interval is simple to operate and can quickly complete the interval setting, suitable for scenarios lacking detailed ground feature elevation data; the method of determining the interval based on the ground foundation elevation value obtained from the building 3D model allows the interval to better fit the actual elevation characteristics of the non-building area, further eliminating the reliance on human experience.

[0014] Secondly, the present invention provides a surface deformation monitoring device based on parameter search interval optimization. The device includes: an acquisition module for acquiring multiple SAR images, satellite orbit data, location information of multiple buildings in a geographic coordinate system, and radar imaging geometric parameters of the SAR images of a target area; a first determination module for determining at least one building-related area and at least one non-building area of ​​the target area based on the location information of multiple buildings in a geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images; a second determination module for determining a first residual elevation search interval of the building-related area based on the roof elevation value of at least one target building in the building-related area; a third determination module for determining a second residual elevation search interval of the non-building area; and a calculation module for performing time-series InSAR phase calculation based on the first residual elevation search interval, the second residual elevation search interval, a preset deformation rate search interval, and multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area.

[0015] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the surface deformation monitoring method based on parameter search interval optimization described in the first aspect or any corresponding embodiment.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the surface deformation monitoring method based on parameter search interval optimization described in the first aspect or any corresponding embodiment.

[0017] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the surface deformation monitoring method based on parameter search interval optimization described in the first aspect or any corresponding embodiment. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of the first process of a surface deformation monitoring method based on parameter search interval optimization according to an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the second process of the surface deformation monitoring method based on parameter search interval optimization according to an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the third process of the surface deformation monitoring method based on parameter search interval optimization according to an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of pixel regions on a SAR image;

[0024] Figure 6 This is a structural block diagram of a surface deformation monitoring device based on parameter search interval optimization according to an embodiment of the present invention;

[0025] Figure 7 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 embodiments of the present invention, not all embodiments. 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.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the surface deformation monitoring method based on parameter search interval optimization depends is described here. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0030] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0031] Temporal synthetic aperture radar interferometry (InSAR) is a remote sensing technology that uses multi-time-point SAR satellite imagery and interferometric phase analysis to achieve long-term monitoring of millimeter-level minute deformations of the Earth's surface, used for surface deformation monitoring. When performing temporal InSAR measurements, a large number of SAR images are typically registered, differentially interferometrically processed, and the flat and topographic phases are removed before modeling the residual phase. The model usually includes two core physical parameters: linear deformation rate and residual elevation. During the solution process, a global search interval and step size must be preset for these two parameters. A grid search method is used to traverse all possible parameter combinations, and the optimal solution is selected by evaluating coherence indices, thus achieving surface deformation monitoring. The model is shown in the following formula:

[0032]

[0033] in, The residual phase of the first i-th interferometric pair is the phase value remaining after removing interference from flat ground, terrain, etc., from the original differential interferometric phase, which contains deformation and residual elevation information. The original differential interferometric phase of the i-th interferometric pair is generated from two SAR images and includes various signals such as topography, deformation, and atmospheric delay. This indicates the operating wavelength of the SAR sensor, which is the wavelength of the electromagnetic waves emitted by the radar. The average slant distance from the satellite to the target monitoring point refers to the straight-line distance from the satellite radar antenna to the point on the ground surface. The average incident angle of a SAR image is the angle between the radar beam and the surface normal, reflecting the radar's observation angle. The vertical baseline component of the i-th interferometric pair is the baseline length of the satellite orbits corresponding to the two SAR images in the direction perpendicular to the line of sight. It is a core parameter that affects the terrain phase. Representing the residual elevation parameter, it is a correction to the initial digital elevation model (DEM), reflecting the error of the initial elevation, and is one of the core parameters to be solved; The time baseline of the i-th interferometric pair is the time difference between the acquisition of two SAR images, expressed in years or days, reflecting the time span of deformation. The linear deformation rate is the long-term average deformation rate of the monitoring point, reflecting the speed of surface subsidence or uplift, and is one of the core parameters to be solved.

[0034] In related technologies, the search interval for residual elevation of the entire study area is generally determined by the user's prior knowledge and experience. However, this method of determining the search interval based on human experience is difficult to balance accuracy and computational efficiency. In densely populated urban areas or areas with large topographic relief, if the parameter search interval for residual elevation is set too narrowly, it will lead to calculation errors because it cannot cover the true residual elevation. If it is set too wide to avoid errors, the number of parameter combinations will increase significantly, resulting in a sharp increase in computation time. At the same time, this method is highly subjective, and the differences in experience among different operators will lead to inconsistent interval settings, and it is difficult to adapt to the elevation characteristics of different areas. When facing complex areas (such as areas with large differences in the elevation of high-rise buildings or areas with abrupt topographic changes), the experience-preset interval lacks flexibility, cannot accurately match the actual elevation characteristics, and is prone to missing true parameters or redundant calculations, ultimately affecting the reliability and overall efficiency of surface deformation monitoring results.

[0035] In view of this, this application provides a surface deformation monitoring method based on parameter search interval optimization, which can be applied to a server to realize surface deformation monitoring. The method provided in this application, by integrating prior geographic information about buildings, first divides the target area into building-related areas and non-building areas. Then, for building-related areas with large elevation differences, it dynamically and accurately determines the local search interval of residual elevation parameters, namely the first residual elevation search interval, based on the roof elevation value of the target building. This transforms the global and broad absolute parameter search in related technologies into a localized error interval search based on prior knowledge, eliminating the reliance on human experience. It avoids the problems of incomplete coverage of the true residual elevation and calculation errors caused by excessively narrow interval settings in related technologies, and also avoids the drawbacks of a surge in the number of parameter combinations and a sharp increase in calculation time caused by excessively wide intervals. At the same time, it eliminates the inconsistency in interval settings caused by differences in the experience of different operators, effectively adapts to the elevation characteristics of complex areas such as densely built-up urban areas and areas with large topographic relief, and significantly reduces the parameter search space. Ultimately, while ensuring the accuracy of time-series InSAR phase calculation and obtaining accurate surface deformation rate field and residual elevation field, it significantly improves data processing efficiency and enhances the reliability of surface deformation monitoring results and the intelligence and automation level of the overall technical solution.

[0036] According to an embodiment of the present invention, a method for monitoring surface deformation based on parameter search interval optimization is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] This embodiment provides a surface deformation monitoring method based on parameter search interval optimization, which can be used in the aforementioned server. Figure 2This is a flowchart of a surface deformation monitoring method based on parameter search interval optimization according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps:

[0038] Step S201: Acquire multiple SAR images of the target area, satellite orbit data, location information of multiple buildings in the geographic coordinate system, and radar imaging geometric parameters of the SAR images.

[0039] For example, the target area can be an area requiring surface deformation monitoring, and multiple SAR images can be time-series data formed by registering N (N≥20, usually 30-60) SAR satellite images in the same orbital direction within the target area. Satellite orbit data records parameters such as orbital position, attitude, and orbital altitude when the SAR satellite acquires images, and is the key basis for realizing the conversion between the geographic coordinate system and the radar image coordinate system and calculating the interferometric baseline. The location information of multiple buildings in the geographic coordinate system is the spatial information such as the planar coordinates and elevation of the buildings in the target area within a unified geographic reference frame. In this embodiment, the location information of multiple buildings in the geographic coordinate system can be obtained through the 3D model of the buildings in the target area. The 3D model of the buildings is a key prior information source, which can be obtained through UAV oblique photogrammetry, airborne lidar scanning, existing city information models (CIM) or building white models, high-resolution stereo satellite mapping, etc. Its form can be a rasterized digital surface model (DSM) or a building model with vector contours and height. The radar imaging geometry parameters of SAR images include parameters such as radar incident angle, side view angle, range and azimuth resolution, which reflect the radar imaging geometry.

[0040] Step S202: Based on the location information of multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images, determine at least one building-related area and at least one non-building area of ​​the target area.

[0041] For example, in this embodiment of the application, the known location information of buildings is used, combined with the geographical coverage of multiple SAR images, to identify areas within the target area that overlap with the spatial location of buildings and define them as building-related areas; while areas that do not overlap with any building location are defined as non-building areas.

[0042] Step S203: Determine the first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area.

[0043] For example, in this embodiment of the application, the roof elevation of the target building within the building association area is used as a priori benchmark, and a reasonable error range is set around the elevation. This range is then directly used as the residual elevation search interval of the building association area.

[0044] Step S204: Determine the second residual elevation search interval for the non-building area.

[0045] For example, in one embodiment of this application, the second residual elevation search interval for non-building areas can be determined based on past experience, and this embodiment of the application does not impose specific limitations.

[0046] Step S205: Based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval, and multiple SAR images, perform time-series InSAR phase calculation to obtain the surface deformation rate field and residual elevation field of the target area.

[0047] For example, using regionally customized residual elevation search intervals (the first interval for building-related areas and the second interval for non-building areas) and preset deformation rate search intervals as parameter constraints, and using multiple SAR images as data basis, time-series InSAR phase calculations are carried out. By separating atmospheric, noise and other interference signals in the phase, the optimal deformation rate and residual elevation parameters of each monitoring point in the target area are solved. Finally, these single-point parameters are integrated according to spatial location to form a surface deformation rate field and residual elevation field covering the entire target area.

[0048] The surface deformation monitoring method based on parameter search interval optimization provided in this embodiment first divides the target area into building-related areas and non-building areas by integrating prior geographic information of buildings. Then, for building-related areas with large elevation differences, the local search interval of residual elevation parameters, namely the first residual elevation search interval, is dynamically and accurately determined based on the roof elevation value of the target building. This transforms the global and broad absolute parameter search in related technologies into a localized error interval search based on prior knowledge, eliminating the reliance on human experience and avoiding the true error caused by overly narrow interval settings in related technologies. It addresses the issues of incomplete residual elevation coverage and calculation errors, while avoiding the drawbacks of excessively wide intervals leading to a surge in the number of parameter combinations and a dramatic increase in computation time. It also eliminates inconsistencies in interval settings caused by differences in operator experience, effectively adapting to the elevation characteristics of complex areas such as densely populated urban high-rise areas and areas with large terrain undulations. This significantly reduces the parameter search space, ultimately ensuring the accuracy of time-series InSAR phase calculations and obtaining accurate surface deformation rate and residual elevation fields, while significantly improving data processing efficiency and enhancing the reliability of surface deformation monitoring results and the overall intelligence and automation level of the technical solution.

[0049] This embodiment provides a surface deformation monitoring method based on parameter search interval optimization, which can be used in the aforementioned server. Figure 3 This is a flowchart of a surface deformation monitoring method based on parameter search interval optimization according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps:

[0050] Step S301: Acquire multiple SAR images of the target area, satellite orbit data, location information of multiple buildings in the geographic coordinate system, and radar imaging geometric parameters of the SAR images. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0051] Step S302: Based on the location information of multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images, determine at least one building-related area and at least one non-building area of ​​the target area.

[0052] Specifically, step S302 includes:

[0053] Step S3021: Based on satellite orbit data, radar imaging geometric parameters, and the location information of multiple buildings, each building is mapped onto each SAR image to determine the pixel area of ​​each building in each SAR image.

[0054] For example, in the embodiments of this application, the function interface provided by mature software (such as Gamma, SARscape) can be used to input satellite orbit parameters, radar imaging geometric parameters and the location information of multiple buildings, and through precise geometric positioning calculations, the corresponding pixel or pixel area of ​​each building on each SAR image can be determined.

[0055] Step S3022: Based on the pixel region of the building in each SAR image, perform semantic region division on the corresponding SAR image to determine at least one building-related region and at least one non-building region of the target area.

[0056] For example, semantic partitioning is performed on each SAR image based on the pixel range of buildings in the SAR image. First, the specific pixel area occupied by each building in the corresponding SAR image is determined. Then, the portion of the image containing these building pixels is defined as the building-related region, and the portion not containing any building pixels is defined as the non-building region, ultimately achieving spatial partitioning of the target area. In this embodiment, the building-related region is used to represent a set of pixels that have a stable mapping relationship with one or more ground building models. The non-building region is used to represent a set of pixels that cannot establish a stable mapping relationship with any building model, typically corresponding to roads, squares, green spaces, bare soil, or water bodies.

[0057] Step S303: Determine the first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area.

[0058] Specifically, step S303 includes:

[0059] Step S3031: If the building association area is associated with only one independent building, the independent building in the building association area shall be taken as the target building.

[0060] For example, if the building association area on the SAR image is associated with only one independent building, that independent building is taken as the target building.

[0061] Step S3032: Extract the roof elevation value of the target building from the pre-built 3D building model.

[0062] For example, in this embodiment of the application, the roof elevation value H_building of the target building is extracted from the three-dimensional building model.

[0063] Step S3033: Determine the first residual elevation search interval of the building-related area based on the roof elevation value and the preset error tolerance value.

[0064] For example, in this embodiment of the application, a conservative error tolerance value Δh is preset, centered on the roof elevation value H_building of the target building (e.g., Δh = 5 meters is set based on model accuracy and measurement uncertainty). Then, the residual elevation search interval corresponding to this area is [H_building - Δh, H_building + Δh]. This interval reflects the possible slight deviation of the "actual radar scattering phase center height" from the "known building model height".

[0065] Step S304: Determine the second residual elevation search interval for the non-building area.

[0066] Specifically, step S304 includes:

[0067] Obtain the initial residual elevation search interval, and define the initial residual elevation search interval as the second residual elevation search interval for the non-building area; or,

[0068] The second residual elevation search interval for the non-building area is determined based on the ground foundation elevation value of the non-building area, which is determined according to the three-dimensional building model.

[0069] For example, the initial residual elevation search interval is a residual elevation search interval within the target area determined based on human experience. The initial residual elevation search interval can be determined as the second residual elevation search interval for non-building areas, or it can be determined based on the surface elevation value of the non-building area. In this embodiment, for non-building areas such as roads and open spaces, there is a lack of prior building heights. In this case, the external DEM elevation value H_dem corresponding to that location can be used as a reference benchmark. Considering factors such as surface roughness and vegetation, a relatively lenient but still much smaller error tolerance Δh (e.g., Δh = 15 meters) can be set for such areas. Then the search interval for this area is [H_dem - Δh, H_dem + Δh]. Another optimization strategy is to assign different Δh values ​​according to the semantic category of the area in the optical image (e.g., "asphalt road" or "slope").

[0070] Step S305: Based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval, and multiple SAR images, time-series InSAR phase calculation is performed to obtain the surface deformation rate field and residual elevation field of the target area. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0071] This embodiment provides a surface deformation monitoring method based on parameter search interval optimization, which can be used in the aforementioned server. Figure 4 This is a flowchart of a surface deformation monitoring method based on parameter search interval optimization according to an embodiment of the present invention, as shown below. Figure 4 As shown, the process includes the following steps:

[0072] Step S401: Acquire multiple SAR images of the target area, satellite orbit data, location information of multiple buildings in the geographic coordinate system, and radar imaging geometric parameters of the SAR images. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0073] Step S402: Based on the location information of multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images, determine at least one building-associated area and at least one non-building area within the target area. For details, please refer to [link to relevant documentation]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0074] Step S403: Determine the first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area.

[0075] Specifically, step S403 includes:

[0076] Step S4031: If the building association area is associated with multiple buildings of different heights, the roof elevation values ​​of each building are traversed in the pre-constructed 3D building model to determine the first roof elevation value of the first target building and the second roof elevation value of the second target building. The first target building is the tallest building among the multiple buildings of different heights, and the second target building is the shortest building among the multiple buildings of different heights.

[0077] For example, in this embodiment of the application, if the mapping projection of a building-related area on a SAR image overlaps with that of multiple buildings of different heights (such as adjacent building groups), then the roof elevation values ​​of all related buildings are traversed to find the maximum value H_max and the minimum value H_min.

[0078] Step S4032: Determine the first residual elevation search interval of the building-related area based on the first roof elevation value, the second roof elevation value, and the error tolerance value.

[0079] For example, in this embodiment of the application, the first residual elevation search interval can be determined as [H_min-Δh, H_max+Δh]. This interval covers the height range of all possible dominant scattering sources within the mixing region.

[0080] Step S404: Based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval, and multiple SAR images, time-series InSAR phase calculation is performed to obtain the surface deformation rate field and residual elevation field of the target area. For details, please refer to [link to relevant documentation]. Figure 3 Step S304 of the illustrated embodiment will not be described again here.

[0081] Step S405: Based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval, and multiple SAR images, perform time-series InSAR phase calculation to obtain the surface deformation rate field and residual elevation field of the target area.

[0082] Specifically, step S405 includes:

[0083] Step S4051: Preprocess multiple SAR images to obtain differential interferometric atlases.

[0084] For example, in this embodiment of the application, a SAR image is selected as the common master image and the rest are used as slave images to generate N-1 interferometric pairs; the flat phase and topographic phase of each interferometric pair are removed using external DEM and orbital data to form a series of differential interferograms; subsequently, common errors such as atmospheric delay phase can be estimated and removed as needed to obtain a differential interferogram set dominated by residual phase.

[0085] Step S4052: Based on the first residual elevation search interval of the building-related area, the second residual elevation search interval of the non-building-related area, the preset deformation rate search interval, and the differential interferometric atlas, determine the surface deformation rate field and residual elevation field of the target area.

[0086] For example, in this embodiment of the application, the differential residual elevation search interval of the building-related area and the non-building area and the preset deformation rate search interval are used as parameter constraint boundaries to limit the parameter range of the solution. Then, relying on the phase data provided by the differential interferometric atlas with residual phase as the main component, the phase signals corresponding to the surface deformation and residual elevation are separated through phase modeling and parameter inversion. Finally, the single-point solution results are integrated according to the spatial distribution to form a surface deformation rate field and residual elevation field covering the target area.

[0087] The following specific embodiment illustrates the surface deformation monitoring method based on parameter search interval optimization provided in this application.

[0088] Example:

[0089] The surface deformation monitoring method based on parameter search interval optimization provided in this application includes the following steps:

[0090] Step 1: Acquire multi-temporal SAR data and auxiliary data. Collect N (N≥20, typically 30-60) SAR satellite images covering the monitoring area and along the same orbital direction to form a time-series dataset. Simultaneously, acquire necessary auxiliary data, including but not limited to: 1) a digital elevation model (DEM) of the monitoring area, such as SRTM DEM or higher-precision DEM data, for initial terrain phase removal; 2) precise satellite orbit data; 3) a 3D elevation model of urban buildings in the monitoring area. This 3D model is a crucial source of prior information and can be acquired through UAV oblique photogrammetry, airborne lidar scanning, existing city information models (CIM) or building white models, high-resolution stereo satellite mapping, etc. Its form can be a rasterized digital surface model (DSM) or a building model with vector contours and heights.

[0091] Step 2 involves preprocessing the multi-temporal SAR data to generate a differential interferometric atlas. This step is part of the standard PSInSAR processing front-end workflow. Specifically, it includes: finely registering all SAR images; selecting one image as the common master image and the rest as slave images to generate N-1 interferometric pairs; using external DEM and orbital data to remove the flat-ground phase and topographic phase from each interferometric pair, forming a series of differential interferograms; subsequently, common errors such as atmospheric delay phase can be estimated and removed as needed to obtain a differential interferometric atlas dominated by residual phase.

[0092] Step 3: Establish the mapping relationship between the 3D model of urban buildings and SAR imagery, and perform semantic region division on the SAR imagery. This is one of the key steps of this invention. The goal of this step is to map the 3D information of buildings in the geographic coordinate system onto the SAR imagery in the radar slant range-azimuth coordinate system. In practice, mature software (such as Gamma, SARscape) can be used to provide function interfaces, inputting satellite orbit parameters, radar imaging parameters, and 3D building models. Through precise geometric positioning calculations, the corresponding pixel or pixel region of each building on each SAR image can be determined. A specific schematic diagram of the pixel region on the SAR imagery can be shown below. Figure 5 As shown, A and C represent building-related areas, and B represents non-building areas. Through geometric mapping, the projected outlines of buildings and roads A, B, and C on the ground are accurately mapped onto specific areas A, B, and C in the SAR image. Based on this mapping relationship, semantic region division can be performed on the entire SAR image.

[0093] (1) Building-related region: A set of pixels that have a stable mapping relationship with one or more ground building models. For example... Figure 5 Regions A and C in the diagram. These regions are typically densely populated areas of candidate permanent scatterers (PS) with stable, strong scattering properties.

[0094] (2) Non-building areas: A set of pixels that cannot establish a stable mapping relationship with any building model, typically corresponding to roads, squares, green spaces, bare soil, or water bodies. For example... Figure 5 Region B in the text.

[0095] Step 4: For each segmented region, adaptively determine the search interval for the residual elevation parameters based on prior elevation information. Traditional methods use a uniform residual elevation search range (e.g., [-100 meters, +100 meters]) for all pixels. This application's embodiment abandons this approach, customizing the search interval for each segmented region (or even down to each pixel). The specific rules are as follows:

[0096] 1. For a single building associated with a region: If a region on a SAR image is associated with only one independent building (e.g., ... Figure 2 If the corresponding building A is associated with the building, then the roof elevation value H_building of that building is extracted from the 3D model of the building. Using this elevation value as the center, a conservative error tolerance Δh is preset (for example, Δh = 5 meters based on model accuracy and measurement uncertainty). The residual elevation search interval for this area is then [H_building - Δh, H_building + Δh]. This interval reflects the possible slight deviation between the "actual radar scattering phase center height" and the "known building model height".

[0097] 2. For mixed areas with multiple buildings: If the mapped projections of a certain area on the SAR image overlap with those of multiple buildings of different heights (such as adjacent building complexes), then the roof elevation values ​​of all associated buildings are traversed to find the maximum value H_max and the minimum value H_min. The residual elevation search interval for this area is then determined as [H_min-Δh, H_max+Δh]. This interval covers the height range of all possible dominant scattering sources within the mixed area.

[0098] 3. For non-building areas: For non-building areas such as roads and open spaces, there is a lack of prior building heights. In this case, the external DEM elevation value H_dem corresponding to the location can be used as a reference benchmark. Considering factors such as surface roughness and vegetation, a relatively loose but still much smaller error tolerance Δh (e.g., Δh = 15 meters) can be set for these areas. Then the search interval for this area is [H_dem - Δh, H_dem + Δh]. Another optimization strategy is to assign different Δh values ​​according to the semantic category of the area in the optical image (e.g., "asphalt road" or "slope").

[0099] In this way, the entire SAR image is given a spatially varying residual elevation search interval matrix that is closely related to ground features. The search interval for each pixel is reduced from a wide range that could be hundreds of meters to a narrow range that is usually only ten to tens of meters.

[0100] Step 5: Perform temporal InSAR phase calculation based on the adaptive search interval to obtain deformation results. This step integrates the aforementioned dynamically generated search interval within the traditional PSInSAR phase calculation framework. For each candidate point on the image (such as a PS point or a distributed scatterer DS), parameter estimation is performed as follows:

[0101] Deformation rate parameter: Its search range can still maintain a reasonable physical range based on prior knowledge (such as [-50,+50] mm / year), or be optimized synchronously with the elevation search.

[0102] Residual elevation parameters: The customized narrow interval determined for the location of this point in step S104 is directly adopted, replacing the original global wide interval. Subsequently, a grid search or a more optimized parameter estimation algorithm is used to traverse the significantly reduced parameter combination space. For each parameter combination, its corresponding temporal phase model is calculated, and the temporal coherence of this point on all interferometric pairs is evaluated. Finally, the parameter combination that maximizes coherence is selected as the optimal deformation rate and residual elevation estimate for this point. Due to the significant reduction in the search interval, the number of parameter combinations that need to be traversed decreases by an order of magnitude, thereby greatly shortening the computation time. At the same time, because the search interval closely surrounds the true physical height, it avoids finding spurious coherence extrema in incorrect height intervals, thus improving the accuracy and reliability of parameter estimation.

[0103] Step 6: Output high-precision deformation rate field and residual elevation field. After traversing all candidate points and completing the calculation, the surface deformation rate field with millimeter-level accuracy for the monitoring area and the optimized residual elevation field (reflecting the fine adjustment of the scattering center height) are obtained.

[0104] In summary, the embodiments of this application, through the technical path of "prior information mapping - semantic region division - interval dynamic customization", deeply integrate static urban spatial information into the dynamic temporal InSAR processing flow, intelligently guide the parameter search process, and effectively solve the contradiction between computational efficiency and solution accuracy in related methods. It is particularly suitable for high-precision monitoring scenarios of urban surface deformation with abundant and stable building scatterers.

[0105] The method provided in this application significantly improves computational efficiency by converting the broad absolute parameter search into a narrow-band error interval search based on prior information, greatly reducing the scale of parameter space traversal and shortening data processing time. Secondly, it improves deformation calculation accuracy by customizing the search interval for each pixel or local region, avoiding model errors caused by improper global interval settings, and making the estimation of deformation rate and residual elevation more accurate. Thirdly, it lowers the technical application threshold by reducing reliance on user prior experience and repeated debugging, thus improving the automation and intelligence level of the temporal InSAR technology processing flow. Finally, it enhances method reliability by incorporating a high-precision urban 3D model as a constraint, improving the stability of the parameter calculation process and the interpretability of the results, making it particularly suitable for complex urban environments with dense buildings and drastic elevation changes.

[0106] Furthermore, in this embodiment, in addition to the 3D building model, UAV oblique photogrammetry 3D models, airborne or vehicle-mounted LiDAR point cloud data, and DSM generated by high-resolution satellite stereo mapping can be used as prior elevation data sources. When establishing the mapping relationship between SAR images and the 3D model, besides relying on specific software interfaces, it can be implemented by programming based on the conversion principle between the geometric radar coordinate system and the geographic coordinate system. For SAR image segmentation, image semantic segmentation algorithms can be used to automatically identify the scattering areas of buildings and then associate them with the geographic model. For areas associated with multiple buildings, in addition to taking the elevation extreme values, the average elevation or the main distribution range can also be calculated as a benchmark. For areas without buildings, a higher-precision DEM or a combination of optical image classification results (distinguishing between roads, green spaces, and water bodies) can be used to set differentiated initial elevations and search ranges.

[0107] This embodiment also provides a surface deformation monitoring device based on parameter search interval optimization. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0108] This embodiment provides a surface deformation monitoring device based on parameter search interval optimization, such as... Figure 6 As shown, it includes:

[0109] The acquisition module 601 is used to acquire multiple SAR images of the target area, satellite orbit data, location information of multiple buildings in the geographic coordinate system, and radar imaging geometric parameters of the SAR images;

[0110] The first determining module 602 is used to determine at least one building-related area and at least one non-building area of ​​the target area based on the location information of multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data and radar imaging geometric parameters of the SAR images.

[0111] The second determining module 603 is used to determine a first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area;

[0112] The third determining module 604 is used to determine the second residual elevation search interval for the non-building area;

[0113] The solution module 605 is used to perform time-series InSAR phase solution based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval and multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area.

[0114] In some alternative implementations, the first determining module 602 includes:

[0115] The first determination submodule is used to map each building onto each SAR image based on satellite orbit data, radar imaging geometric parameters, and the location information of multiple buildings, and to determine the pixel area of ​​each building in each SAR image.

[0116] The second determination submodule is used to perform semantic region division of the corresponding SAR image based on the pixel region of the building in each SAR image, and to determine at least one building-related region and at least one non-building region of the target area.

[0117] In some alternative implementations, the solution module 605 includes:

[0118] The processing submodule is used to preprocess multiple SAR images to obtain differential interferometric atlases;

[0119] The third determination submodule is used to determine the surface deformation rate field and residual elevation field of the target area based on the first residual elevation search interval of the building-related area, the second residual elevation search interval of the non-building-related area, the preset deformation rate search interval, and the differential interferometric atlas.

[0120] In some alternative implementations, the second determining module 603 includes:

[0121] The fourth determination submodule is used to select the independent building in the building association area as the target building if the building association area is associated with only one independent building.

[0122] The fifth determination submodule is used to extract the roof elevation value of the target building from the pre-built 3D building model;

[0123] The sixth determination submodule is used to determine the first residual elevation search interval of the building-related area based on the roof elevation value and the preset error tolerance value.

[0124] In some alternative implementations, the solver module 605 further includes:

[0125] The seventh determination submodule is used to determine the first roof elevation value of the first target building and the second roof elevation value of the second target building if the building association area is associated with multiple buildings of different heights. The first target building is the tallest building among the multiple buildings of different heights, and the second target building is the shortest building among the multiple buildings of different heights.

[0126] The eighth determination submodule is used to determine the first residual elevation search interval of the building-related area based on the first roof elevation value, the second roof elevation value, and the error tolerance value.

[0127] In some alternative implementations, the third determining module 604 includes:

[0128] The acquisition submodule is used to acquire the initial residual elevation search interval and determine the initial residual elevation search interval as the second residual elevation search interval for the non-building area; or, the ninth determination submodule is used to determine the second residual elevation search interval for the non-building area based on the ground surface foundation elevation value of the non-building area, wherein the ground surface foundation elevation value is determined according to the three-dimensional building model.

[0129] The surface deformation monitoring device based on parameter search interval optimization provided in this embodiment of the invention can execute the surface deformation monitoring method based on parameter search interval optimization provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0130] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0131] The following is a detailed reference. Figure 7 This diagram illustrates a suitable structural schematic for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 701, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 702 or a program loaded from memory 707 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device. The processor 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0132] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 707 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 7 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0133] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 709, or installed from a memory 707, or installed from a ROM 702. When the computer program is executed by the processor 701, it performs the functions defined in the surface deformation monitoring method based on parameter search interval optimization according to embodiments of the present invention.

[0134] Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0135] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the surface deformation monitoring method based on parameter search interval optimization shown in the above embodiments is implemented.

[0136] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0137] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for surface deformation monitoring based on parameter search interval optimization, characterized in that, The method includes: Acquire multiple SAR images of the target area, satellite orbit data, location information of multiple buildings in the geographic coordinate system, and radar imaging geometric parameters of the SAR images; Based on the location information of the multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images, at least one building-related area and at least one non-building area of ​​the target area are determined. The first residual elevation search interval of the building association area is determined based on the roof elevation value of at least one target building in the building association area; Determine the second residual elevation search interval for the non-building area; Based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval, and the multiple SAR images, time-series InSAR phase calculation is performed to obtain the surface deformation rate field and residual elevation field of the target area. Based on the location information of the multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images, the step of determining at least one building-associated area and at least one non-building area of ​​the target area includes: Based on the satellite orbit data, the radar imaging geometric parameters, and the location information of multiple buildings, each building is mapped onto each SAR image to determine the pixel area of ​​each building in each SAR image. Based on the pixel region of the building in each SAR image, the corresponding SAR image is semantically divided to determine at least one building-related region and at least one non-building region of the target area.

2. The method of claim 1, wherein, The steps of performing time-series InSAR phase calculation based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval, and the multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area include: The multiple SAR images are preprocessed to obtain a differential interferometric atlas; Based on the first residual elevation search interval of the building-related area, the second residual elevation search interval of the non-building-related area, the preset deformation rate search interval, and the differential interferometric atlas, the surface deformation rate field and residual elevation field of the target area are determined.

3. The method according to claim 1, characterized in that, The step of determining the first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area includes: If the building association area is associated with only one independent building, the independent building in the building association area shall be taken as the target building; Extract the roof elevation value of the target building from a pre-built 3D architectural model; The first residual elevation search interval for the building-related area is determined based on the roof elevation value and the preset error tolerance value.

4. The method according to any one of claims 1 to 3, characterized in that, The step of determining the first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area further includes: If the building association area is associated with multiple buildings of different heights, the roof elevation values ​​of each building are traversed in the pre-built 3D building model to determine the first roof elevation value of the first target building and the second roof elevation value of the second target building. The first target building is the tallest building among the multiple buildings of different heights, and the second target building is the shortest building among the multiple buildings of different heights. The first residual elevation search interval for the building-related area is determined based on the first roof elevation value, the second roof elevation value, and the error tolerance value.

5. The method according to claim 4, characterized in that, The step of determining the second residual elevation search interval of the non-building area includes: Obtain the initial residual elevation search interval, and determine the initial residual elevation search interval as the second residual elevation search interval for the non-building area; or, The second residual elevation search interval of the non-building area is determined based on the ground foundation elevation value of the non-building area, wherein the ground foundation elevation value is determined according to the building 3D model.

6. A surface deformation monitoring device based on parameter search interval optimization, characterized in that, The device includes: The acquisition module is used to acquire multiple SAR images of the target area, satellite orbit data, the location information of multiple buildings in the geographic coordinate system, and the radar imaging geometric parameters of the SAR images; The first determining module is used to determine at least one building-related area and at least one non-building area of ​​the target area based on the location information of the multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data and radar imaging geometric parameters of the SAR images. The second determining module is used to determine the first residual elevation search interval of the building association area based on the roof elevation value of at least one target building in the building association area; The third determining module is used to determine the second residual elevation search interval of the non-building area; The calculation module is used to perform time-series InSAR phase calculation based on the first residual elevation search interval, the second residual elevation search interval, the preset deformation rate search interval, and the multiple SAR images to obtain the surface deformation rate field and residual elevation field of the target area. Based on the location information of the multiple buildings in the geographic coordinate system, multiple SAR images, satellite orbit data, and radar imaging geometric parameters of the SAR images, the step of determining at least one building-associated area and at least one non-building area of ​​the target area includes: Based on the satellite orbit data, the radar imaging geometric parameters, and the location information of multiple buildings, each building is mapped onto each SAR image to determine the pixel area of ​​each building in each SAR image. Based on the pixel region of the building in each SAR image, the corresponding SAR image is semantically divided to determine at least one building-related region and at least one non-building region of the target area.

7. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the surface deformation monitoring method based on parameter search interval optimization as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the surface deformation monitoring method based on parameter search interval optimization as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the surface deformation monitoring method based on parameter search interval optimization as described in any one of claims 1 to 5.

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