Building deformation detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a building deformation detection method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting a first vector frame of a target building in a geographic coordinate system, projecting the first vector frame from the geographic coordinate system to an SAR image coordinate system, and obtaining a second vector frame in the SAR image coordinate system; extracting an interferometric phase data subset of each pixel point in the second vector frame, and screening out a stable point candidate set from the interferometric phase data subsets; constructing a local coherence network based on the stable point candidate set, and selecting an optimal reference point from the local coherence network; and calculating a deformation time sequence of other reference points in the local coherence network relative to the optimal reference point, and determining a deformation detection result of the target building according to the deformation time sequence. According to the method, the optimal reference point is selected in the local network of each building, a local relative deformation detection network for the building is formed, and the method is more suitable for analyzing differential deformation such as differential settlement and torsion in the building.

Description

Technical Field

[0001] This invention relates to the field of building inspection technology, specifically to methods, devices, electronic equipment, and storage media for detecting building deformation. Background Technology

[0002] With the continuous advancement of urbanization, the need for long-term, detailed health monitoring of individual buildings is becoming increasingly urgent. Temporal InSAR technology, with its wide-area coverage and high precision, has been widely adopted in practical applications. Currently, traditional temporal InSAR processing methods, represented by PS-InSAR and SBAS, generally perform unified, holistic calculations and analyses on the entire area covered by synthetic aperture radar imagery.

[0003] However, when this full-scene processing mode is applied to the specialized monitoring of individual buildings, the selection of reference benchmarks is limited. Traditional methods usually select a stable area far away from the deformation zone within the entire scene as a global unified reference benchmark. Such a distant reference point may introduce unnecessary atmospheric errors when analyzing the differential deformation such as uneven settlement and thermal expansion inside the building. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for detecting deformation of buildings, in order to solve the problem of how to improve the accuracy of detecting differential deformation inside buildings.

[0005] In a first aspect, the present invention provides a method for detecting the deformation of a building, the method comprising: Extract the first vector box of the target building in the geographic coordinate system, and project the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system. The second vector box contains multiple pixels. Extract a subset of interference phase data for each pixel within the second vector frame, and select a candidate set of stable points from the subset of interference phase data based on the time series amplitude deviation of each pixel. A local coherence network is constructed based on the candidate set of stable points, and the optimal reference point is selected from the local coherence network. Calculate the deformation time series of other reference points within the local coherence network relative to the optimal reference point, and determine the deformation detection result of the target building based on the deformation time series.

[0006] This invention extracts a first vector box of the target building in the geographic coordinate system, projects the first vector box from the geographic coordinate system to the SAR image coordinate system, and obtains a second vector box in the SAR image coordinate system. It then extracts a subset of interferometric phase data for each pixel within the second vector box and selects a candidate set of stable points from this subset. Based on the candidate set of stable points, it constructs a local coherence network and selects the optimal reference point from the local coherence network. Finally, it calculates the deformation time series of other reference points within the local coherence network relative to the optimal reference point, and determines the deformation detection result of the target building based on the deformation time series. This invention selects the optimal reference point within the local network of each building, forming a local relative deformation detection network for that building, which is more suitable for analyzing differential deformations such as uneven settlement and torsion within buildings.

[0007] In one optional implementation, the step of selecting a candidate set of stable points from the subset of interferometric phase data based on the time-series amplitude deviation of each pixel includes: Calculate the amplitude deviation of each pixel and compare the amplitude deviation with a preset amplitude deviation threshold to obtain a reference point where the amplitude deviation is less than the amplitude deviation threshold; Based on the reference point, a candidate set of stable points is constructed.

[0008] In one optional implementation, constructing a local coherence network based on the candidate set of stable points includes: For any two reference points in the candidate set of stable points, solve for the optimal elevation error and optimal linear rate between the two points; Based on the optimal elevation error and the optimal linear rate, calculate the coherence value between each pair of the reference points; The maximum coherence value is selected from the calculated coherence values. If the maximum coherence value is greater than a preset coherence threshold, a connection edge is established between the two reference points corresponding to the maximum coherence value. The local coherence network is constructed based on the connecting edges.

[0009] In one optional implementation, the step of solving for the optimal elevation error and optimal linear rate between any two reference points in the candidate set of stable points includes: The optimal elevation error and optimal linear velocity between two points can be solved using the following formulas:

[0010] In the formula, Indicates the optimal elevation error; Indicates the optimal linear rate; This represents the relative elevation difference between two reference points; This represents the relative linear deformation rate between two reference points; This represents the total number of SAR images of the target building; Indicates the first Scenery; Indicates reference point and reference points ; Indicates the first Scene Image The observation differential phase between the two points; A phase model representing relative elevation difference and relative linear deformation rate; It represents the imaginary unit.

[0011] In one optional implementation, the local coherence network includes multiple reference points, and selecting the optimal reference point from the local coherence network includes: The reference point with the highest connectivity in the local coherent network is selected as the optimal reference point.

[0012] In one optional implementation, calculating the deformation time series of other reference points within the local coherence network relative to the optimal reference point includes: For each neighboring reference point connected to the optimal reference point, calculate the residual phase sequence of the neighboring reference point; The residual phase sequence is unwrapped with temporal variation constraints, and the unwrapped residual phase sequence is converted into the deformation time sequence.

[0013] In one optional implementation, projecting the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain a second vector box in the SAR image coordinate system includes: Based on SAR satellite imaging parameters and digital elevation models, the first vector box is projected from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system.

[0014] Secondly, the present invention provides a deformation detection device for buildings, the device comprising: The coordinate projection module is used to extract the first vector box of the target building in the geographic coordinate system, and project the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system. The second vector box contains multiple pixels. The data filtering module is used to extract a subset of interference phase data for each pixel within the second vector frame, and to filter out a candidate set of stable points from the subset of interference phase data based on the time series amplitude deviation of each pixel. A network construction module is used to construct a local coherence network based on the candidate set of stable points, and select the optimal reference point from the local coherence network; The deformation detection module is used to calculate the deformation time series of other reference points in the local coherence network relative to the optimal reference point, and to determine the deformation detection result of the target building based on the deformation time series.

[0015] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the deformation detection method of the building described in the first aspect or any corresponding embodiment thereof.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the deformation detection method for a building according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a method for detecting the deformation of a building according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a building deformation detection device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] 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.

[0020] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] As an optional application scenario of this invention, such as Figure 1 As shown, the deformation detection system for this building may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0023] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0024] With the continuous advancement of urbanization, the need for long-term, detailed health monitoring of individual buildings is becoming increasingly urgent. Temporal InSAR technology, with its wide-area coverage and high precision, has been widely adopted in practical applications. Currently, traditional temporal InSAR processing methods, represented by PS-InSAR and SBAS, generally perform unified, holistic calculations and analyses on the entire area covered by synthetic aperture radar imagery.

[0025] However, when this full-scene processing mode is applied to the specialized monitoring of individual buildings, the selection of reference benchmarks is limited. Traditional methods usually select a stable area far away from the deformation zone within the entire scene as a global unified reference benchmark. Such a distant reference point may introduce unnecessary atmospheric errors when analyzing the differential deformation such as uneven settlement and thermal expansion inside the building.

[0026] Based on this, according to an embodiment of the present invention, a method for detecting the deformation of a building is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] This embodiment provides a method for detecting the deformation of a building, which can be used in the aforementioned building deformation detection system. Figure 2 This is a flowchart of a building deformation detection method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Extract the first vector box of the target building in the geographic coordinate system, project the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system. The second vector box contains multiple pixels.

[0028] Step S202: Extract the subset of interference phase data for each pixel within the second vector box, and select a candidate set of stable points from the subset of interference phase data based on the time series amplitude deviation of each pixel.

[0029] Step S203: Construct a local coherence network based on the candidate set of stable points, and select the optimal reference point from the local coherence network.

[0030] Step S204: Calculate the deformation time series of other reference points in the local coherence network relative to the optimal reference point, and determine the deformation detection result of the target building based on the deformation time series.

[0031] The deformation detection method for buildings provided in this embodiment extracts a first vector box of the target building in the geographic coordinate system, projects the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain a second vector box in the SAR image coordinate system; extracts a subset of interferometric phase data for each pixel in the second vector box, and filters a candidate set of stable points from the subset of interferometric phase data; constructs a local coherence network based on the candidate set of stable points, selects the optimal reference point from the local coherence network; calculates the deformation time series of other reference points in the local coherence network relative to the optimal reference point, and determines the deformation detection result of the target building based on the deformation time series. This invention selects the optimal reference point within the local network of each building, forming a local relative deformation detection network for that building, which is more suitable for analyzing differential deformations such as uneven settlement and torsion within buildings.

[0032] The steps described above are explained in detail below.

[0033] In step S201, the first vector box of the target building in the geographic coordinate system is extracted, and the first vector box is projected from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system. The second vector box contains multiple pixels.

[0034] In one embodiment, projecting a first vector box from a geographic coordinate system to a SAR image coordinate system to obtain a second vector box in the SAR image coordinate system includes: Based on SAR satellite imaging parameters and digital elevation models, the first vector box is projected from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system.

[0035] A geographic coordinate system is a spatial reference coordinate system based on the Earth's sphere, using latitude and longitude to accurately describe geographical locations. The first vector frame is a polygonal region defined in the geographic coordinate system that completely surrounds the target building. Its vertex coordinates are represented by latitude and longitude values. Projecting the first vector frame from the geographic coordinate system to the SAR image coordinate system yields the second vector frame in the SAR image coordinate system. The second vector frame will contain multiple pixels, which are the basic imaging units of the synthetic aperture radar image.

[0036] In one specific implementation, based on SAR satellite imaging parameters and a digital elevation model (DEM), the coordinates of a first vector frame are calculated, and the first vector frame is projected from the geographic coordinate system to the SAR image coordinate system, thereby obtaining a second vector frame corresponding to the geographic area and located on the SAR image. A DEM is a three-dimensional terrain model that records surface elevation information using regular grid cells.

[0037] Specifically, techniques such as point cloud data extraction, optical remote sensing stereo image pairs, and oblique photogrammetry model extraction are used to obtain the first vector box of the target building in the geographic coordinate system. This embodiment does not limit this.

[0038] Among them, the first vector box of the target building in the geographic coordinate system is composed of It consists of vertices, each vertex Includes longitude, latitude, and elevation information:

[0039]

[0040] in, This represents the first vector box of the target building in the geographic coordinate system. This indicates the number of vertices in the first vector box; Represents the vertices of the first vector box; This indicates the longitude of each vertex; Represents the latitude of each vertex; This represents the elevation information of each vertex.

[0041] SAR imagery refers to a single-view, multi-image SAR image containing a target area. SAR (Synthetic Aperture Radar) satellite imaging parameters include the SAR satellite's imaging angle, viewing direction, incident angle, and satellite orbit information. These parameters determine the geometric relationship of the SAR satellite's imaging of the ground and buildings, and affect the radar's illumination area and image clarity.

[0042] A digital elevation model (DEM) refers to precise elevation data covering the target building and its surrounding area. The horizontal coordinate system of the DEM must be consistent with the geographic coordinate system of the first vector frame, and it must provide the elevation value for each grid point.

[0043] The SAR image coordinate system is a two-dimensional coordinate system in SAR images, typically using pixel row and column numbers to represent positions within the image. When projecting the first vector box from geographic coordinates to SAR image coordinates in a forward direction, for each geographic coordinate point on the outline of the first vector box, a path is found that passes through that point and conforms to the radar imaging geometry, so that a unique corresponding pixel can be found on the SAR image.

[0044] Synthetic Aperture Radar (SAR), as a side-looking active imaging system, suffers from severe geometric distortions in images caused by undulating terrain, primarily including overlay, perspective contraction, and shadowing. Using a simple planar orthophoto projection formula results in significant positional and shape deviations in the transformation from the geographic coordinate system to the SAR image coordinate system, making it impossible to accurately pinpoint the true imaging area of ​​the target building. Therefore, this embodiment employs terrain-corrected projection transformation technology to project the first vector frame in geographic coordinates into a second vector frame in the SAR image coordinate system.

[0045] Specifically, the latitude and longitude coordinates of the boundary points of the first vector frame are first read, and then encrypted interpolation is performed on the boundary line at preset intervals. For each coordinate point, bilinear interpolation is performed to obtain its corresponding elevation value, thus forming a sequence of three-dimensional geographic points with elevation information.

[0046] Next, SAR imaging parameters are loaded, and the range equation and Doppler equation are constructed. The range equation characterizes the instantaneous slant range of a ground point from the satellite antenna as equal to the slant range represented by the corresponding pixel on the image. The Doppler equation characterizes the Doppler frequency generated by the radial velocity of the ground point relative to the satellite as equal to the Doppler center frequency of the image in that azimuth direction. The range equation and the Doppler equation together constrain a ground point to lie on the intersection of the equission slant range plane and the equidoppler plane defined by a specific pixel at the time of imaging.

[0047] For each 3D geographic point in the sequence, an average elevation is first assumed, or the initial azimuth, time, and slant range of the 3D geographic point are estimated using a simplified model. Based on the estimated azimuth, time, slant range, and satellite orbit data, an equidistant and Doppler curve in space is calculated by solving the distance equation and the Doppler equation. The spatial curve is then intersected in 3D space with a terrain surface model composed of a digital elevation model to obtain the intersection point with the surface of the terrain surface model.

[0048] The algorithm calculates the difference between the horizontal position of the intersection point and the horizontal position of the input 3D geographic point. Simultaneously, it compares the elevation of the intersection point with the elevation of the 3D geographic point. Using the calculated difference, a numerical optimization algorithm updates the estimated azimuth time and slope range until the error between the horizontal position of the intersection point and the horizontal position of the input 3D geographic point is less than a preset threshold, or the maximum number of iterations is reached. At this point, the final azimuth time and slope range are converted into azimuth row numbers and range column numbers, respectively, using the image's temporal sequence and sampling parameters.

[0049] The image coordinates, consisting of azimuth row numbers and range column numbers, obtained after the above iterative calculations of all 3D geographic points in the sequence, are connected in their original order to form a closed polygonal region on the SAR image. This polygon is the second vector box after terrain correction.

[0050] In the formula, Indicates the second vector box; This refers to terrain correction projection conversion technology; Indicates the first vector box; Represents a digital elevation model; This represents SAR satellite imaging parameters.

[0051] In this context, each vertex of the second vector box corresponds to the row and column coordinates of the SAR image:

[0052] In the formula, Indicates the second vector box; This indicates the number of vertices in the second vector box, which is the same as the number of vertices in the first vector box; Represents the vertices of the first vector box. This represents the row number coordinates of each vertex in the SAR image. This indicates the column number coordinates of each vertex in the SAR image.

[0053] All pixels within the second vector frame constitute the SAR image data area that precisely corresponds to the actual surface area of ​​the target building after eliminating the influence of terrain distortion. This embodiment overcomes the influence of terrain distortion in side-looking radar imaging, ensuring that the extracted image area highly matches the geographical range of the target building. By combining iterative processing with a digital elevation model, slant range errors caused by terrain can be corrected, ensuring that even in complex terrain, the second vector frame can maximally conform to the true imaging geometry of the target building on the slant range plane, laying a precise spatial data foundation for subsequent targeted deformation analysis.

[0054] In step S202, a subset of interference phase data for each pixel within the second vector frame is extracted, and a candidate set of stable points is selected from the subset of interference phase data based on the time series amplitude deviation of each pixel.

[0055] Specifically, the time-series SLC image data stack for the area covered by the second vector frame is loaded. A time-series SLC image data stack refers to a collection of SAR images acquired from repeated observations of the same area at different times by the same SAR satellite sensor, arranged in a time sequence. Its data format is SLC. SLC refers to the single-look complex format of SAR satellite data, where each pixel value not only contains echo intensity information but, more importantly, retains complete complex information, namely the real and imaginary parts, or equivalent amplitude and phase.

[0056] From the temporal SLC data stack, the image that is centered in time and has the best imaging quality is selected as the master image. All other images are used as auxiliary images, and the master image serves as the common reference benchmark for all interferometry calculations in the spatial coordinate system. Each auxiliary image is paired with the master image to form an interferometric pair, which is used to generate subsequent differential interferograms.

[0057] Based on the precisely projected and positioned second vector frame, targeted data extraction is performed on each interferometric pair consisting of a main image and a secondary image. Specifically, only all pixels within the polygonal region of the second vector frame in both the main and secondary SLC images are extracted. For each extracted pixel pair, conjugate multiplication and other operations are performed using their complex information to generate a small-scale differential interferogram containing only the target building area. The value of each pixel in the differential interferogram is the interferometric phase. The small-scale differential interferograms generated from all interferometric pairs are organized and stored according to their time series, ultimately forming a subset of interferometric phase data for each pixel within the second vector frame. The subset of interferometric phase data refers to the set of interferometric phase observations refined from the massive data of the entire scene, corresponding only to the imaging area of ​​the target building, and arranged in a time series.

[0058] Extract the second vector box Time-series SLC data for all internal pixels. For any pixel... Calculate the first Interference phase of the secondary image relative to the primary image:

[0059] In the formula, Indicates the calculation of the first The interference phase of the secondary image relative to the primary image; Indicates the first Complex values ​​of SLC data from landscape auxiliary images; Represents the conjugate of the complex values ​​of the main image; This indicates the phase angle extraction operation, resulting in a subset of the interferometric phase data.

[0060] In the formula, Represents a subset of interferometric phase data; Represents image coordinates Pixel interference phase time series data at the location; Indicates the second vector box A closed area that is enclosed.

[0061] In one embodiment, a candidate set of stable points is selected from a subset of the interferometric phase data based on the time-series amplitude deviation of each pixel, including: Calculate the amplitude deviation of each pixel and compare it with a preset amplitude deviation threshold to obtain reference points whose amplitude deviation is less than the amplitude deviation threshold; based on the reference points, construct a candidate set of stable points.

[0062] Specifically, in the subset of interferometric phase data, the mean and standard deviation of the time series for each pixel are calculated, and the amplitude deviation for each pixel is calculated using the following formula:

[0063] In the formula, This represents the amplitude deviation of each pixel; This represents the mean of the time series for each pixel. The standard deviation of the time series for each pixel.

[0064] Set the amplitude deviation threshold to , will satisfy The pixels were selected as candidate stable points. .

[0065] In step S203, a local coherence network is constructed based on the candidate set of stable points, and the optimal reference point is selected from the local coherence network.

[0066] In one embodiment, constructing a local coherence network based on a stable point candidate set includes: For any two reference points in the candidate set of stable points, solve for the optimal elevation error and optimal linear velocity between the two points; based on the optimal elevation error and optimal linear velocity, calculate the coherence value between each pair of reference points; select the maximum coherence value from the calculated coherence values; if the maximum coherence value is greater than a preset coherence threshold, establish a connection edge between the two reference points corresponding to the maximum coherence value; construct a local coherence network based on the connection edge.

[0067] In one embodiment, a coherence network is constructed within a candidate set of stable points. For any two reference points in the candidate set, the optimal elevation error and optimal linear velocity between the two points are solved. Specifically, the optimal elevation error and optimal linear velocity between the two points are solved using the following formulas:

[0068] In the formula, Indicates the optimal elevation error; Indicates the optimal linear rate; Indicates reference point and reference points The relative elevation difference between them; Indicates reference point and reference points The relative linear deformation rate between them; This represents the total number of SAR images of the target building; Indicates the first Scenery; Indicates reference point and reference points ; Indicates the first Scene Image The observation differential phase between the two points; A phase model representing relative elevation difference and relative linear deformation rate. The search area is set according to the height of the target building, with a search step of 0.5 meters. The search range is [-50, 50] mm / year, the search step size is 0.5 mm / year, and all values ​​within the search range are traversed. combination; It represents the imaginary unit.

[0069] At the same time, the maximum coherence value corresponding to the edge between the two reference points is obtained:

[0070] In the formula, Indicates reference point and reference points The maximum coherence value corresponding to the edge between them; Total number of SAR images; For the first Scene Image The observation differential phase between the two points; It's about reference points. and reference points A phase model for the optimal elevation difference and optimal linear deformation rate between them; Indicates the optimal elevation error; This represents the optimal linear rate. If the maximum coherence value exceeds the set coherence threshold, a connection reference point is added to the edge set. and reference points The edge; It represents the imaginary unit.

[0071] in, The calculation process is as follows:

[0072]

[0073]

[0074] In the formula, Indicates reference point and reference points Phase model of the elevation difference between them; Indicates reference point and reference points A phase model of linear deformation rates between them; It's about reference points. and reference points A phase model of elevation difference and linear deformation rate; , , For SAR satellite imaging parameters, For radar wavelength, Slope distance Angle of incidence; For the first Vertical spatial baseline between the secondary and primary images. For the first The temporal baseline between the secondary and primary images; Indicates reference point and reference points The relative elevation difference between them; Indicates reference point and reference points The relative linear deformation rate between them.

[0075] In one embodiment, the local coherence network includes multiple reference points, and selecting the optimal reference point from the local coherence network includes: The reference point with the highest connectivity in the local coherent network is selected as the optimal reference point.

[0076] Specifically, in locally coherent networks, the reference point with the highest network connectivity is selected as the locally optimal reference point:

[0077] In the formula, Indicates a locally optimal reference point; Indicates a reference point; Represents the set of nodes in a locally coherent network; Indicates reference point Network connectivity refers to the number of edges corresponding to each reference point.

[0078] In step S204, the deformation time series of other reference points in the local coherence network relative to the optimal reference point is calculated, and the deformation detection result of the target building is determined based on the deformation time series.

[0079] In one embodiment, calculating the deformation time series of other reference points within the local coherence network relative to the optimal reference point includes: For each neighboring reference point connected to the optimal reference point, calculate the residual phase sequence of the neighboring reference points; perform phase unwrapping on the residual phase sequence with temporal variation constraints, and convert the unwrapped residual phase sequence into a deformation time series.

[0080] For the optimal reference point Each connected neighboring point Calculate its residual phase sequence :

[0081] In the formula, Represents the optimal reference point Each connected neighboring point The corresponding residual phase sequence; Indicates the first Optimal reference point in landscape image and its neighboring points The observed differential phase between; Indicates the first Vertical spatial baseline between the secondary and primary images. Indicates the first The temporal baseline between the secondary and primary images; , , For SAR satellite imaging parameters, For radar wavelength, Slope distance Angle of incidence; Indicates the optimal reference point and its neighboring points Optimal elevation error between

[0082] Next, a time-dimensional phase unwrapping algorithm is used to process the residual phase sequence, resulting in the unwrapped residual phase sequence. It is assumed that the phase change between adjacent time sampling points is less than... .set up For the first The phase after untangling at a given moment. .for The untangling process involves finding the integer. , so that:

[0083] In the formula, Indicates the first The phase after untangling at a given moment; Indicates the first The phase after untangling at a given moment; .

[0084] The unwrapped residual phase sequence is converted into a deformation time sequence along the radar line of sight (LOS) according to the radar wavelength:

[0085] In the formula, This represents the deformation time series of other reference points relative to the optimal reference point; Indicates the radar wavelength; For the first The residual phase sequence after untangling at each time point.

[0086] In one embodiment, the calculation process and results for each vector frame are stored independently, and the deformation detection result of the target building is ultimately determined based on the deformation time series. After phase unwrapping, for each high-quality monitoring point within the target building area, such as a permanent scatterer or a distributed scatterer, its deformation time series along the sensor's line of sight has been obtained. The deformation time series refers to a series of cumulative deformations of other reference points relative to the optimal reference point, arranged chronologically throughout the entire observation period.

[0087] Based on the deformation time series of all reference points within the building area relative to the optimal reference point, statistical analysis is used to determine the overall deformation pattern of the target building during the observation period. For example, it can be determined that the building exhibits uniform settlement, overall uplift, stable without significant deformation, or obvious trend movement.

[0088] By comparing the deformation time series of monitoring points on different parts or structural units of a building, it is possible to identify whether the building has uneven settlement, torsional deformation, or local instability. Specifically, this can be achieved by analyzing deformation rate contour maps, calculating the relative deformation of adjacent point pairs, or detecting jumps and nonlinear trends in the sequence of specific points. For example, if monitoring points on one side of the building show continuous settlement while the other side remains stable, it indicates differential settlement of the building, which may pose a threat to structural safety.

[0089] Correlation analysis can be performed between deformation time series and timelines of external factors. For example, if the deformation series shows periodic fluctuations that are highly correlated with temperature changes, it can be inferred that it may mainly originate from the thermo-expansion effect of building materials; if the deformation accelerates after a certain external event, the impact of that event on building stability can be assessed.

[0090] Finally, the above analysis results are transformed into quantitative indicators and visual reports that can be used for engineering judgment. Typical deformation detection results include, but are not limited to: the average deformation rate of the building as a whole and key parts (unit: mm / year), the maximum differential deformation and its location, deformation time series curves, deformation rate cloud maps or contour maps, cumulative deformation distribution maps of key stages, and deformation early warning information based on preset alarm thresholds.

[0091] This embodiment associates deformation analysis results with the target building using vector frames and stores them in a separate folder, solving the problem of difficulty in corresponding deformation points with building structures in traditional methods, thus facilitating refined structural health assessments. Simultaneously, the phase unwrapping algorithm with time-series constraints effectively filters out anomalous jumps during the unwrapping process, improving the accuracy and reliability of the deformation time series results. Furthermore, by selecting the optimal reference point within the local network of each building, a local relative deformation monitoring network is constructed for that building, making it more suitable for analyzing differential deformations such as uneven settlement and torsion within the building.

[0092] This embodiment also provides a deformation detection device for buildings, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0093] This embodiment provides a deformation detection device for buildings, such as... Figure 3 As shown, it includes: The coordinate projection module 301 is used to extract the first vector box of the target building in the geographic coordinate system, project the first vector box from the geographic coordinate system to the SAR image coordinate system, and obtain the second vector box in the SAR image coordinate system. The second vector box contains multiple pixels. The data filtering module 302 is used to extract a subset of the interference phase data of each pixel in the second vector box, and to filter out a set of stable points from the subset of interference phase data based on the time series amplitude deviation of each pixel. The network construction module 303 is used to construct a local coherence network based on a stable point candidate set and select the optimal reference point from the local coherence network. The deformation detection module 304 is used to calculate the deformation time series of other reference points in the local coherence network relative to the optimal reference point, and to determine the deformation detection result of the target building based on the deformation time series.

[0094] In some alternative implementations, the coordinate projection module 301 includes: The coordinate projection unit is used to project the first vector box from the geographic coordinate system to the SAR image coordinate system based on SAR satellite imaging parameters and digital elevation model, so as to obtain the second vector box in the SAR image coordinate system.

[0095] In some alternative implementations, the data filtering module 302 includes: The candidate set construction unit is used to calculate the amplitude deviation of each pixel and compare the amplitude deviation with a preset amplitude deviation threshold to obtain reference points whose amplitude deviation is less than the amplitude deviation threshold; based on the reference points, a stable point candidate set is constructed.

[0096] In some alternative implementations, network building module 303 includes: The network construction unit is used to solve for the optimal elevation error and optimal linear velocity between any two reference points in the candidate set of stable points; based on the optimal elevation error and optimal linear velocity, the coherence value between each pair of reference points is calculated; the maximum coherence value is selected from the calculated coherence values; if the maximum coherence value is greater than a preset coherence threshold, a connection edge is established between the two reference points corresponding to the maximum coherence value; and a local coherence network is constructed based on the connection edge.

[0097] In some alternative implementations, the network building blocks solve for the optimal elevation error and optimal linear rate between two points using the following formulas:

[0098] In the formula, Indicates the optimal elevation error; Indicates the optimal linear rate; This represents the relative elevation difference between two reference points; This represents the relative linear deformation rate between two reference points; This represents the total number of SAR images of the target building; Indicates the first Scenery; Indicates reference point and reference points ; Indicates the first Scene Image The observation differential phase between the two points; A phase model representing relative elevation difference and relative linear deformation rate; It represents the imaginary unit.

[0099] In some alternative implementations, the local coherence network includes multiple reference points, and the network building module 303 further includes: The reference point selection unit is used to select the reference point with the highest connectivity in the local coherence network as the optimal reference point.

[0100] In some alternative implementations, the deformation detection module 304 includes: The sequence unwrapping unit is used to calculate the residual phase sequence of each neighboring reference point connected to the optimal reference point; perform phase unwrapping on the residual phase sequence with temporal variation constraints; and convert the unwrapped residual phase sequence into a deformation time series.

[0101] The building deformation detection device provided in this embodiment of the invention can execute the building deformation detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0102] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0103] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0104] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0105] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the building deformation detection method of the embodiments of the present invention.

[0106] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0107] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the deformation detection method for buildings shown in the above embodiments is implemented.

[0108] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0109] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for detecting the deformation of a building, characterized in that, The method includes: Extract the first vector box of the target building in the geographic coordinate system, and project the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system. The second vector box contains multiple pixels. Extract a subset of interference phase data for each pixel within the second vector frame, and select a candidate set of stable points from the subset of interference phase data based on the time series amplitude deviation of each pixel. A local coherence network is constructed based on the candidate set of stable points, and the optimal reference point is selected from the local coherence network. Calculate the deformation time series of other reference points within the local coherence network relative to the optimal reference point, and determine the deformation detection result of the target building based on the deformation time series.

2. The method according to claim 1, characterized in that, The step of selecting a candidate set of stable points from the subset of interferometric phase data based on the time-series amplitude deviation of each pixel includes: Calculate the amplitude deviation of each pixel and compare the amplitude deviation with a preset amplitude deviation threshold to obtain a reference point where the amplitude deviation is less than the amplitude deviation threshold; Based on the reference point, a candidate set of stable points is constructed.

3. The method according to claim 2, characterized in that, The construction of a local coherence network based on the candidate set of stable points includes: For any two reference points in the candidate set of stable points, solve for the optimal elevation error and optimal linear rate between the two points; Based on the optimal elevation error and the optimal linear rate, calculate the coherence value between each pair of the reference points; The maximum coherence value is selected from the calculated coherence values. If the maximum coherence value is greater than a preset coherence threshold, a connection edge is established between the two reference points corresponding to the maximum coherence value. The local coherence network is constructed based on the connecting edges.

4. The method according to claim 3, characterized in that, The step of solving for the optimal elevation error and optimal linear velocity between any two reference points in the candidate set of stable points includes: The optimal elevation error and optimal linear velocity between two points can be solved using the following formulas: In the formula, Indicates the optimal elevation error; Indicates the optimal linear rate; This represents the relative elevation difference between two reference points; This represents the relative linear deformation rate between two reference points; This represents the total number of SAR images of the target building; Indicates the first Scenery; Indicates reference point and reference points ; Indicates the first Scene Image The observation differential phase between the two points; A phase model representing relative elevation difference and relative linear deformation rate; It represents the imaginary unit.

5. The method according to claim 1, characterized in that, The local coherence network includes multiple reference points, and the step of selecting the optimal reference point from the local coherence network includes: The reference point with the highest connectivity in the local coherent network is selected as the optimal reference point.

6. The method according to claim 5, characterized in that, The calculation of the deformation time series of other reference points within the local coherence network relative to the optimal reference point includes: For each neighboring reference point connected to the optimal reference point, calculate the residual phase sequence of the neighboring reference point; The residual phase sequence is unwrapped with temporal variation constraints, and the unwrapped residual phase sequence is converted into the deformation time sequence.

7. The method according to claim 1, characterized in that, The step of projecting the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system includes: Based on SAR satellite imaging parameters and digital elevation models, the first vector box is projected from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system.

8. A deformation detection device for buildings, characterized in that, The device includes: The coordinate projection module is used to extract the first vector box of the target building in the geographic coordinate system, and project the first vector box from the geographic coordinate system to the SAR image coordinate system to obtain the second vector box in the SAR image coordinate system. The second vector box contains multiple pixels. The data filtering module is used to extract a subset of interference phase data for each pixel within the second vector frame, and to filter out a candidate set of stable points from the subset of interference phase data based on the time series amplitude deviation of each pixel. A network construction module is used to construct a local coherence network based on the candidate set of stable points, and select the optimal reference point from the local coherence network; The deformation detection module is used to calculate the deformation time series of other reference points in the local coherence network relative to the optimal reference point, and to determine the deformation detection result of the target building based on the deformation time series.

9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the deformation detection method for a building as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the deformation detection method for a building as described in any one of claims 1 to 7.

Images