Building settlement identification and classification method based on PSInSAR technology

Abstract

The invention discloses a building settlement identification and classification method based on a PSInSAR technology. The method comprises the steps of selecting a plurality of SAR images of an area where a target building is located at different time; performing data processing on the SAR image through a PSInSAR technology to obtain a PS point of the target building; selecting retest leveling net elevation data; performing interpolation calculation by using a Kriging interpolation method to obtain surface elevation data; comparing the three-dimensional coordinate position information of the PS points with the surface elevation data of the target building area, and classifying the PS points according to the difference with the surface elevation; screening by using the three-dimensional position information of the first type PS points to obtain the first type PS points on the target building and the second type PS points in the surrounding range of the first type PS points; and classifying the settlement types of the target building according to the settlement amounts of the first type PS points and the second type PS points of the area where the target building is located and the settlement amount difference value. The method has the advantage that the measurement precision reaches the mm level.

Description

Technical Field

[0001] The present invention relates to the field of surface settlement monitoring, and in particular to a method for identifying and classifying building settlement based on PSInSAR technology. Background Art

[0002] Building settlement monitoring has always been a key issue in civil engineering and urban construction. Early detection and classification assessment of building settlement can effectively prevent potential safety hazards and ensure the stability and service life of buildings. Traditional settlement monitoring methods rely on manual observation and leveling. Although these methods have certain advantages in accuracy, they are time-consuming and labor-intensive, making it difficult to achieve large-scale continuous monitoring, especially in complex terrain and high-density urban building areas. Their application is limited.

[0003] Permanent scatterer synthetic aperture radar interferometry (PSInSAR) is a high-precision surface deformation monitoring technology based on synthetic aperture radar (SAR) images, which can monitor settlement in a large area. This technology obtains permanent scatterer points (PS points) in the target area through a long series of multiple SAR images, and reflects the surface deformation through PS points with good coherence. This method has the advantages of all-weather, all-time, and high resolution, and can monitor millimeter-level surface deformation information in complex urban environments.

[0004] However, when the existing PSInSAR technology is applied to building settlement monitoring, it mainly focuses on the overall deformation of the ground surface, and it is difficult to effectively distinguish the relative relationship between the building settlement and the ground settlement, and then classify and evaluate the building settlement. In addition, how to combine accurate surface elevation data to achieve accurate comparative analysis of building and ground settlement is also a major difficulty in the existing technology. This is where the application needs to focus on improvement. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a building settlement identification and classification method based on PSInSAR technology, which uses the repeated leveling network elevation data to classify PS point data, identify and distinguish PS points on buildings and the ground surface, and classify the target building settlement type according to the settlement amount and settlement difference between the target building PS point and the surrounding surface PS points, thereby realizing the evaluation of the target building settlement situation.

[0006] In order to solve the above technical problems, the present invention provides a method for identifying and classifying building settlement based on PSInSAR technology, comprising the following steps: Step S1: select several different times in the area where the target building is located, namely, t1, t2, ..., t n Sequential SAR images; Step S2, using the permanent scatterer synthetic aperture radar interferometry technique PSInSAR to process the SAR image in the area where the target building is located, and obtain the required PS points of the target building; Step S3, selecting the re-measured leveling network elevation data of the area where the target building is located; Step S4, using the Kriging interpolation method to interpolate the selected re-surveyed leveling network elevation data to obtain the surface elevation data of the target building area; Step S5: Compare the three-dimensional coordinate position information of the PS point with the surface elevation data of the target building area, and divide the PS point data into the following three categories: ① For PS points with elevations exceeding the ground elevation by more than 15m, they are identified as Class 1 PS points obtained by reflecting electromagnetic waves from the target building; ② For PS points whose elevation is less than 5m above the ground elevation, they are identified as Class 2 PS points obtained by reflecting electromagnetic waves from the surface of the target building area; ③ For PS points whose elevation exceeds the ground elevation by 5m to 10m, it is determined that they are other low structures or trees in the target building area reflecting electromagnetic waves to obtain Class 3 PS points; Step S6, using the three-dimensional position information of the Class 1 PS point to screen and obtain the Class 1 PS point on the target building and the Class 2 PS points within a range of 10m to 15m around it; Step S7, classifying the settlement type of the target building according to the settlement amounts and settlement differences of the Class 1 PS points and Class 2 PS points in the area where the target building is located, so as to evaluate the settlement of the target building; ① Stable state: If the settlement of both the Class 1 PS point and the Class 2 PS point is less than 50 mm, the settlement of the ground surface and the building in the target building area is small and in a stable state; ② Shallow settlement: The settlement of the Class 1 PS point is less than 50 mm, and the settlement of the Class 2 PS point is greater than 100 mm. The settlement of the ground surface is significantly higher than the settlement of the building. In this case, the foundation of the building is solid, but the ground surface in the area where the target building is located has shallow settlement; ③ Building structure settlement: The settlement of the Class 1 PS point is greater than 100mm, and the settlement of the Class 2 PS point is less than 50mm. The settlement of the building is significantly higher than the settlement of the ground surface, indicating that the structural foundation of the building is weak and a large settlement has occurred on the stable ground; ④ Deep settlement: The settlement of both Class 1 and Class 2 PS points is greater than 100 mm. Both the building and the ground are unstable, and their relative movement indicates that the deep foundation will become unstable.

[0007] The beneficial effects of the present invention are: 1) With the help of radar satellites, through time-series measurement of the target area, the fine and tiny deformation of the building can be obtained, and the measurement accuracy reaches the mm level. In addition, the radar satellite has a long observation distance and a large range, and the building settlement information in the entire city can be extracted and classified; 2) Using the re-surveyed leveling network elevation data, the PS point data is classified to identify and distinguish PS points on the building and the ground (step S5). According to the settlement amount and settlement difference between the PS point of the target building and the PS points on the surrounding ground, the settlement type of the target building is classified (step S7), thereby realizing the assessment of the settlement of the target building. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flow chart of a specific embodiment of the present invention; Figure 2 It is a schematic diagram of building settlement types in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0009] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0010] like Figure 1 As shown, the present invention provides a method for identifying and classifying building settlement based on PSInSAR technology, comprising the following steps: Step S10, selecting SAR images of the target building area at several different times, i.e., a sequence of t1, t2, ..., tn; Step S20, performing data processing on the SAR image in the area where the target building is located by using the permanent scatterer synthetic aperture radar interferometry measurement technology PSInSAR to obtain the required PS points of the target building area; Step S21, using SARscap software to generate a connection diagram, generate a differential interferogram, perform parameter estimation and phase unwrapping, remove the atmospheric phase, and generate InSAR data of the target building area; Step S22: Select the second-phase resurvey leveling network elevation data of the target building area, and import it into ArcGIS Pro software together with the InSAR data. Use the Kriging interpolation tool in ArcGIS Pro software to select leveling points within 3±1 times the area of ​​the target area as reference points according to the density of PS points in the target building area. Estimate the settlement of the unknown point by weighted summation of the settlement of the reference points, that is: (1) Where: Z0 is the settlement of the unknown point, Z i is the settlement of the reference point, λ i is the weight coefficient, which satisfies the two conditions of minimum difference between the estimated value and the true value and unbiased estimation, namely: (2) (3) The above equations are derived together to obtain the Kriging equations, and the settlement of the unknown points is obtained by solving the equations, and the surface elevation data of the target building area is obtained; Step S30, comparing the three-dimensional coordinate position information of the PS point with the surface elevation data of the target building area, and classifying the PS point data into three categories: PS points with elevations exceeding the surface elevation by more than 15m are classified as Class 1 PS points; PS points with elevations exceeding the ground elevation by less than 5m are classified as Class 2 PS points; PS points with elevations exceeding the ground elevation by between 5m and 10m are classified as Class 3 PS points; Step S40, filter the Class 1 PS points on the target building and the Class 2 PS points within a 10m range around it. If the PS points in the target building area are sparse, expand the range to 15m, and classify the settlement type of the target building according to the settlement amount and settlement difference of the Class 1 PS points and Class 2 PS points in the target building area, so as to evaluate the settlement of the target building. Figure 2 As shown: ① If the settlement of both the Class 1 PS point and the Class 2 PS point is less than 50 mm, the settlement type of the target building is classified as stable; ② If the settlement of the Class 1 PS point is less than 50 mm and the settlement of the Class 2 PS point is greater than 100 mm, the settlement type of the target building is classified as shallow settlement; ③ If the settlement of the Class 1 PS point is greater than 100 mm and the settlement of the Class 2 PS point is less than 50 mm, the settlement type of the target building is classified as building structure settlement; ④ If the settlement of both Class 1 PS points and Class 2 PS points is greater than 100 mm, the settlement type of the target building is classified as deep settlement.

[0011] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying and classifying building settlement based on PSInSAR technology, comprising the following steps: Step S1, selecting a number of SAR images of the target building area at different times; Step S2, performing data processing on the SAR image in the area where the target building is located by using the permanent scatterer synthetic aperture radar interferometry technology to obtain the permanent scatterer PS point of the target building; Step S3, selecting the re-measured leveling network elevation data of the area where the target building is located; Step S4, using the Kriging interpolation method to interpolate the selected re-surveyed leveling network elevation data to obtain the surface elevation data of the target building area; Step S5, comparing the three-dimensional coordinate position information of the PS point with the surface elevation data of the target building area, and classifying the PS point into three categories according to the difference with the surface elevation: Class 1 PS point, Class 2 PS point, and Class 3 PS point; Step S6, using the three-dimensional position information of the Class 1 PS point to screen and obtain the Class 1 PS point on the target building and the Class 2 PS points within a range of 10m to 15m around it; Step S7: classify the settlement type of the target building according to the settlement amounts and settlement differences of the Class 1 PS points and Class 2 PS points in the area where the target building is located, so as to evaluate the settlement of the target building.

2. The PSInSAR-based building settlement identification and classification method according to claim 1, characterized in that: In step S5, the PS points are classified into three categories: ① For PS points with elevations exceeding the ground elevation by more than 15m, they are identified as Class 1 PS points obtained by reflecting electromagnetic waves from the target building; ② For PS points whose elevation is less than 5m above the ground elevation, they are identified as Class 2 PS points obtained by reflecting electromagnetic waves from the surface of the target building area; ③ For PS points whose elevation exceeds the ground elevation by 5m to 10m, it is determined that they are other low structures or trees in the target building area that reflect electromagnetic waves to obtain Class 3 PS points.

3. The method for identifying and classifying building subsidence based on PSInSAR technology according to claim 1, characterized in that: The target building settlement type in step S7 is: ① Stable state: The settlement of Class 1 PS points and Class 2 PS points is less than 50 mm; ② Shallow settlement: The settlement of Class 1 PS points is less than 50 mm, and the settlement of Class 2 PS points is greater than 100 mm; ③ Building structure settlement: The settlement of Class 1 PS points is greater than 100mm, and the settlement of Class 2 PS points is less than 50mm; ④Deep settlement: The settlement of Class 1 PS points and Class 2 PS points is greater than 100 mm.

4. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the building settlement identification and classification method based on PSInSAR technology as described in any one of claims 1 to 3.

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