Tower slope deformation monitoring method and device, storage medium and computer equipment

Abstract

According to the tower slope deformation monitoring method and device, the storage medium and the computer equipment provided by the invention, when slope deformation monitoring is carried out on the transmission tower, the radar image data and the digital elevation data of the transmission tower slope are firstly collected, full space-time monitoring of the data is realized, a monitoring blind area is avoided, and then the slope deformation of the transmission tower is monitored based on the digital elevation data. The D-InSAR technology is adopted to carry out deformation measurement on radar image data to generate short-term deformation information, so that the measurement precision can reach a millimeter or submillimeter level; then, based on the short-term deformation information, the SBAS-InSAR technology is adopted to carry out deformation measurement on the radar image data, and long-term deformation information is obtained, so that the continuity and accuracy of deformation monitoring are improved; besides, deformation measurement can be carried out on radar image data by adopting a PS-InSAR technology based on long-term deformation information and digital elevation data to obtain high-precision and high-stability time sequence deformation information, so that a comprehensive and accurate monitoring effect is realized.

Description

Technical Field

[0001] The present application relates to the field of measurement and monitoring technology, and in particular to a method, device, storage medium and computer equipment for monitoring tower slope deformation. Background Art

[0002] In today's rapidly developing society, the widespread deployment and continued expansion of power infrastructure has become a critical element supporting modern life and economic activities. Transmission towers, as a core component of the power transmission network, are crucial for the safe and reliable operation of the power system. However, transmission towers are often built in areas with complex and variable terrain. These locations are susceptible to natural factors such as wind and rain erosion and geological changes, making slope stability a critical safety hazard.

[0003] Currently, monitoring the slope stability of transmission towers primarily relies on manual on-site surveys and simple measuring equipment. For example, in slope deformation monitoring, remote sensing technology is used to obtain satellite images of different points on the slope and compare them to determine the slope deformation. This method is often limited by terrain complexity and monitoring efficiency, making it difficult to achieve comprehensive and accurate monitoring results when meeting large-scale, high-precision monitoring requirements. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect that the methods in the existing technology are limited by the complexity of the terrain and the monitoring efficiency, and it is difficult to achieve comprehensive and accurate monitoring effects when facing large-scale, high-precision monitoring needs.

[0005] The present application provides a method for monitoring tower slope deformation, the method comprising:

[0006] Collecting radar image data and digital elevation data of the transmission tower slope, and performing differential interference processing on adjacent radar images in the radar image data to obtain adjacent time-phase interference graphs;

[0007] Selecting ground control points of the adjacent time-phase interferograms, and performing quality screening and differential interferometry on the adjacent time-phase interferograms using the digital elevation data and the ground control points to generate short-term deformation information;

[0008] A small baseline interferogram network is constructed using the short-term deformation information and the radar image data, and a singular value decomposition method is used to solve the cumulative deformation rate of the small baseline interferogram network to obtain long-term deformation information;

[0009] Selecting any radar image from the radar image data, registering it with other radar images, and then performing interference processing to obtain a multi-temporal interferogram, and selecting permanent scatterer candidate points of the multi-temporal interferogram based on the long-term deformation information;

[0010] The multi-temporal interferogram is subjected to deformation phase separation processing based on the permanent scatterer candidate points and the digital elevation data, and the processed multi-temporal interferogram is parameterized using an iterative method to obtain temporal deformation information.

[0011] Optionally, selecting the ground control points of the adjacent temporal interferograms includes:

[0012] Determining the phase derivative variance of each pixel in the adjacent time-phase interferogram, and binarizing each phase derivative variance to mark the type of each pixel according to the binarization result; the type includes stable pixels and unstable pixels;

[0013] Using a preset large sliding window to divide the adjacent time-phase interferogram into large window areas, and counting the proportion of stable pixels in each large window area, so as to mark the large window area with the largest proportion as a stable area;

[0014] Using a preset small sliding window to divide the stable area into small window areas, and calculating the average coherence coefficient of each small window area, so as to mark the small window area with the smallest average coherence coefficient as a high coherence candidate area;

[0015] Calculating the coherence coefficient of each pixel in the high coherence candidate area, and marking the pixel with the highest coherence coefficient as the initial control point;

[0016] Determining a mean value of coherence coefficients in a neighborhood of the initial control point, and determining whether the mean value of coherence coefficients exceeds a preset threshold;

[0017] If yes, taking the initial control point as the ground control point of the adjacent temporal interferogram;

[0018] If not, the stable region is removed from the adjacent time-phase interference graph, and the process returns to the step of marking the large window region with the largest proportion as the stable region and subsequent steps.

[0019] Optionally, the using the digital elevation data and the ground control points to perform quality screening and differential interferometry on the adjacent temporal interferograms to generate short-term deformation information includes:

[0020] generating a topographic fringe map using the digital elevation data, and performing a differential operation between the topographic fringe map and the adjacent time-phase interference map to generate an intermediate interference map;

[0021] performing phase unwrapping and error correction on the intermediate interferogram using the ground control points to obtain a final interferogram;

[0022] Perform differential interferometry on the final interference pattern to generate short-term deformation information of the transmission tower slope.

[0023] Optionally, constructing a small baseline interferogram network using the short-term deformation information and the radar image data includes:

[0024] Based on the short-term deformation information, image pairs that meet constraint conditions are selected from the radar image data, and the image pairs are subjected to interferometric processing to generate an initial interferogram network; wherein the constraint conditions include time constraints and spatial constraints;

[0025] The initial interferogram network is optimized using a minimum generation number algorithm to obtain an intermediate interferogram network;

[0026] The phase closure loop residual of each interferogram in the intermediate interferogram network is calculated, and the interferograms whose phase closure loop residual exceeds a preset residual threshold are removed from the intermediate interferogram network to obtain a small baseline interferogram network.

[0027] Optionally, the selecting any radar image from the radar image data and registering it with other radar images and then performing interference processing to obtain a multi-temporal interferogram includes:

[0028] Select any radar image from the radar image data and mark it as a primary image, and mark other radar images in the radar image data except the primary image as secondary images;

[0029] Using a feature matching algorithm to align each auxiliary image to the reference coordinate system of the main image to obtain an image pair;

[0030] Phase difference calculation is performed on the image pair to generate a multi-temporal interferogram according to the calculation result.

[0031] Optionally, the selecting permanent scatterer candidate points of the multi-temporal interferogram based on the long-term deformation information includes:

[0032] Based on the long-term deformation information, a long-term stable region is screened from the multi-temporal interferogram, and an amplitude dispersion index and a coherence coefficient of each pixel in the long-term stable region are calculated to obtain a calculation result;

[0033] Based on the calculation result, high coherence pixels in the long-term stable area are determined, and permanent scatterer candidate points are selected from each high coherence pixel according to a preset coherence coefficient threshold.

[0034] Optionally, performing deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and the digital elevation data includes:

[0035] Acquiring track data of the transmission tower slope, and differentially removing terrain phase from the multi-temporal interferogram based on the track data and the digital elevation data;

[0036] Based on the permanent scatterer candidate points, a space-time filtering method is used to separate the atmospheric delay phase in the differential multi-temporal interferogram.

[0037] The present application also provides a tower slope deformation monitoring device, comprising:

[0038] A data collection module is used to collect radar image data and digital elevation data of the transmission tower slope, and perform differential interference processing on adjacent radar images in the radar image data to obtain adjacent time-phase interference diagrams;

[0039] a first measurement module, configured to select ground control points of the adjacent time-phase interferograms, and perform quality screening and differential interferometry on the adjacent time-phase interferograms using the digital elevation data and the ground control points to generate short-term deformation information;

[0040] A second measurement module is configured to construct a small baseline interferogram network using the short-term deformation information and the radar image data, and to solve the cumulative deformation rate of the small baseline interferogram network using a singular value decomposition method to obtain long-term deformation information;

[0041] a candidate point selection module, configured to select any radar image from the radar image data, register it with other radar images, and then perform interference processing to obtain a multi-temporal interferogram, and select permanent scatterer candidate points in the multi-temporal interferogram based on the long-term deformation information;

[0042] The third measurement module is used to perform deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and the digital elevation data, and use an iterative method to solve the parameters of the processed multi-temporal interferogram to obtain time series deformation information.

[0043] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the tower slope deformation monitoring method as described in any one of the above embodiments.

[0044] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0045] The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the tower slope deformation monitoring method as described in any one of the above embodiments are performed.

[0046] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0047] The tower slope deformation monitoring method, device, storage medium and computer equipment provided by the present application can first collect radar image data and digital elevation data of the transmission tower slope when monitoring the slope deformation of the transmission tower, realize full-time and space monitoring of the data, and avoid monitoring blind spots. After data collection, adjacent radar images in the radar image data can be differentially interfered to obtain adjacent time-phase interference diagrams, so that the measurement accuracy reaches the millimeter or sub-millimeter level; then, the ground control points of the adjacent time-phase interference diagrams can be selected, and the digital elevation data and ground control points can be used to perform quality screening and differential interference measurement on the adjacent time-phase interference diagrams to generate short-term deformation information. Here, the automatic selection of ground control points can reduce the reliance on manual experience selection and improve the measurement accuracy of D-InSAR technology; then, a small baseline interference diagram network can be constructed through short-term deformation information and radar image data , and the singular value decomposition method is used to solve the cumulative deformation rate of the small baseline interferogram network to obtain long-term deformation information. Here, the SBAS-InSAR technology is used to monitor the long-term series of surface deformation, which can improve the continuity and accuracy of deformation monitoring; in addition, any radar image can be selected from the radar image data and aligned with other radar images for interference processing to obtain a multi-temporal interferogram, and permanent scatterer candidate points of the multi-temporal interferogram are selected based on the long-term deformation information. The accuracy and stability of surface deformation monitoring can be further improved by PS-InSAR technology. Here, the deformation phase separation processing of the multi-temporal interferogram can be performed based on the permanent scatterer candidate points and digital elevation data, and the iterative method is used to solve the parameters of the processed multi-temporal interferogram to obtain millimeter or submillimeter level time series deformation information, achieving comprehensive and accurate monitoring effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 A schematic flow chart of a tower slope deformation monitoring method provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of a process for optimizing the selection of ground control points provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of a process for optimizing a small baseline interferogram network construction according to an embodiment of the present application;

[0052] Figure 4 A schematic structural diagram of a tower slope deformation monitoring device provided in an embodiment of the present application;

[0053] Figure 5 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0055] Currently, monitoring the slope stability of transmission towers primarily relies on manual on-site surveys and simple measuring equipment. For example, in slope deformation monitoring, remote sensing technology is used to obtain satellite images of different points on the slope and compare them to determine the slope deformation. This method is often limited by terrain complexity and monitoring efficiency, making it difficult to achieve comprehensive and accurate monitoring results when meeting large-scale, high-precision monitoring requirements.

[0056] Based on this, this application proposes the following technical solutions, please refer to the following for details:

[0057] This application proposes the following technical solutions, please refer to the following for details:

[0058] In one embodiment, Figure 1 As shown, Figure 1 This is a flow chart of a method for monitoring the deformation of a tower slope provided in an embodiment of the present application. This application also provides a method for monitoring the deformation of a tower slope, which specifically includes the following:

[0059] S110: collecting radar image data and digital elevation data of the transmission tower slope, and performing differential interference processing on adjacent radar images in the radar image data to obtain adjacent time-phase interference graphs.

[0060] In this step, when monitoring the slope deformation of the transmission tower, the computer equipment can first collect radar image data and digital elevation data of the transmission tower slope to achieve all-weather, all-time and all-space monitoring of the data and avoid monitoring blind spots; after the data collection is completed, the computer equipment can perform differential interference processing on adjacent radar images in the radar image data to obtain adjacent time-phase interference diagrams, so that the measurement accuracy reaches the millimeter or sub-millimeter level.

[0061] Radar imagery data refers to synthetic radar (SAR) imagery, which uses radar satellites or airborne radar to capture surface reflection signals from transmission tower slopes. It provides all-weather, all-time, and all-space monitoring capabilities, unaffected by weather and lighting conditions. Digital elevation data, on the other hand, refers to digital elevation models (DEMs), which represent surface elevation information and can be used to remove terrain phase information to improve deformation monitoring accuracy.

[0062] Specifically, computer equipment can use satellite radar remote sensing systems or airborne synthetic aperture radar (SAR) to collect multi-temporal radar images of transmission tower slopes, forming a time series dataset. This data can then be used to obtain surface scattering characteristics, enabling all-weather, all-time and all-spatial monitoring and avoiding data loss due to factors such as weather and light. Simultaneously, computer equipment can also synchronously acquire digital elevation model (DEM) data and record slope terrain elevation information through laser radar (LiDAR), stereo image pair measurement, or satellite altimetry technology, providing data support for subsequent terrain phase removal.

[0063] Furthermore, after data acquisition is complete, the computer can pre-process the radar image data, including image registration and noise filtering, to ensure that all radar images are in the same spatial reference frame and improve the data's signal-to-noise ratio. The computer can then use D-INSAR (Differential InSAR) technology to select radar images from adjacent time phases for interferometry processing and perform a differential calculation on the phase information of the two images to generate an initial interferogram. This image reflects the relative micro-deformation of the surface between the two observations, enabling measurement accuracy to reach millimeter or sub-millimeter levels.

[0064] S120: selecting ground control points of adjacent time-phase interferograms, and performing quality screening and differential interferometry on the adjacent time-phase interferograms using digital elevation data and ground control points to generate short-term deformation information.

[0065] In this step, after the adjacent time-phase interferograms are generated in step S110, the computer device can select the ground control points of the adjacent time-phase interferograms and use the digital elevation data and ground control points to perform quality screening and differential interferometry on the adjacent time-phase interferograms to generate short-term deformation information. This can reduce the reliance on manual experience selection through the automated selection of ground control points and improve the measurement accuracy of D-InSAR technology.

[0066] Ground Control Points (GCPs) are highly stable pixels selected during interferometry. They typically exhibit high long-term coherence, low noise, and deformation stability, making them useful for phase correction and error compensation. In this application, GCPs serve as reference points for deformation calculations, ensuring relatively stable measurement results and the reliability of short-term deformation information.

[0067] Specifically, to further improve measurement accuracy, after generating interferograms of adjacent temporal phases, the computer analyzes the interferogram's coherence characteristics and selects ground control points based on a preset coherence threshold, providing a reliable reference for subsequent phase unwrapping and error correction. After selecting the ground control points, the computer combines them with digital elevation data to perform quality screening on the interferograms. The digital elevation data removes the effects of terrain phase, while the ground control points perform error compensation and phase correction to reduce the impact of non-deformation signals such as orbital error and atmospheric delay on measurement accuracy, ensuring the reliability of deformation information. Therefore, the screened interferograms more accurately reflect surface deformation information, and the resulting short-term deformation monitoring results can accurately depict subtle deformation changes in the slopes around transmission towers over different time periods.

[0068] S130: A small baseline interferogram network is constructed using short-term deformation information and radar image data, and a singular value decomposition method is used to solve the cumulative deformation rate of the small baseline interferogram network to obtain long-term deformation information.

[0069] In this step, after the short-term deformation information is generated in step S120, the computer equipment can construct a small baseline interferogram network using the short-term deformation information and radar image data, and use the singular value decomposition method to solve the cumulative deformation rate of the small baseline interferogram network to obtain long-term deformation information. Here, the SBAS-InSAR (Small Baseline Subset InSAR) technology is used to monitor the long-term series of surface deformation, which can improve the continuity and accuracy of deformation monitoring.

[0070] Specifically, computers can use short-term deformation information and radar image data to construct a small-baseline interferogram network. This short-term deformation information, combined with the time series information of the radar imagery, forms a multi-temporal interferometric dataset. This small-baseline interferogram network can then be further analyzed. Computers can use singular value decomposition to process the interferometric data within the small-baseline interferogram network and determine the cumulative deformation rate, thereby obtaining long-term surface deformation information. This allows for large-scale, high-spatial-density surface deformation monitoring within a relatively short time interval.

[0071] It is understood that long-term deformation information is obtained by using efficient SBAS-InSAR technology to monitor long-term surface deformation series, thus providing a more detailed and continuous dynamic analysis of deformation over time. This application, through the application of SBAS-InSAR technology, can significantly improve the continuity of deformation monitoring, avoid errors caused by the limitations of a single time-phase interferogram, and enhance the accuracy and reliability of deformation measurement results.

[0072] S140: Select any radar image from the radar image data, register it with other radar images, and then perform interference processing to obtain a multi-temporal interferogram, and select permanent scatterer candidate points of the multi-temporal interferogram based on long-term deformation information.

[0073] In this step, after the long-term deformation information is generated in step S130, the computer device can select any radar image from the radar image data, align it with other radar images, and then perform interference processing to obtain a multi-temporal interferogram. Based on the long-term deformation information, permanent scatterer candidate points in the multi-temporal interferogram are selected, thereby obtaining images from multiple time points.

[0074] Permanent scatterer candidate points are points in radar images that exhibit stable scattering characteristics and maintain high coherence over long periods of time. These points exhibit consistent reflectance across multiple time series of radar images and can serve as benchmarks for deformation monitoring. They can be applied to PS-InSAR (Persistent Scatterer InSAR) technology to improve the accuracy and reliability of deformation monitoring.

[0075] Specifically, the computer equipment can select an image as a reference image from the collected radar image data, and accurately align it with other radar images acquired at different time points; after completing the alignment, the computer equipment can perform interference processing on the aligned image to generate a multi-temporal interferogram, thereby revealing the surface deformation between different time points.

[0076] Furthermore, to further enhance the reliability of deformation monitoring, the computer can also select candidate permanent scatterers with long-term stable scattering properties from the multi-temporal interferogram based on long-term deformation information. These candidate points are typically targets on the surface with stable reflective properties, such as buildings, exposed rocks, or other features with stable radar returns. They maintain high coherence in images taken at different time points. Therefore, by selecting these permanent scatterer candidates, the computer can effectively enhance the accuracy of deformation monitoring and ensure the continuity and consistency of deformation information.

[0077] S150: performing deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and the digital elevation data, and solving the parameters of the processed multi-temporal interferogram using an iterative method to obtain temporal deformation information.

[0078] In this step, after the multi-temporal interferogram and permanent scatterer candidate points are generated in step S140, the computer device can perform deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and digital elevation data, and use an iterative method to solve the parameters of the processed multi-temporal interferogram to obtain temporal deformation information.

[0079] It should be noted that the PS-InSAR technology is used to generate the time-series deformation information. Specifically, by combining the deformation data of multiple time points with the PS-InSAR technology, the surface deformation trend can be extracted with high precision, thereby further improving the accuracy and stability of surface deformation monitoring and achieving comprehensive and accurate monitoring results.

[0080] It can be understood that permanent scatterer candidate points can enable the data analysis process to focus on long-term stable, highly coherent targets, thereby reducing noise interference and improving the reliability of deformation phases. Digital elevation data can effectively remove terrain phase errors, making the deformation signal more prominent. Therefore, the computer equipment can perform deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and digital elevation data. After completing the deformation phase separation processing, the multi-temporal interferogram parameters are solved using an iterative method to optimize the accuracy of deformation estimation and form time-series deformation information.

[0081] The iterative method is a numerical calculation method that repeatedly updates the approximate values ​​of variables to gradually approach the exact solution to the problem. This application uses iterative calculations to establish optimal matching relationships between multiple time-phase images, eliminating systematic errors and obtaining more stable time-series deformation information.

[0082] In the above embodiment, when monitoring the slope deformation of the transmission tower, the radar image data and digital elevation data of the transmission tower slope can be collected first to realize the full-time and space monitoring of the data and avoid the monitoring blind area. After the data is collected, the adjacent radar images in the radar image data can be differentially interfered to obtain the adjacent time-phase interference diagram, so that the measurement accuracy reaches the millimeter or sub-millimeter level; then, the ground control points of the adjacent time-phase interference diagram can be selected, and the digital elevation data and ground control points can be used to perform quality screening and differential interference measurement on the adjacent time-phase interference diagram to generate short-term deformation information. Here, the automatic selection of ground control points can reduce the dependence on manual experience selection and improve the measurement accuracy of D-InSAR technology; then, a small baseline interference diagram network can be constructed by using the short-term deformation information and radar image data, and the singular value decomposition method can be used to analyze the small baseline interference diagram. The baseline interferogram network is used to solve the cumulative deformation rate and obtain long-term deformation information. Here, SBAS-InSAR technology is used to monitor the long-term series of surface deformation, which can improve the continuity and accuracy of deformation monitoring. In addition, any radar image can be selected from the radar image data and aligned with other radar images for interference processing to obtain a multi-temporal interferogram. Based on the long-term deformation information, permanent scatterer candidate points of the multi-temporal interferogram are selected, and the accuracy and stability of surface deformation monitoring can be further improved by PS-InSAR technology. Here, the deformation phase separation processing of the multi-temporal interferogram can be performed based on the permanent scatterer candidate points and digital elevation data, and the parameters of the processed multi-temporal interferogram can be solved by the iterative method to obtain millimeter or submillimeter level time series deformation information, achieving comprehensive and accurate monitoring effects.

[0083] In one embodiment, the process of selecting ground control points of adjacent temporal interferograms in step S120 may include:

[0084] S1211: Determine the phase derivative variance of each pixel in the adjacent phase interferogram, and binarize each phase derivative variance to mark the type of each pixel according to the binarization result; the type includes stable pixel and unstable pixel.

[0085] S1212: Using a preset large sliding window to divide the adjacent phase interferogram into large window areas, and counting the proportion of stable pixels in each large window area, so as to mark the large window area with the largest proportion as a stable area.

[0086] S1213: Using a preset small sliding window to divide the stable area into small window areas, and calculating the average coherence coefficient of each small window area, so as to mark the small window area with the smallest average coherence coefficient as a high coherence candidate area.

[0087] S1214: Calculate the coherence coefficient of each pixel in the high coherence candidate area, and mark the pixel with the highest coherence coefficient as the initial control point.

[0088] S1215: Determine the mean value of the coherence coefficients of the neighborhood of the initial control point, and determine whether the mean value of the coherence coefficients exceeds a preset threshold.

[0089] S1216: If yes, the initial control point is used as the ground control point of the adjacent temporal interferogram.

[0090] S1217: If not, the stable region is removed from the adjacent phase interference graph, and the process returns to execute the steps of marking the large window region with the largest proportion as the stable region and subsequent steps.

[0091] In this embodiment, when selecting a ground control point, the computer device may first determine the phase derivative variance of each pixel in the adjacent time-phase interferogram and binarize each phase derivative variance to label each pixel type based on the binarization result; the types here include stable pixels and unstable pixels. The computer device may then use a preset large sliding window to divide the adjacent time-phase interferogram into large window areas and calculate the proportion of stable pixels in each large window area, marking the large window area with the largest proportion as a stable area. Subsequently, the computer device may use a preset small sliding window to divide the stable area into small window areas and calculate the average coherence coefficient of each small window area, marking the small window area with the smallest average coherence coefficient as a high coherence candidate area. After determining the high coherence candidate area, the computer device may calculate the coherence coefficient of each pixel in the high coherence candidate area and mark the pixel with the highest coherence coefficient as the initial control point. The computer device then determines the average coherence coefficient of the neighborhood of the initial control point and, when the average coherence coefficient exceeds a preset threshold, selects the initial control point as the ground control point of the adjacent time-phase interferogram. If the mean value of the coherence coefficient does not exceed a preset threshold, the computer device can remove the stable area from the adjacent time-phase interferogram and reselect the ground control point from the adjacent time-phase interferogram after removal.

[0092] Among them, the large sliding window and the small sliding window can be set according to the monitoring accuracy requirements. Generally speaking, the size of the large sliding window is larger than the small sliding window, and they are in a multiple relationship.

[0093] It is understandable that in the processing of radar interferometry data, the selection of ground control points has a direct impact on the accuracy of InSAR deformation monitoring. In order to obtain reliable deformation measurements, it is usually necessary to select a stable or highly coherent pixel with a known deformation value, that is, a ground control point, and correct other unwrapped phases based on this reference pixel. Phase unwrapping is a process used to resolve the interferometric phase ambiguity, so restoring the true phase value is a necessary step to obtain surface deformation information. Therefore, the accuracy of phase unwrapping directly determines the accuracy of the final surface deformation, and is one of the main error sources of InSAR technology. For time-series InSAR technology, it is necessary to perform phase unwrapping on multiple differential interferograms and solve the deformation parameters. In order to achieve this process, all interferograms must have the same size and projection positioning.

[0094] However, due to the uncertainty of regional deformation of the surface, the phase change in each unwrapping interference pair should be based on a reference pixel. In order to ensure that all unwrapping interference patterns are in the same reference frame, it is usually necessary to set the phase of the reference pixel to zero. Based on this, the present application can analyze the coherence matrix or use image registration technology to find the optimal reference pixel, thereby improving the accuracy and stability of phase unwrapping. In addition, if regional analysis of surface deformation is required, it is necessary to correct it through an independently determined stable reference point. Therefore, in order to improve the accuracy and stability of phase unwrapping, the present application can combine a variety of methods, such as adaptive selection of reference pixels, image registration technology, etc., to obtain more reliable unwrapping results.

[0095] In summary, the most important characteristic of ground control points as the spatial reference of the interferogram is "stability" and they need to be continuously coherent in all interferograms. Reliable unwrapping reference points can ensure the accuracy of the phase unwrapping results. Generally speaking, the selection of reference pixels should meet the following criteria:

[0096] 1) The ground control points should avoid the locations of terrain residual interference fringes and deformation interference fringes, and stay away from the deformation areas in the interference pattern;

[0097] 2) Ground control points should not be selected at the edge of the interferogram and should avoid phase jump areas to avoid being selected on slope phases;

[0098] 3) The ground control points should be less affected by spatiotemporal decoherence in the interferogram sequence and maintain high coherence.

[0099] Therefore, the ground control points should be selected on the pixels with high coherence and stable phase changes on the interference pattern, and residual points should be avoided. Residual points often appear in areas of loss of coherence or phase discontinuity. Combining the SAR imaging principle and the basic principles of InSAR technology, the interference phase map reflects the changes in the surface objects during the acquisition of two SAR images. In order to select appropriate ground control points, this application can quantify its selection criteria based on the interference phase information.

[0100] Schematically, as Figure 2 As shown, Figure 2 A schematic diagram of a process for optimizing the selection of ground control points provided in an embodiment of the present application; Figure 2 In this paper, for a set of interferogram sequences (adjacent phase interferograms) obtained from N scenes of SAR images, the phase derivative variance of each pixel can be calculated for each interferogram, and the phase derivative variance can be binarized by setting a threshold. Then, a sliding window is set to obtain the phase-stable area in each window, and the proportion of the stable area in each window of all interferograms is counted and sorted. A small sliding window is set again in the window with the largest proportion of continuous area, and the average coherence in each small window is calculated according to the coherence coefficient map to obtain high-coherence pixels in the phase-stable area, ensuring that the reference pixel is not an isolated high-coherence point.

[0101] Specifically, according to the relationship between the residual point and the interference phase, the interference phase in the area where the residual point exists in the interference pattern is discontinuous. In order to obtain the phase stable area in the interference pattern, the computer equipment can describe the rate of change of the surface phase by calculating the phase derivative variance, because the phase derivative variance can reflect the change of the surface phase. First, the phase derivative variance of all pixels in the interference pattern sequence is calculated and the average is obtained, and then each pixel is binarized according to the threshold; the pixel value greater than the threshold is set to 0 (unstable), and the pixel value less than the threshold is set to 1 (stable). Next, a suitable sliding window is set in the pilot area, and the proportion of stable pixels in each window is counted to determine the window with the largest proportion of stable pixels as the stable area in the interference pattern. It is worth noting that since the edge of the image is prone to deformation, interference fringes usually exist in the edge area of ​​the interference pattern. Therefore, during the application process, the computer equipment needs to Figure 4 Pixels within a certain range are excluded to avoid the reference pixel falling on the edge of the image.

[0102] Furthermore, in the interferogram, areas with residual points typically exhibit loss of coherence. Therefore, after determining the stable region, it is necessary to select highly coherent points that are less susceptible to decoherence noise as reference pixels. Different objects exhibit varying temporal decoherence, so in the interferogram, objects that exhibit minimal variation and can form angular reflections typically exhibit higher coherence and appear brighter in the coherence coefficient plot. Therefore, computers can use the coherence coefficient plot to determine the quality of the interference fringes and the degree of noise disturbance.

[0103] To elaborate, a higher coherence coefficient indicates a greater similarity between the two SAR images and better quality interference fringes in the generated interferogram. Therefore, it is necessary to select the point with the highest coherence coefficient within the stable region as the reference pixel. To avoid the uncertainty introduced by the value of a single highly coherent pixel, a smaller sliding window is set within the stable region. The search is performed based on the mean coherence coefficient within the window, and the mean value of the pixels within the neighboring window is used as the reference pixel. Furthermore, multi-temporal coherence maps are independent of satellite calibration accuracy and can provide useful information. They are very sensitive to subtle changes in surface targets. To prevent certain phase-stable regions from being severely affected by decoherence, the coherence coefficient values ​​of the obtained reference pixels are determined based on the overall coherence of the interferogram. If the coherence does not meet the threshold, the search returns to the next stable region with a lower proportion of phase-stable pixels, and continues until the coherence of the reference pixel meets the criteria.

[0104] In one embodiment, the process of performing quality screening and differential interferometry on adjacent temporal interferograms using digital elevation data and ground control points to generate short-term deformation information in step S120 may include:

[0105] S1221: Generate a topographic fringe map using digital elevation data, and perform differential analysis between the topographic fringe map and adjacent time-phase interference maps to generate an intermediate interference map.

[0106] S1222: Phase unwrapping and error correction are performed on the intermediate interferogram using ground control points to obtain a final interferogram.

[0107] S1223: Perform differential interferometry on the final interferogram to generate short-term deformation information of the transmission tower slope.

[0108] In this embodiment, the computer device can use digital elevation data to generate a terrain fringe map, and differentiate the terrain fringe map from the adjacent phase interference map to generate an intermediate interference map, and then use ground control points to phase unwrap and error correct the intermediate interference map to obtain a final interference map. Finally, the computer device can perform differential interference measurement on the final interference map to generate short-term deformation information of the transmission tower slope.

[0109] Specifically, the computer device can use digital elevation data to generate a terrain fringe map that accurately reflects terrain undulation information. The computer device then performs a differential operation on the generated terrain fringe map with adjacent temporal interferograms to eliminate the terrain effect, thereby obtaining an intermediate interferogram. This intermediate interferogram can more clearly reflect surface deformation information, but may still be affected by phase aliasing and measurement errors. Therefore, to ensure data accuracy and deformation measurement precision, the computer device can use pre-selected ground control points to perform phase unwrapping on the intermediate interferogram, restoring discontinuous phase information caused by periodic changes in the interferometric phase to a continuous phase. Furthermore, to further reduce the influence of systematic errors and atmospheric effects, the computer device can also perform error correction on the intermediate interferogram based on the ground control points, ensuring that the final interferogram is more accurate and stable. After obtaining the final interferogram, the computer device can perform differential interferometry on it, extracting surface deformation information by calculating the interferometric phase difference at different temporal phases, and ultimately generating short-term deformation information of the transmission tower slope.

[0110] In one embodiment, the process of constructing a small baseline interferogram network using short-term deformation information and radar image data in step S130 may include:

[0111] S131: Based on the short-term deformation information, image pairs that meet the constraint conditions are selected from the radar image data, and the image pairs are interferometrically processed to generate an initial interferogram network; wherein the constraint conditions include time constraints and space constraints.

[0112] S132: Optimizing the initial interference graph network using a minimum generation number algorithm to obtain an intermediate interference graph network.

[0113] S133: Calculate the phase closure loop residual of each interferogram in the intermediate interferogram network, and remove the interferogram whose phase closure loop residual exceeds a preset residual threshold from the intermediate interferogram network to obtain a small baseline interferogram network.

[0114] In this embodiment, based on short-term deformation information, the computer device can select image pairs from the radar image data that meet constraints and perform interferometric processing on these image pairs to generate an initial interferogram network. The constraints may include temporal and spatial constraints. The computer device can then optimize the initial interferogram network using a minimum spanning number algorithm to obtain an intermediate interferogram network. The computer device then calculates the phase closure loop residual for each interferogram in the intermediate interferogram network and removes interferograms whose phase closure loop residual exceeds a preset residual threshold from the intermediate interferogram network, thereby obtaining a small-baseline interferogram network.

[0115] It is understandable that the generalized small baseline set method can be applied to inverting surface deformation through interferogram networks. Assuming N SAR images acquired at times (t1, ..., tN), and M unwrapped interferograms I = [I1, ..., IM] T obtained by differential interferometry of these SAR images, the incremental displacement vector d = [d1, ..., dN-1] (where di represents the incremental displacement between times ti and ti+1) can be solved using the following equation:

[0116] I=Gd

[0117] Where G is an M×N design matrix composed of matrix elements 0, 1, and -1, representing the relationship between the interferogram network and the displacement increments. This means that the unwrapped interferogram (the displacement between two SAR images during acquisition) can be considered the sum of the corresponding displacement increments. By summing these displacement increments, we can obtain the cumulative deformation for each SAR acquisition (i.e., the time series deformation). Combining this with the least squares method, we can obtain the average deformation rate from the accumulated deformation.

[0118] However, the echo signal received by the SAR sensor is easily affected by the surface coverage. Due to the influence of factors such as surface and seasonal changes during SAR image acquisition, the interference pattern sequence always contains a certain amount of decoherence noise. Therefore, there needs to be a sufficient number of interference patterns in the interference pattern network of the time-series InSAR technology, and the quality of the interference pattern needs to be as high as possible to reduce the influence of decoherence. In addition, there may be varying degrees of atmospheric delay phases in the interference pattern, as well as residual DEM errors, orbit errors, etc. These noises cannot be completely separated from the deformation phase and will eventually affect the results of phase unwrapping. Especially for the small baseline set method, phase unwrapping is an indispensable key step. Each unwrapped interference pattern is used as an observation to solve the deformation model. The quality of the phase unwrapping result will have a direct impact on the inverted surface deformation information. Therefore, this application needs to eliminate the interference patterns with poor quality to improve the efficiency and accuracy of InSAR deformation measurement.

[0119] Among them, the Sentinel-1 binary satellite system has promoted the application of InSAR technology to a new stage. Due to its short revisit period and open data access, it has a large amount of archived data in most areas. However, for long-term SAR imagery, there may still be gaps in the interferogram network in some areas. Moreover, due to the influence of incoherence factors such as vegetation, cultivated land, lakes or snow, after the low-quality interferograms are eliminated, there may be acquisition gaps of several months or even longer in the interferogram network. In this context, the present application can construct and optimize a small baseline interferogram network based on phase closure loop residuals and minimum spanning trees; compared with the traditional singular value decomposition (SVD) method, the present application can well interpret and process noisy data while preserving the original data information to the greatest extent.

[0120] Schematically, as Figure 3 As shown, Figure 3 A schematic diagram of a process for optimizing a small baseline interferogram network construction according to an embodiment of the present application; Figure 3 In this paper, an interferogram sequence can be generated according to an appropriate spatiotemporal baseline threshold, and the interference coherence can be maximized while ensuring the connectivity of the interferogram set. Then, the phase closure loop root mean square of each interferogram is calculated. The N SAR images and M unwrapped interferograms are regarded as points and edges in graph theory, respectively. The phase closure loop root mean square is used as the weight of the corresponding interferogram. Based on the MST algorithm, N-1 unwrapped interferograms with small root mean square are obtained to connect the N SAR images. Finally, the root mean square threshold is set to test the phase closure loop residuals of the remaining interferograms to eliminate the interference pairs with excessive phase unwrapping errors.

[0121] In one embodiment, the process of selecting any radar image from the radar image data, registering it with other radar images, and then performing interference processing to obtain a multi-temporal interferogram in step S140 may include:

[0122] S141: Select any radar image from the radar image data and mark it as a primary image, and mark other radar images in the radar image data except the primary image as secondary images.

[0123] S142: Using a feature matching algorithm, each auxiliary image is aligned to the reference coordinate system of the main image to obtain an image pair.

[0124] S143: Calculate the phase difference of the image pair to generate a multi-temporal interferogram according to the calculation result.

[0125] In this embodiment, the computer device can select any radar image from the radar image data and mark it as the main image, and mark other radar images in the radar image data except the main image as auxiliary images. Then, a feature matching algorithm can be used to align each auxiliary image to the reference coordinate system of the main image to obtain an image pair, and the phase difference of the image pair can be calculated to generate a multi-temporal interference map based on the calculation results.

[0126] Specifically, the computer device can select any radar image from the radar image data as the primary image and use it as the baseline reference image for subsequent interferometric processing. At the same time, the computer device can mark all other radar images in the radar image data except the primary image as auxiliary images for subsequent matching and registration with the primary image. In order to ensure that the auxiliary images acquired at different times can maintain the same spatial reference coordinate system as the primary image, the computer device can use a feature matching algorithm to accurately register each auxiliary image and align it to the reference coordinate system of the primary image. This registration process can compensate for image geometric distortion caused by factors such as orbital offset and perspective change, ensuring that multi-temporal images can be accurately compared at the pixel level. After feature matching and registration, the computer device can form image pairs, each consisting of a primary image and a corresponding auxiliary image.

[0127] Furthermore, after generating the image pairs, the computer calculates the phase difference between each pair, extracting the phase change information between the images using radar interferometry. By analyzing the phase differences between images of different temporal phases, surface deformation information can be effectively extracted, ultimately generating a multi-temporal interferogram. Multi-temporal interferograms provide continuous time series of surface deformation information, significantly improving the accuracy and timeliness of deformation monitoring, thereby providing high-precision, full-time and full-space data support for long-term stability monitoring of transmission tower slopes.

[0128] In one embodiment, the process of selecting permanent scatterer candidate points of the multi-temporal interferogram based on the long-term deformation information in step S140 may include:

[0129] S144: Screening a long-term stable region from the multi-temporal interferogram based on the long-term deformation information, and calculating the amplitude deviation index and coherence coefficient of each pixel in the long-term stable region to obtain a calculation result.

[0130] S145: Determine highly coherent pixels in the long-term stable area based on the calculation results, and select permanent scatterer candidate points from each highly coherent pixel according to a preset coherence coefficient threshold.

[0131] In this embodiment, when selecting candidate points for permanent scatterers, the computer device can screen out a long-term stable area from the multi-temporal interferogram based on the long-term deformation information, and calculate the amplitude deviation index and coherence coefficient of each pixel in the long-term stable area to obtain a calculation result. Then, based on the calculation result, the high-coherence pixels in the long-term stable area are determined, and the permanent scatterer candidate points are selected from each high-coherence pixel according to a preset coherence coefficient threshold.

[0132] Specifically, computers can analyze the changing trends of surface deformation in multi-temporal interferograms to identify areas that remain stable and exhibit minimal deformation over long observation periods. These areas are designated as long-term stable regions, ensuring that the pixels in these regions have high temporal stability and interferometric phase reliability. These long-term stable regions typically correspond to features such as buildings, rocks, and infrastructure, which exhibit strong reflective properties in radar images and are less affected by atmospheric and environmental factors.

[0133] Furthermore, the computer can calculate the amplitude dispersion index and coherence coefficient for each pixel in the long-term stable region to quantitatively assess the stability of the pixel throughout the time series image. The amplitude dispersion index can be used to reflect the changes in the pixel's radar echo intensity, while the coherence coefficient can be used to measure the consistency of the interferometric phase between different image phases. Therefore, after calculating the amplitude dispersion index and coherence coefficient, the computer can further screen highly coherent pixels in the long-term stable region based on these calculation results. These pixels are those that exhibit high coherence, minimal deformation, and low environmental interference in the long-term time series data. Subsequently, the computer can select pixels that meet the requirements from the highly coherent pixels as permanent scatterer candidates based on a preset coherence coefficient threshold.

[0134] In one embodiment, the process of performing deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and the digital elevation data in step S150 may include:

[0135] S151: Acquire the track data of the transmission tower slope, and differentially remove the terrain phase from the multi-temporal interferogram based on the track data and the digital elevation data.

[0136] S152: Based on the permanent scatterer candidate points, the atmospheric delay phase in the differential multi-temporal interferogram is separated using a spatiotemporal filtering method.

[0137] In this embodiment, deformation phase separation processing is performed on the multi-temporal interferogram. The computer equipment can first obtain the orbit data of the transmission tower slope, and differentially remove the terrain phase from the multi-temporal interferogram based on the orbit data and digital elevation data. Then, based on the permanent scatterer candidate points, the space-time filtering method is used to separate the atmospheric delay phase in the differential multi-temporal interferogram.

[0138] Orbital data refers to the precise orbital parameters of a radar satellite when acquiring radar images, including information such as the satellite's position, velocity, and attitude. In this application, it can be combined with digital elevation data to remove the terrain phase caused by surface elevation differences in the multi-temporal interferogram, so that the interferometric phase of the multi-temporal interferogram primarily reflects surface deformation information.

[0139] It is understandable that after the computer equipment removes the terrain phase from the multi-temporal interferogram by differentially analyzing orbital data and digital elevation data, it can then perform atmospheric delay phase separation on the processed multi-temporal interferogram based on candidate permanent scatterer points. Because radar waves are affected by atmospheric factors such as water vapor content and atmospheric pressure changes during propagation, SAR images acquired at different times may contain varying degrees of atmospheric delay errors, which can affect the accuracy of deformation measurements. To effectively suppress these errors, the computer equipment can use spatiotemporal filtering to jointly analyze the interferograms at multiple time points, identify and remove the atmospheric delay phase, and thus improve the reliability of the deformation information. Ultimately, the multi-temporal interferogram after deformation phase separation can accurately reflect the actual deformation of the transmission tower slope.

[0140] The following describes a pole tower slope deformation monitoring device provided in an embodiment of the present application. The pole tower slope deformation monitoring device described below and the pole tower slope deformation monitoring method described above can be referenced to each other.

[0141] In one embodiment, Figure 4 As shown, Figure 4 This is a schematic diagram of the structure of a tower slope deformation monitoring device provided in an embodiment of the present application. The present application also provides a tower slope deformation monitoring device, including a data collection module 210, a first measurement module 220, a second measurement module 230, a candidate point selection module 240, and a third measurement module 250, specifically including the following:

[0142] The data collection module 210 is used to collect radar image data and digital elevation data of the transmission tower slope, and perform differential interference processing on adjacent radar images in the radar image data to obtain adjacent time-phase interference diagrams.

[0143] The first measurement module 220 is used to select ground control points of adjacent time-phase interferograms, and perform quality screening and differential interferometry on the adjacent time-phase interferograms using digital elevation data and ground control points to generate short-term deformation information.

[0144] The second measurement module 230 is used to construct a small baseline interferogram network using short-term deformation information and radar image data, and use the singular value decomposition method to solve the cumulative deformation rate of the small baseline interferogram network to obtain long-term deformation information.

[0145] The candidate point selection module 240 is used to select any radar image from the radar image data, align it with other radar images, and then perform interference processing to obtain a multi-temporal interferogram, and select permanent scatterer candidate points in the multi-temporal interferogram based on long-term deformation information.

[0146] The third measurement module 250 is used to perform deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and the digital elevation data, and to solve the parameters of the processed multi-temporal interferogram using an iterative method to obtain time-series deformation information.

[0147] In the above embodiment, when monitoring the slope deformation of the transmission tower, the radar image data and digital elevation data of the transmission tower slope can be collected first to realize the full-time and space monitoring of the data and avoid the monitoring blind area. After the data is collected, the adjacent radar images in the radar image data can be differentially interfered to obtain the adjacent time-phase interference diagram, so that the measurement accuracy reaches the millimeter or sub-millimeter level; then, the ground control points of the adjacent time-phase interference diagram can be selected, and the digital elevation data and ground control points can be used to perform quality screening and differential interference measurement on the adjacent time-phase interference diagram to generate short-term deformation information. Here, the automatic selection of ground control points can reduce the dependence on manual experience selection and improve the measurement accuracy of D-InSAR technology; then, a small baseline interference diagram network can be constructed by using the short-term deformation information and radar image data, and the singular value decomposition method can be used to analyze the small baseline interference diagram. The baseline interferogram network is used to solve the cumulative deformation rate and obtain long-term deformation information. Here, SBAS-InSAR technology is used to monitor the long-term series of surface deformation, which can improve the continuity and accuracy of deformation monitoring. In addition, any radar image can be selected from the radar image data and aligned with other radar images for interference processing to obtain a multi-temporal interferogram. Based on the long-term deformation information, permanent scatterer candidate points of the multi-temporal interferogram are selected, and the accuracy and stability of surface deformation monitoring can be further improved by PS-InSAR technology. Here, the deformation phase separation processing of the multi-temporal interferogram can be performed based on the permanent scatterer candidate points and digital elevation data, and the parameters of the processed multi-temporal interferogram can be solved by the iterative method to obtain millimeter or submillimeter level time series deformation information, achieving comprehensive and accurate monitoring effects.

[0148] In one embodiment, the first measurement module 220 may include:

[0149] The type marking submodule is used to determine the phase derivative variance of each pixel in the adjacent phase interferogram and binarize each phase derivative variance to mark the type of each pixel according to the binarization result; the type includes stable pixel and unstable pixel.

[0150] The proportion statistics submodule is used to use a preset large sliding window to divide the large window area in the adjacent phase interference diagram, and count the proportion of stable pixels in each large window area to mark the large window area with the largest proportion as the stable area.

[0151] The coefficient calculation submodule is used to divide the stable area into small window areas using a preset small sliding window, and calculate the average coherence coefficient of each small window area to mark the small window area with the smallest average coherence coefficient as a high coherence candidate area.

[0152] The pixel marking submodule is used to calculate the coherence coefficient of each pixel in the high coherence candidate area and mark the pixel with the highest coherence coefficient as the initial control point.

[0153] The threshold judgment submodule is used to determine the mean value of the coherence coefficient of the neighborhood of the initial control point and to judge whether the mean value of the coherence coefficient exceeds a preset threshold.

[0154] The control point determination submodule is used to use the initial control point as the ground control point of the adjacent time phase interferogram when the mean value of the coherence coefficient exceeds a preset threshold.

[0155] The region elimination submodule is used to eliminate the stable region from the adjacent phase interference graph when the mean value of the coherence coefficient does not exceed the preset threshold, and return to execute the large window region with the largest proportion as the stable region and subsequent steps.

[0156] In one embodiment, the first measurement module 220 may further include:

[0157] The interference pattern difference submodule is used to generate a terrain fringe map using digital elevation data, and to differentiate the terrain fringe map from adjacent time-phase interference patterns to generate an intermediate interference pattern.

[0158] The error correction submodule is used to perform phase unwrapping and error correction on the intermediate interferogram through ground control points to obtain the final interferogram.

[0159] The differential interferometry submodule is used to perform differential interferometry on the final interferogram to generate short-term deformation information of the transmission tower slope.

[0160] In one embodiment, the second measurement module 230 may include:

[0161] The network generation submodule is used to screen image pairs that meet the constraints from the radar image data based on short-term deformation information, and perform interference processing on the image pairs to generate an initial interferogram network; the constraints include time constraints and spatial constraints.

[0162] The network optimization submodule is used to optimize the initial interference graph network using the minimum generation number algorithm to obtain the intermediate interference graph network.

[0163] The interference pair removal submodule is used to calculate the phase closure loop residual of each interference pattern in the intermediate interference pattern network, and remove the interference patterns whose phase closure loop residual exceeds a preset residual threshold from the intermediate interference pattern network to obtain a small baseline interference pattern network.

[0164] In one embodiment, the candidate point selection module 240 may include:

[0165] The image marking submodule is used to select any radar image from the radar image data and mark it as the main image, and mark other radar images in the radar image data except the main image as auxiliary images.

[0166] The image alignment submodule is used to align each auxiliary image to the reference coordinate system of the main image using a feature matching algorithm to obtain an image pair.

[0167] The phase difference calculation submodule is used to calculate the phase difference of the image pair to generate a multi-temporal interferogram based on the calculation results.

[0168] In one embodiment, the candidate point selection module 240 may further include:

[0169] The region screening submodule is used to screen out the long-term stable region from the multi-temporal interferogram based on the long-term deformation information, and calculate the amplitude deviation index and coherence coefficient of each pixel in the long-term stable region to obtain the calculation results.

[0170] The candidate point selection submodule is used to determine the high coherence pixels in the long-term stable area based on the calculation results, and select permanent scatterer candidate points from each high coherence pixel according to a preset coherence coefficient threshold.

[0171] In one embodiment, the third measurement module 250 may include:

[0172] The phase removal submodule is used to obtain the track data of the transmission tower slope and differentially remove the terrain phase from the multi-temporal interferogram based on the track data and digital elevation data.

[0173] The separation processing submodule is used to separate the atmospheric delay phase in the multi-temporal interferogram after difference based on the permanent scatterer candidate points by using the space-time filtering method.

[0174] In one embodiment, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the tower slope deformation monitoring method as described in any of the above embodiments.

[0175] In one embodiment, the present application also provides a computer device having computer-readable instructions stored therein. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the tower slope deformation monitoring method as described in any of the above embodiments.

[0176] Schematically, as Figure 5 As shown, Figure 5 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 5 Computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions executable by processing component 302, such as application programs. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 302 is configured to execute the instructions to perform the tower slope deformation monitoring method according to any of the above-described embodiments.

[0177] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0178] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0179] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0180] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0181] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one 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 present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring tower slope deformation, characterized in that: The method comprises: Collecting radar image data and digital elevation data of the transmission tower slope, and performing differential interference processing on adjacent radar images in the radar image data to obtain adjacent time-phase interference graphs; Selecting ground control points of the adjacent time-phase interferograms, and performing quality screening and differential interferometry on the adjacent time-phase interferograms using the digital elevation data and the ground control points to generate short-term deformation information; A small baseline interferogram network is constructed using the short-term deformation information and the radar image data, and a singular value decomposition method is used to solve the cumulative deformation rate of the small baseline interferogram network to obtain long-term deformation information; Selecting any radar image from the radar image data, registering it with other radar images, and then performing interference processing to obtain a multi-temporal interferogram, and selecting permanent scatterer candidate points of the multi-temporal interferogram based on the long-term deformation information; The multi-temporal interferogram is subjected to deformation phase separation processing based on the permanent scatterer candidate points and the digital elevation data, and the processed multi-temporal interferogram is parameterized using an iterative method to obtain temporal deformation information.

2. The tower slope deformation monitoring method according to claim 1, characterized in that: The selecting of the ground control points of the adjacent temporal interferograms comprises: Determining the phase derivative variance of each pixel in the adjacent time-phase interferogram, and binarizing each phase derivative variance to mark the type of each pixel according to the binarization result; the type includes stable pixels and unstable pixels; Using a preset large sliding window to divide the adjacent time-phase interferogram into large window areas, and counting the proportion of stable pixels in each large window area, so as to mark the large window area with the largest proportion as a stable area; Using a preset small sliding window to divide the stable area into small window areas, and calculating the average coherence coefficient of each small window area, so as to mark the small window area with the smallest average coherence coefficient as a high coherence candidate area; Calculating the coherence coefficient of each pixel in the high coherence candidate area, and marking the pixel with the highest coherence coefficient as the initial control point; Determining a mean value of coherence coefficients in a neighborhood of the initial control point, and determining whether the mean value of coherence coefficients exceeds a preset threshold; If yes, taking the initial control point as the ground control point of the adjacent temporal interferogram; If not, the stable region is removed from the adjacent time-phase interference graph, and the process returns to the step of marking the large window region with the largest proportion as the stable region and subsequent steps.

3. The tower slope deformation monitoring method according to claim 1, characterized in that: The method of using the digital elevation data and the ground control points to perform quality screening and differential interferometry on the adjacent temporal interferograms to generate short-term deformation information includes: Generating a topographic fringe map using the digital elevation data, and performing a differential operation between the topographic fringe map and the adjacent time-phase interference map to generate an intermediate interference map; performing phase unwrapping and error correction on the intermediate interferogram using the ground control points to obtain a final interferogram; Perform differential interferometry on the final interference pattern to generate short-term deformation information of the transmission tower slope.

4. The tower slope deformation monitoring method according to claim 1, characterized in that: The constructing of a small baseline interferogram network using the short-term deformation information and the radar image data includes: Based on the short-term deformation information, image pairs that meet constraint conditions are selected from the radar image data, and interferometric processing is performed on the image pairs to generate an initial interferogram network; wherein the constraint conditions include time constraints and space constraints; The initial interference graph network is optimized by using a minimum generation number algorithm to obtain an intermediate interference graph network; The phase closure loop residual of each interferogram in the intermediate interferogram network is calculated, and the interferograms whose phase closure loop residual exceeds a preset residual threshold are removed from the intermediate interferogram network to obtain a small baseline interferogram network.

5. The tower slope deformation monitoring method according to claim 1, characterized in that: The step of selecting any radar image from the radar image data, registering it with other radar images, and then performing interference processing to obtain a multi-temporal interferogram includes: Select any radar image from the radar image data and mark it as a primary image, and mark other radar images in the radar image data except the primary image as secondary images; Using a feature matching algorithm to align each auxiliary image to the reference coordinate system of the main image to obtain an image pair; Phase difference calculation is performed on the image pair to generate a multi-temporal interferogram according to the calculation result.

6. The tower slope deformation monitoring method according to claim 5, characterized in that: The selecting of permanent scatterer candidate points of the multi-temporal interferogram based on the long-term deformation information includes: Based on the long-term deformation information, a long-term stable region is screened from the multi-temporal interferogram, and an amplitude dispersion index and a coherence coefficient of each pixel in the long-term stable region are calculated to obtain a calculation result; Based on the calculation result, high coherence pixels in the long-term stable area are determined, and permanent scatterer candidate points are selected from each high coherence pixel according to a preset coherence coefficient threshold.

7. The tower slope deformation monitoring method according to claim 1, characterized in that: The performing deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and the digital elevation data includes: Acquiring track data of the transmission tower slope, and differentially removing terrain phase from the multi-temporal interferogram based on the track data and the digital elevation data; Based on the permanent scatterer candidate points, a space-time filtering method is used to separate the atmospheric delay phase in the differential multi-temporal interferogram.

8. A tower slope deformation monitoring device, characterized in that: include: A data collection module is used to collect radar image data and digital elevation data of the transmission tower slope, and perform differential interference processing on adjacent radar images in the radar image data to obtain adjacent time-phase interference diagrams; a first measurement module, configured to select ground control points of the adjacent time-phase interferograms, and perform quality screening and differential interferometry on the adjacent time-phase interferograms using the digital elevation data and the ground control points to generate short-term deformation information; A second measurement module is configured to construct a small baseline interferogram network using the short-term deformation information and the radar image data, and to solve the cumulative deformation rate of the small baseline interferogram network using a singular value decomposition method to obtain long-term deformation information; a candidate point selection module, configured to select any radar image from the radar image data, register it with other radar images, and then perform interference processing to obtain a multi-temporal interferogram, and select permanent scatterer candidate points in the multi-temporal interferogram based on the long-term deformation information; The third measurement module is used to perform deformation phase separation processing on the multi-temporal interferogram based on the permanent scatterer candidate points and the digital elevation data, and use an iterative method to solve the parameters of the processed multi-temporal interferogram to obtain time series deformation information.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the tower slope deformation monitoring method according to any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the tower slope deformation monitoring method according to any one of claims 1 to 7 are executed.

Images