An automatic deformation zone boundary extraction method based on Alphashape algorithm

Abstract

The invention discloses a method for automatically extracting deformation zone boundaries based on an Alphashape algorithm, which relates to the technical field of geological disaster prevention and control, and aims to solve the problem that steel epoxy sleeves have epoxy filling dissatisfaction defects during on-site construction, which affects the sleeve repair effect. The key points of the technical solution are as follows: selecting time series SAR images of the target area to generate a PS point set; PS point screening in the deformation zone: statistically analyzing the PS points obtained in step S1, screening out points with abnormal deformation rates, marking them as point set S, and the area where S is located is the deformation zone; constructing a Delaunay triangulation network for the point set S screened in step S2; deformation zone boundary extraction: on the basis of the Delaunay triangulation network, the Alphashape algorithm is used to extract the boundary line of the deformation zone. The effect of automatically extracting the deformation boundary and facilitating calculation is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of geological disaster prevention and control, and in particular to an automatic deformation zone boundary extraction method based on an Alphashape algorithm. Background Art

[0002] In the early days, surface deformation monitoring was mainly based on leveling. Leveling points were deployed to obtain the surface deformation of the entire area. Although this method has high accuracy, it is labor-intensive, inefficient and has high labor costs. In recent years, with the development of synthetic aperture radar interferometry (InSAR), large-area, high-precision surface deformation monitoring has become possible.

[0003] InSAR technology is a technology that extracts three-dimensional spatial information of ground targets based on the phase data of complex radar images. Its basic idea is to use two antennas to simultaneously image or one antenna to repeatedly image at a certain time interval to obtain complex radar image pairs of the same area. Since the distances between the two antennas and a certain ground target are not equal, a phase difference is generated between the same-name image points in the complex radar image pair, forming an interference pattern. The phase value in the interference pattern is the measured value of the phase difference between the two images. According to the geometric relationship between the phase difference between the two images and the three-dimensional spatial position of the ground target, the three-dimensional coordinates of the ground target can be determined using the parameters of the flight trajectory.

[0004] The above-mentioned existing technical solutions have the following defects: the surface deformation information obtained by InSAR technology is usually contained in a large number of measurement points (PS points). In a small area, manual interpretation can be performed based on the graded display of deformation rate to obtain the boundary of the deformation zone. However, this method has problems such as low efficiency, being affected by human subjective factors, and difficulty in ensuring accuracy in the time-series monitoring of surface deformation over a large area. Summary of the invention

[0005] The purpose of the present invention is to provide an automatic deformation zone boundary extraction method based on the Alphashape algorithm, which can quickly and automatically obtain the deformation zone boundary.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for automatically extracting deformation zone boundaries based on the Alphashape algorithm, the steps of which are as follows:

[0008] S1: Select the time series SAR images of the target area and generate the PS point set;

[0009] S2: PS point screening in the deformation zone: Statistical analysis is performed on the PS points obtained in step S1 to screen out points with abnormal deformation rates, which are marked as point set S. The area where S is located is the deformation zone;

[0010] S3: Construct a Delaunay triangulation network for the point set S selected in step S2;

[0011] S4: Deformation zone boundary extraction: Based on the Delaunay triangulation, the Alphashape algorithm is used to extract the deformation zone boundary line.

[0012] By adopting the above technical solution, the Alphashape algorithm is used to calculate the overall extracted point set, so as to facilitate the overall acquisition of the significant deformation area, increase the overall extraction speed, and increase the overall extraction accuracy.

[0013] Furthermore, the specific step S2 is to perform statistical analysis based on the PS points obtained in S1 to calculate the mean value of the deformation rate and standard deviation σ, and select the deformation rate that meets the condition according to the statistical law The PS points are marked as point set S, and the area where S is located is the area where the surface subsidence rate is abnormal.

[0014] By adopting the above technical solution, the area S with abnormal surface subsidence rate is marked, which is convenient for overall substitution into the formula for calculation, thereby facilitating the overall calculation of the significant deformation area according to the situation, thereby ensuring overall good calculation effect and calculation accuracy.

[0015] Furthermore, the mean The calculation formula is:

[0016]

[0017] The calculation formula of the standard deviation σ is:

[0018]

[0019] In the formula For the The deformation rate of each PS point.

[0020] By adopting the above technical solution, by substituting the overall data into the formula for calculation, it is convenient to quickly calculate different values ​​as a whole, ensuring the overall good calculation speed and efficiency, and increasing the overall extraction effect.

[0021] Furthermore, the specific steps of S4 are:

[0022] A1: For the Delaunay triangulation constructed in step S3, extract each edge to construct an edge set Q;

[0023] A2: Calculate the average point spacing of the point set S, marked as , as a parameter of the Alphashape algorithm;

[0024] A3: Create a new edge set T to store the boundary lines of the deformation area;

[0025] A4: Select any edge L from the edge set Q to make a judgment: when the edge length is greater than When , the edge L is not easy to be used as a boundary line, because according to the Alphashape principle, the edge length is greater than When , at least one of the two points constituting L is a discrete point;

[0026] A5: When the length of L is less than When the endpoints of L are calculated , And the radius is The coordinates of the center of the circle;

[0027] A6: Remove from the S point set , After that, calculate the remaining points to , If all points to or The distance is greater than , then L is the boundary line of the point set S, add the line segment L to the set T, if the remaining points to or The distances are not all greater than , it means that L is not the boundary line of the point set S;

[0028] A7: Perform steps A1-A6 on all edges in Q, and the edge set in T constitutes the boundary of the deformation zone;

[0029] A8: Convert the edge set in T into faces to obtain the surface data of the deformation area.

[0030] By adopting the above technical solution, the chip of the ultrasonic guided wave transducer is used to detect the whole, ensuring a good overall detection and positioning effect, thereby ensuring a good overall positioning and scanning effect, and increasing the overall filling effect.

[0031] Furthermore, the calculation formula of the average point spacing is:

[0032]

[0033] The center of the circle , The coordinate calculation formula is:

[0034]

[0035]

[0036]

[0037]

[0038] in:

[0039] .

[0040] By adopting the above technical solution, the significant deformation area can be quickly extracted from the InSAR big data and the deformation zone boundary can be automatically extracted, thereby ensuring the overall fast extraction effect and the overall high-precision extraction effect.

[0041] In summary, the beneficial technical effects of the present invention are:

[0042] 1. The Alphashape algorithm is used to calculate the deformation zone boundary, which can efficiently and quickly extract the significant deformation area in the InSAR big data and automatically extract the deformation zone boundary. To a certain extent, it avoids the problems of low efficiency, subjective factors, and difficulty in ensuring accuracy when manually extracting the deformation zone, and achieves the effect of automatically extracting the deformation boundary;

[0043] 2. The Delaunay triangulation network is used to screen the point set so that the overall calculation can be performed through the Alphashape algorithm. It can efficiently and quickly extract the significant deformation area in the InSAR big data, determine the boundary range of the deformation area, and achieve the effect of convenient calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the working process of the present invention;

[0045] Figure 2 This is a schematic diagram of the principle of Alphashape identifying boundary lines of the present invention. DETAILED DESCRIPTION

[0046] The present invention is further described in detail below in conjunction with the accompanying drawings.

[0047] Reference Figure 1 ,An automatic deformation zone boundary extraction method based on Alphashape algorithm, the steps are as follows: S1: selecting the time series SAR images of the target area and generating the PS point set.

[0048] S2: Screening of PS points in the deformation zone: Statistical analysis is performed on the PS points obtained in step S1 to screen out points with abnormal deformation rates, which are marked as point set S. The area where S is located is the deformation zone.

[0049] The PS point generated in step S1 carries the average deformation rate information of the point. The deformation rates of all PS points are statistically analyzed to calculate the mean value of the deformation rate. and standard deviation σ, and select the deformation rate that meets the condition according to the statistical law PS points are marked as point set S, and the area where S is located is the area where the surface subsidence rate is abnormal. The calculation formula for the standard deviation σ is as follows:

[0050]

[0051]

[0052] In the formula For the The deformation rate of each PS point.

[0053] S3: Construct a Delaunay triangulation for the point set S selected in step S2.

[0054] S4: Deformation zone boundary extraction: Based on the Delaunay triangulation, the Alphashape algorithm is used to extract the deformation zone boundary line.

[0055] First, calculate the average point spacing of the point set S, marked as , as the parameters of the Alphashape algorithm, and at the same time, for the Delaunay triangulation constructed in step S3, each edge is extracted to construct an edge set Q.

[0056]

[0057] Choose an edge L from the edge set Q to make a judgment: when the edge length is greater than When , the edge L is not easy to be used as a boundary line, because according to the Alphashape principle, the edge length is greater than When the length of the side of L is less than When the endpoints of L are calculated , And the radius is The coordinates of the center of the circle,. , Two points with a radius of There are two circles with their centers , The coordinate calculation formula is:

[0058]

[0059]

[0060]

[0061]

[0062] in:

[0063]

[0064] Remove from the S point set , After that, calculate the remaining points to , If all points to or The distance is greater than , then L is the boundary line of the point set S. If the remaining points to or The distances are not all greater than , it means that L is not the boundary line of the point set S.

[0065] The embodiments of this specific implementation method are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for automatically extracting deformation zone boundaries based on the Alphashape algorithm, characterized in that: The steps are as follows: S1: Select the time series SAR images of the target area and generate the PS point set; S2: PS point screening in deformation area: Statistical analysis is performed on the PS points obtained in step S1 to screen out points with abnormal deformation rate, which are marked as point set S. The area where S is located is the deformation area, and the mean value of the deformation rate is calculated. and standard deviation σ, and select the deformation rate that meets the condition according to the statistical law The PS points are marked as point set S, and the area where S is located is the area where the surface subsidence rate is abnormal; S3: Construct a Delaunay triangulation network for the point set S selected in step S2; S4: Deformation zone boundary extraction: Based on the Delaunay triangulation, the Alphashape algorithm is used to extract the deformation zone boundary line; A1: For the Delaunay triangulation constructed in step S3, extract each edge to construct an edge set Q; A2: Calculate the average point spacing of the point set S, marked as α, as the parameter of the Alphashape algorithm; A3: Create a new edge set T to store the boundary lines of the deformation area; A4: Select any edge L from the edge set Q to make a judgment: when the edge length is greater than 2*α, the edge L is not easy to be used as a boundary line, because according to the Alphashape principle, when the edge length is greater than 2*α, at least one of the two points constituting L is a discrete point; A5: When the side length of L is less than 2*α, calculate the coordinates of the center of the circle with a radius of α and endpoints P1(x1,y1) and P2(x2,y2) of L; A6: After removing P1 and P2 from the point set S, calculate the distances from the remaining points to P3 and P4 respectively. If the distances from all points to P3 or P4 are greater than α, it means that L is the boundary line of the point set S. Add line segment L to the set T. If the distances from the remaining points to P3 or P4 are not all greater than α, it means that L is not the boundary line of the point set S. A7: Perform steps A1-A6 on all edges in Q, and the edge set in T constitutes the boundary of the deformation zone; A8: Convert the edge set in T into faces to obtain the surface data of the deformation area.

2. The method for automatically extracting deformation zone boundaries based on the Alphashape algorithm according to claim 1, characterized in that: The mean The calculation formula is: The calculation formula of the standard deviation σ is: Where x i is the deformation rate of the i-th PS point.

3. The method for automatically extracting deformation zone boundaries based on the Alphashape algorithm according to claim 1, characterized in that: The calculation formula of the average point spacing is: The center P 3( x3,y 3) , P 4( x4,y 4) The coordinate calculation formula is: in:

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