A method for updating digital elevation models based on InSAR surface deformation

Abstract

This invention relates to a method for updating a digital elevation model (DEM) based on InSAR surface deformation. The method acquires surface deformation monitoring results of a target area using time-series InSAR technology. When the accuracy of the surface deformation monitoring results is verified by CORS stations, surface deformation zones are extracted from the monitoring results of the target area based on different geographical conditions by setting thresholds for surface deformation rate or surface deformation amount. Point cloud data of the surface deformation zones are acquired using airborne lidar. After data preprocessing, coordinate transformation, point cloud classification, and rasterization of the DEM, the DEM within the surface deformation zones is updated. Stable area DEM data is collected based on the principle of optimal current availability and mosaicked to form the updated DEM. Differential processing and leveling data analysis are performed on the two-phase DEM of the target area to verify the reliability of the surface deformation zones, achieving rapid updating of the DEM.

Description

Technical Field

[0001] This invention relates to the field of topographic surveying and modeling technology, and in particular to a method for updating a digital elevation model based on InSAR surface deformation. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Digital elevation models (DEMs) serve as the core data foundation for real-world 3D geospatial frameworks, and their accuracy and timeliness directly impact the effectiveness of terrain representation, spatial analysis, and applications in real-world 3D scenes. The need for real-time reflection of surface morphological changes using DEM data is increasingly urgent in fields such as new infrastructure construction, urban dynamics, and natural disaster monitoring.

[0004] Conventional methods for updating digital elevation models (DEMs) based on airborne LiDAR point clouds are characterized by high accuracy, but these methods are inherently costly, subject to numerous limitations, and struggle to achieve large-area, frequent updates. Current problems include: firstly, insufficient update continuity, hindering "dynamic monitoring." Traditional methods often involve "periodic updates" (e.g., every 5-10 years), which fail to capture high-frequency changes in the Earth's surface and cannot meet the demands of refined applications. Secondly, insufficient accuracy in the updated area leads to large-scale redundant measurements, resulting in high production costs and long production cycles. Therefore, there is an urgent need to explore an efficient and economical mechanism for the routine updating of DEMs. Summary of the Invention

[0005] To solve the above-mentioned technical problems, or at least partially solve them, this invention provides a method for updating a digital elevation model based on InSAR surface deformation.

[0006] This invention provides a method for updating a digital elevation model based on InSAR surface deformation, comprising: The surface deformation monitoring results of the target area are obtained based on the time-series InSAR technology, where the range of the target area is larger than the range of the synthetic aperture radar interferometric image. When the accuracy of the surface deformation monitoring results is verified by CORS stations and meets the requirements, the surface deformation zone can be extracted from the surface deformation monitoring results of the target area by setting a threshold for the surface deformation rate or the surface deformation amount, depending on different geographical conditions. Point cloud data of the surface deformation area is acquired by airborne lidar, and the digital elevation model in the surface deformation area is updated after data preprocessing, coordinate transformation, point cloud classification, and rasterized digital elevation model construction. Data of digital elevation models in stable areas are collected based on the principle of optimal current availability, and then mosaicked to form an updated digital elevation model. The reliability of the surface deformation area is verified by differential processing and leveling data analysis of the two phases of digital elevation models of the target area, so as to realize the rapid updating of the digital elevation model.

[0007] Furthermore, permanent scatterer interferometry (PS-InSAR) technology is used for monitoring. Since the target area is larger than the range of synthetic aperture radar interferometry imagery, when collecting surface deformation monitoring results for the target data, a unified benchmark is established by combining inverse range weighted interpolation and system bias fitting to eliminate system bias. On the unified benchmark, strip data or adjacent orbit data are stitched together to obtain surface deformation monitoring results covering the target area.

[0008] Furthermore, the method of eliminating system bias based on a combination of inverse distance weighted interpolation and system bias fitting to unify the benchmark, and to stitch together strip data or adjacent track data on the unified benchmark, includes: Find the common area between the two sets of data to be spliced; In the public area, the deformation values ​​of the first set of data are interpolated to the positions of the permanent scatterers of the second set of data using the inverse distance weighted interpolation method, resulting in a perfectly matched pair of permanent scatterer points in space. Calculate the difference between the deformation values ​​of the permanent scatterer point pairs to obtain the difference field, which represents the systematic deviation between the two sets of data to be stitched together; The system deviation surface is obtained by surface fitting of the discrete difference field using the robust least squares method. Based on the system deviation surface, the reference datum of the first set of data is unified to the datum of the second set of data, and data splicing is achieved on the unified datum.

[0009] Furthermore, when screening for surface deformation areas, the cumulative deformation calculation using the optimal reference point selected from permanent scatterers includes: firstly, screening permanent scatterers with reflection intensities higher than a threshold; using Delaunay to combine permanent scatterers and solve the model to select the optimal reference point; using the optimal reference point as a benchmark, controlling the correlation of permanent scatterers after removing the atmospheric delay phase to 0.8-1.0; decomposing the deformation, elevation, and atmospheric components in the interference phase; and then obtaining the cumulative deformation.

[0010] Furthermore, the step of acquiring point cloud data of the surface deformation zone through airborne lidar, and then performing data preprocessing, coordinate transformation, point cloud classification, and rasterized digital elevation model construction to update the digital elevation model within the surface deformation zone includes: Flight routes are designed based on the extent of surface deformation zones, point cloud density requirements, and terrain. Airborne lidar was used to acquire point cloud data of the surface deformation zone along the flight path, and synchronous image data was also acquired. During data preprocessing, differential GNSS / IMU integrated navigation solution technology is used to fuse laser ranging data with high-precision position and attitude information to calculate the three-dimensional coordinates of each laser point in the point cloud in the WGS84 coordinate system; the point cloud data of adjacent flight strips are stitched together to form a continuous point cloud covering the entire surface deformation zone. The continuous point cloud covering the entire surface deformation zone was converted from WGS84 geodetic coordinates to the 2000 National Geodetic Coordinate System through parameter transformation and projected onto the Gauss-Kruger projection zone; using a high-precision quasi-geoid model, it was converted to the 1985 National Elevation Datum. Point cloud classification is performed by using point cloud semantic segmentation and fine-tuning point cloud classification combined with image data; Digital elevation models of surface deformation regions are constructed by utilizing the characteristics of classified point cloud objects.

[0011] Furthermore, for continuous point clouds covering the entire surface deformation zone, the consistency of point clouds in overlapping flight strip areas is checked to detect and correct possible systematic errors in point cloud registration; using the deployed base station control point data and GNSS continuously operating stations, the elevation mean square error of the point cloud data is calculated to ensure that the elevation mean square error meets the requirements of the corresponding terrain category.

[0012] Furthermore, when constructing a digital elevation model of a surface deformation area using the characteristics of the classified point cloud objects, an irregular triangular network is constructed using all ground feature points and feature lines; among them, all ground points in the point cloud are used as ground feature points with the same weight; and river edges and isometric water area boundaries with elevation information are selected as feature lines according to the actual situation.

[0013] Furthermore, the differential processing of the two phases of digital elevation models for the target area includes: calculating the difference between the corresponding elevation values ​​of the old and new phases of digital elevation models, and extracting the digital elevation model difference map; Set an elevation difference threshold to filter out interference information based on actual conditions; use an area threshold to accurately extract effective change areas larger than the area threshold; use a focus statistics tool to obtain the mode of the grid area with a set radius in the digital elevation model difference map, and smooth the difference results within the grid area.

[0014] Secondly, the present invention provides a digital elevation model update device based on InSAR surface deformation, comprising: at least one processing unit, the processing unit being connected to a storage unit via a bus unit, the storage unit storing a computer program that can run on a processor, and the processing unit implementing the digital elevation model update method based on InSAR surface deformation by running the computer program stored in the storage unit.

[0015] Thirdly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed, implements the digital elevation model update method based on InSAR surface deformation.

[0016] The technical solutions provided in the embodiments of the present invention have the following advantages compared with the prior art: This invention proposes a digital elevation model update method based on InSAR surface deformation. It acquires surface deformation monitoring results of the target area based on time-series InSAR technology. During the process, it unifies the benchmark by combining inverse distance weighted interpolation and system bias fitting to eliminate system bias. On the unified benchmark, strip data or adjacent orbit data are stitched together to obtain surface deformation monitoring results covering the target area. After verifying the accuracy of the surface deformation monitoring results, the surface deformation area is extracted to meet the needs of surface deformation area detection in large-scale target areas.

[0017] Based on different geographical conditions, surface deformation zones are extracted from surface deformation monitoring results of the target area by setting thresholds for surface deformation rate or surface deformation amount. For surface deformation zones, point cloud data of the surface deformation zones are acquired using airborne lidar. After data preprocessing, coordinate transformation, point cloud classification, and rasterized digital elevation model construction, the digital elevation model within the surface deformation zone is updated. Stable zone digital elevation model data are collected based on the principle of optimal current availability and mosaicked to form an updated digital elevation model, thereby achieving differentiated digital elevation model updates.

[0018] Differential processing and leveling data analysis were performed on the two phases of digital elevation models of the target area to verify the reliability of the surface deformation area and ensure the reliability of the final digital elevation model. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of a digital elevation model update method based on InSAR surface deformation provided in an embodiment of the present invention; Figure 2The flowchart illustrates a method for eliminating system bias based on a combination of inverse distance weighted interpolation and system bias fitting, provided in this embodiment of the invention, to unify the benchmark and stitch strip data or adjacent track data on the unified benchmark. Figure 3 A schematic diagram illustrating the measurement range and area range provided in an embodiment of the present invention; Figure 4 Line graphs of monitoring values ​​from InSAR and CORS stations provided in this embodiment of the invention; Figure 5 This is a deformation distribution diagram of the target region provided in an embodiment of the present invention; Figure 6 The difference map of the old and new digital elevation models provided in this embodiment of the invention; Figure 7 This is a processed difference map of the old and new digital elevation models provided in an embodiment of the present invention; Figure 8 The port area image is a processed differential map of the old and new digital elevation models provided in this embodiment of the invention; Figure 9 Old remote sensing images of the port area provided in this embodiment of the invention; Figure 10 New remote sensing images of port areas provided in embodiments of the present invention; Figure 11 The smoothed difference map of the old and new digital elevation models provided in this embodiment of the invention is a river region image. Figure 12 This is an old remote sensing image of a river area provided in an embodiment of the present invention; Figure 13 New remote sensing images of river areas provided in embodiments of the present invention; Figure 14 The settlement region image is a smoothed differential map of the old and new digital elevation models provided in this embodiment of the invention. Figure 15 The image provided in this embodiment of the invention is an old remote sensing image of the subsidence area. Figure 16 This is a new remote sensing image of a subsidence area provided in an embodiment of the present invention; Figure 17 This is a leveling data deformation distribution map provided in an embodiment of the present invention; Figure 18 This is a schematic diagram of a digital elevation model update device based on InSAR surface deformation provided in an embodiment of the present invention. Detailed Implementation

[0022] 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.

[0023] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0024] Example 1 like Figure 1 As shown, the present invention provides a digital elevation model update method based on InSAR surface deformation, comprising: S100, based on time-series InSAR technology, acquires surface deformation monitoring results of the target area. The target area of ​​this application is larger than the swath width of the synthetic aperture radar interferometry results.

[0025] Temporal InSAR technology utilizes multiple SAR images in the time domain to select interferometric pairs with temporal and spatial baselines that meet certain conditions. It then establishes an interferometric phase deformation model to obtain surface deformation information. The main processing methods of temporal InSAR include Permanent Scatterer Interferometry (PS-InSAR), Small Baseline Set Interferometry (SBAS-InSAR), and Distributed Scatterer Interferometry (DS-InSAR). This application processes long-term, slow surface deformation changes in large target areas, employing PS-InSAR technology for monitoring. PS-InSAR technology performs differential interferometry on multiple single-view complex images, selecting stable pixels with strong backscattering characteristics on the interferogram as permanent scatterers. Permanent scatterers are mostly building structures and exposed rocks. Because permanent scatterers maintain good stability over long time intervals and are less affected by external noise, they exhibit high coherence. This avoids unstable ground points from participating in data processing and reduces the impact of temporal and spatial decoherence and atmospheric delay on deformation monitoring results.

[0026] In this example, Sentinel-1 data, 90-m resolution SRTM data, and precise orbital data were used to monitor the surface deformation of the target area over a four-year period using PS-InSAR technology. A total of 538 Sentinel-1 datasets were used across the target area. The temporal resolution of the datasets was primarily 12 or 24 days; the maximum spatial baseline value in each dataset was -138.14 meters, and the maximum temporal baseline value was -756 days. The target area covered a large area with complex terrain, diverse surface cover and weather conditions, and inconsistent error factors, which posed certain challenges to surface deformation monitoring.

[0027] In the specific implementation process, such as Figure 3 As shown, the swath width of single-frame multi-view, single-look complex images in PS-InSAR technology is limited, while the target area exceeds the swath width. Furthermore, the results of time-series InSAR multi-track, multi-temporal surface deformation monitoring are scattered and cannot meet the needs of comprehensive analysis of surface deformation over large target areas. To address these issues, this application uses multi-frame data stitching from strips or adjacent tracks; however, data processing for each strip or track is independent, and its deformation results are relative to a selected reference point within its respective track. This results in a systematic bias between deformation maps from different tracks, making it impossible to directly stitch them into a complete and continuous deformation map of the target area. This application uses a method combining inverse distance weighted interpolation and systematic bias fitting to eliminate systematic bias and unify the benchmark. On this unified benchmark, strip data or adjacent track data are stitched together to obtain surface deformation monitoring results covering the target area.

[0028] Specifically, such as Figure 2 As shown, the method of eliminating system bias based on a combination of inverse distance weighted interpolation and system bias fitting to unify the benchmark, and to stitch strip data or adjacent track data on the unified benchmark, includes: Find the common region between the two datasets to be stitched together, which have been transformed to a unified coordinate system; In the public area, the deformation values ​​of the first set of data to be stitched are interpolated to the positions of the permanent scatterers of the second set of data to be stitched using the inverse distance weighted interpolation method, resulting in a perfectly matched pair of permanent scatterer points in space. Calculate the difference between the deformation values ​​of the permanent scatterer point pairs to obtain the difference field, which represents the systematic deviation between the two sets of data to be stitched together; The system deviation surface is obtained by surface fitting of the discrete difference field using the robust least squares method. Based on the system deviation surface, the reference datum of the second set of data to be spliced ​​is unified to the datum of the first set of data to be spliced, and data splicing is achieved on the unified datum.

[0029] S200: When the accuracy of the surface deformation monitoring results is verified by CORS stations and meets the requirements, the surface deformation zone is extracted from the surface deformation monitoring results of the target area by setting a threshold for the surface deformation rate or the surface deformation amount, based on different geographical conditions.

[0030] In the specific implementation process, during accuracy verification, the nearest neighbor permanent scattering body (PSB) within 500 meters of the CORS station was used to verify that the CORS stations were uniformly distributed throughout the target area. The accuracy was verified by matching and comparing the preset CORS stations with the nearest PSB. InSAR measures one-dimensional deformation along the line of sight, and PSBs are discrete. The three-dimensional deformation of the CORS station was projected onto the satellite line of sight, and the deformation of the nearest reliable PSB was compared using the nearest neighbor search. By calculating the correlation coefficient and root mean square error of the InSAR measurement and CORS station time series, the accuracy and reliability of the stitched surface deformation monitoring results were quantitatively evaluated.

[0031] In the example, a total of 81 sets of validation data for CORS and InSAR monitoring values ​​were retrieved. The calculated root mean square error of the validation data was 6.42 mm, and the correlation coefficient was 0.971. The comparison between CORS stations and time-series InSAR monitoring values, and the difference between the two, are shown below. Figure 4 As shown, the line graph of CORS station monitoring values ​​roughly matches the line graph of time-series InSAR monitoring values, with the difference between the two fluctuating around 0-±15 mm.

[0032] Based on geographical conditions (such as urban areas or mountainous areas), set a deformation rate threshold (such as 10 mm per year) or a cumulative surface deformation threshold, and extract the surface deformation zone from the surface deformation monitoring results of the target area.

[0033] In the specific implementation process, the cumulative deformation is calculated using the optimal reference point. First, permanent scatterers with reflection intensities exceeding a threshold are selected. Delaunay is used to combine permanent scatterers and solve the model to select the optimal reference point. Based on this optimal reference point, the correlation of the permanent scatterers after removing the atmospheric delay phase is controlled between 0.8 and 1.0. The deformation, elevation, and atmospheric components in the interference phase are decomposed to obtain the cumulative deformation. For example... Figure 5 As shown, this application sets the threshold between the deformation zone and the stable zone to 50mm. It can be seen that the deformation zone is mainly located in northern, northwestern, and southwestern Shandong. That is, only the digital elevation model within the deformation zone needs to be updated.

[0034] The S300 acquires point cloud data of the surface deformation area using airborne lidar, and completes the update of the digital elevation model within the surface deformation area through data preprocessing, coordinate transformation, point cloud classification, and rasterized digital elevation model construction.

[0035] Based on the extent of the surface deformation zone, the point cloud density requirement (≥0.25 points / m²), and the terrain (which affects accuracy requirements, see Table 1), design the flight path (air altitude, air speed, and lateral overlap), and perform equipment calibration in accordance with CH / T8024 requirements.

[0036] Table 1. Elevation accuracy requirements for point cloud data, in meters;

[0037] While acquiring point cloud data of the surface deformation zone using airborne lidar along the flight path, synchronous image data is also acquired.

[0038] Data preprocessing: The raw point cloud data is decoded, and laser ranging data, GNSS positioning data, flight record data, base station control point data, and GNSS continuously operating station data are organized. Differential GNSS / IMU integrated navigation technology is used to fuse the laser ranging data with high-precision position and attitude information, calculating the three-dimensional coordinates of each laser point in the point cloud in the WGS84 coordinate system. Point cloud data from adjacent flight strips are stitched together to form a continuous point cloud covering the entire surface deformation zone. The consistency of the point cloud in overlapping flight strip areas is checked to detect and correct potential systematic errors in point cloud registration. Using deployed base station control point data and GNSS continuously operating station data, the elevation mean square error of the point cloud data is calculated to ensure it meets the requirements of the corresponding terrain category in Table 1.

[0039] Coordinate transformation: The continuous point cloud covering the entire surface deformation zone is converted from WGS84 geodetic coordinates to the 2000 National Geodetic Coordinate System through parameter transformation, and then projected onto the Gauss-Kruger projection zone. Using a high-precision quasi-geoid model, the ellipsoidal height of the point cloud is converted to the normal height under the 1985 National Height Datum, thus achieving the elevation datum transformation.

[0040] Point cloud classification is performed using point cloud semantic segmentation and fine-tuning combined with image data. The process includes: noise removal using elevation comparison: For each point, the elevation difference between it and points within a certain neighborhood is calculated. If the difference exceeds a preset threshold based on terrain undulation, it is identified as a flypoint or noise due to low vegetation and is removed. Initial ground surface point extraction: The initial ground surface is iteratively extracted from lower laser points, with a ground slope threshold set to ensure a reasonable ground surface. Based on the reflection intensity, echo frequency, and feature shape information of the point cloud data, the lidar point cloud data is automatically classified using a point cloud segmentation model. The automatically classified point cloud data is then manually fine-classified with reference to image data, and misclassified points are reclassified. The resulting classified surface point cloud data, after automatic and manual classification, is stored hierarchically according to ground point cloud, non-ground point cloud, noisy point cloud, and overlapping zone point cloud.

[0041] The construction of a digital elevation model (DEM) for surface deformation areas using the characteristics of classified point cloud objects includes: feature selection; using all ground points in the point cloud as feature points with equal weights. Based on actual conditions, river boundaries and isometric water area boundaries (lakes, reservoirs, ponds, etc.) with elevation information are selected as feature lines for network construction. An irregular triangular network is constructed using all ground feature points and feature lines. When outputting the DEM, the maximum network distance is set according to the actual data to ensure that the interpolation results reflect the complete terrain and avoid interpolation errors. The DEM is divided into map sheets according to specifications. When interpolating at the edges of each map sheet, data within a certain distance range from adjacent sheets are included in the calculation to ensure a natural terrain transition at map sheet junctions. The expansion width is 10% of the map sheet width. After junctions, the data is continuous, the DEM grid does not misalign, and the elevation of points in the same grid within the overlapping area of ​​adjacent map sheets is consistent.

[0042] S400 collects digital elevation model data for stable areas based on the principle of optimal current availability, and then mosaics the data to form an updated digital elevation model.

[0043] S500 performs differential processing and leveling data analysis on the two-phase digital elevation models of the target area to verify the reliability of the surface deformation area.

[0044] To verify the accuracy of defining the updated range of the digital elevation model, the difference between the corresponding elevation values ​​of the old and new digital elevation models was calculated, and a digital elevation model difference map was extracted. The digital elevation model difference map can intuitively represent the degree of elevation change in the same area within a certain period. Areas with positive values ​​in the digital elevation model difference map represent subsidence (collapse, landslide) areas, while negative values ​​represent uplift areas, and vice versa.

[0045] However, the generated digital elevation model (DEM) difference map contains some interfering factors. For example, theoretically, the deformation value should be zero in areas where the terrain has not changed. However, considering the differences in acquisition time, acquisition methods, vegetation cover, and data processing accuracy between the old and new DEM models, the actual deformation value is not zero. Therefore, a height difference threshold is set to filter out interfering information based on the actual situation. Alternatively, changes in small areas of the terrain may be due to human activities, which are not surface deformation phenomena, as the surface deformation studied in this application is a large-scale phenomenon. Effective changes in small areas can be accurately extracted by setting an area threshold. Because the original data resolution is too high, the difference results are too fragmented, causing some interference to the surface deformation analysis. Therefore, this application uses a focus statistics tool to extract the mode of a 20-grid area in the DEM difference map to smooth the difference results.

[0046] The results are as follows Figure 6 and Figure 7As shown in the figure, the red area represents the uplifted area, and the blue area represents the subsidence area.

[0047] In the example, the resolutions of the new and old digital elevation models are 1m and 2m respectively. The data benchmarks are unified, the coordinate system is the 2000 National Geodetic Coordinate System (CGCS2000), the elevation benchmark is the 1985 National Elevation Datum, the projection method is Gauss-Kruger projection, divided into 3° zones, and the coordinate unit is meters.

[0048] The differential map from the digital elevation model clearly shows that the surface deformation in the river mouth area is significant. This deformation can be broadly categorized into three types: first, substantial surface uplift in the northernmost port area; second, large-scale surface subsidence in the central area; and third, minor surface uplift in the river region. These three scenarios were analyzed in conjunction with relevant remote sensing imagery data.

[0049] like Figures 8-10 As shown, the northernmost port area shows a large-scale, relatively regular uplift as seen in the digital elevation model difference map. The red area represents the uplifted portion. The remote sensing imagery shows that the significant surface uplift is due to the construction of the port. What was originally water or mudflats has been transformed into port buildings, resulting in changes in the landform and a significant increase in surface height of more than 2 meters.

[0050] like Figures 11-13 As shown, there is surface uplift in the river area. The surface uplift height is less than 2 meters, and the closer to the river, the greater the surface uplift. The comparison of remote sensing images shows that surface uplift is related to river sediment deposition.

[0051] like Figures 14-15 As shown, there is ground subsidence in the central area, with the subsidence height concentrated between 1m and 2m. This is mainly due to changes in land use and extraction of underground resources.

[0052] The results of this application are compared and analyzed with the level achievements. Figure 7 and Figure 17 The deformation of the leveling results is similar.

[0053] Example 2 like Figure 18As shown, this embodiment of the invention provides a digital elevation model update device based on InSAR surface deformation, comprising: at least one processing unit, the processing unit being connected to a storage unit via a bus unit, the storage unit serving as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as the software program, computer-executable program, and modules corresponding to the digital elevation model update method based on InSAR surface deformation in this embodiment of the invention. The processing unit implements the aforementioned digital elevation model update method based on InSAR surface deformation by running the software program, computer-executable program, and modules stored in the storage unit.

[0054] Of course, the computer program stored in the memory of the digital elevation model update device based on InSAR surface deformation provided in the embodiments of the present invention is not limited to the method operation described above, but can also execute related operations in the digital elevation model update method based on InSAR surface deformation provided in any embodiment of the present invention.

[0055] Example 3 This invention provides a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed, it implements the digital elevation model update method based on InSAR surface deformation.

[0056] In the embodiments provided by this invention, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, structures, or units, and may be electrical, mechanical, or other forms.

[0057] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0058] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0059] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for updating a digital elevation model based on InSAR surface deformation, characterized in that, include: The surface deformation monitoring results of the target area are obtained based on the time-series InSAR technology, where the range of the target area is larger than the range of the synthetic aperture radar interferometric image. When the accuracy of the surface deformation monitoring results is verified by CORS stations and meets the requirements, the surface deformation zone can be extracted from the surface deformation monitoring results of the target area by setting a threshold for the surface deformation rate or the surface deformation amount, depending on different geographical conditions. Point cloud data of the surface deformation area is acquired by airborne lidar, and the digital elevation model in the surface deformation area is updated after data preprocessing, coordinate transformation, point cloud classification, and rasterized digital elevation model construction. Data of digital elevation models in stable areas are collected based on the principle of optimal current availability, and then mosaicked to form an updated digital elevation model. The reliability of the surface deformation area is verified by differential processing and leveling data analysis of the two phases of digital elevation models of the target area, so as to realize the rapid updating of the digital elevation model.

2. The method for updating a digital elevation model based on InSAR surface deformation according to claim 1, characterized in that, Permanent scatterer interferometry (PS-InSAR) technology was selected for monitoring. Since the target area is larger than the range of synthetic aperture radar interferometry imagery, when collecting surface deformation monitoring results for the target data, a unified benchmark was established by combining inverse range weighted interpolation and system bias fitting to eliminate system bias. On the unified benchmark, strip data or adjacent orbit data were stitched together to obtain surface deformation monitoring results covering the target area.

3. The method for updating a digital elevation model based on InSAR surface deformation according to claim 2, characterized in that, The method of eliminating system bias based on a combination of inverse distance weighted interpolation and system bias fitting to unify the benchmark, and to stitch strip data or adjacent track data on the unified benchmark, includes: Find the common area between the two sets of data to be spliced; In the public area, the deformation values ​​of the first set of data are interpolated to the positions of the permanent scatterers of the second set of data using the inverse distance weighted interpolation method, resulting in a perfectly matched pair of permanent scatterer points in space. Calculate the difference between the deformation values ​​of the permanent scatterer point pairs to obtain the difference field, which represents the systematic deviation between the two sets of data to be stitched together; The system deviation surface is obtained by surface fitting of the discrete difference field using the robust least squares method. Based on the system deviation surface, the reference datum of the first set of data is unified to the datum of the second set of data, and data splicing is achieved on the unified datum.

4. The method for updating a digital elevation model based on InSAR surface deformation according to claim 1, characterized in that, When screening for surface deformation regions, the cumulative deformation calculation using the optimal reference point selected from permanent scatterers includes: firstly, screening permanent scatterers with reflection intensities higher than a threshold; using Delaunay to combine permanent scatterers and solve the model to select the optimal reference point; using the optimal reference point as a benchmark, controlling the correlation of permanent scatterers after removing the atmospheric delay phase to 0.8-1.0; decomposing the deformation, elevation, and atmospheric components in the interference phase; and then obtaining the cumulative deformation.

5. The method for updating a digital elevation model based on InSAR surface deformation according to claim 1, characterized in that, The process of acquiring point cloud data of the surface deformation area using airborne lidar, followed by data preprocessing, coordinate transformation, point cloud classification, and rasterized digital elevation model construction to update the digital elevation model within the surface deformation area includes: Flight routes are designed based on the extent of surface deformation zones, point cloud density requirements, and terrain. Airborne lidar was used to acquire point cloud data of the surface deformation zone along the flight path, and synchronous image data was also acquired. During data preprocessing, differential GNSS / IMU integrated navigation solution technology is used to fuse laser ranging data with high-precision position and attitude information to calculate the three-dimensional coordinates of each laser point in the point cloud in the WGS84 coordinate system; the point cloud data of adjacent flight strips are stitched together to form a continuous point cloud covering the entire surface deformation zone. The continuous point cloud covering the entire surface deformation zone was converted from WGS84 geodetic coordinates to the 2000 National Geodetic Coordinate System through parameter transformation and projected onto the Gauss-Kruger projection zone; using a high-precision quasi-geoid model, it was converted to the 1985 National Elevation Datum. Point cloud classification is performed by using point cloud semantic segmentation and fine-tuning point cloud classification combined with image data; Digital elevation models of surface deformation regions are constructed by utilizing the characteristics of classified point cloud objects.

6. The method for updating a digital elevation model based on InSAR surface deformation according to claim 5, characterized in that, For continuous point clouds covering the entire surface deformation zone, the consistency of point clouds in overlapping flight strips is checked to detect and correct possible systematic errors in point cloud registration; using the deployed base station control point data and GNSS continuously operating stations, the elevation mean square error of the point cloud data is calculated to ensure that the elevation mean square error meets the requirements of the corresponding terrain category.

7. The method for updating a digital elevation model based on InSAR surface deformation according to claim 5, characterized in that, When constructing a digital elevation model of a surface deformation region using the characteristics of classified point cloud objects, an irregular triangular network is constructed using all ground feature points and feature lines. Specifically, all ground points in the point cloud are used as ground feature points with equal weights. Based on the actual situation, river edges and isometric water area boundaries with elevation information are selected as feature lines.

8. The method for updating a digital elevation model based on InSAR surface deformation according to claim 1, characterized in that, The differential processing of the two-phase digital elevation models of the target area includes: calculating the difference between the corresponding elevation values ​​of the old and new digital elevation models and extracting the digital elevation model difference map; Set an elevation difference threshold to filter out interference information based on actual conditions; use an area threshold to accurately extract effective change areas larger than the area threshold; use a focus statistics tool to obtain the mode of the grid area with a set radius in the digital elevation model difference map, and smooth the difference results within the grid area.

9. A digital elevation model update device based on InSAR surface deformation, comprising: At least one processing unit, the processing unit being connected to a storage unit via a bus unit, the storage unit storing a computer program that can run on a processor, characterized in that the processing unit implements the digital elevation model update method based on InSAR surface deformation as described in any one of claims 1-8 by running the computer program stored in the storage unit.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the digital elevation model update method based on InSAR surface deformation as described in any one of claims 1-8.

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