Unmanned aerial vehicle-mounted InSAR pavement settlement measurement method, system, equipment and medium

Abstract

The invention relates to the technical field of unmanned aerial vehicle pavement settlement measurement, in particular to an unmanned aerial vehicle-mounted InSAR pavement settlement measurement method, system and device and a medium, and the method comprises the following steps: an unmanned aerial vehicle collects pavement image data; image registration is carried out on the road surface image data, and positions of corresponding pixels in the main image and the auxiliary image are accurately paired; a multi-view processing algorithm is called to process the road surface image data; performing differential interference processing on the road surface image data to generate a differential interference pattern; performing coherence estimation and baseline estimation on the differential interferogram; performing horizon correction processing, filtering processing and phase unwrapping on the differential interferogram in sequence; converting the phase into relative deformation of the pavement; and obtaining a pavement deformation result. According to the invention, high-efficiency and reliable pavement deformation measurement is realized by using the advantage of high spatial resolution of earth observation of the InSAR technology and based on high sensitivity of the X-band synthetic aperture radar full-polarization heavy rail interference measurement technology to fine deformation.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) road surface settlement measurement, and in particular to an unmanned aerial vehicle (UAV)-borne InSAR road surface settlement measurement method, system, equipment and medium. Background Art

[0002] Synthetic Aperture Radar (SAR) is an advanced radar imaging technology that can obtain high-resolution ground images in any weather conditions by scanning the target area with radar beams. Interferometric Synthetic Aperture Radar (InSAR) technology further utilizes the phase information of two or more radar images to accurately measure surface deformation and terrain changes, and is widely used in geological disaster monitoring, urban planning, topographic mapping and other fields.

[0003] Traditional InSAR technology usually relies on satellite platforms or fixed ground stations for data collection. Although it can provide high-precision ground information, its flexibility and response speed are limited to a certain extent. In recent years, the development of UAV platforms has provided new possibilities for the application of InSAR technology. UAVs are highly maneuverable and flexible, and can respond quickly after a disaster and obtain high-resolution ground data. X-band (8-12GHz) radar has high spatial resolution and good target detection capabilities due to its shorter wavelength, making it very suitable for high-precision terrain mapping and deformation monitoring. However, X-band UAV-mounted SAR also faces some challenges in practical applications, such as motion error compensation, ultra-high resolution SAR image registration, geocoding, etc. Therefore, it is necessary to combine high-precision navigation systems and advanced data processing algorithms to ensure the accuracy and reliability of the data.

[0004] In summary, UAV-borne InSAR road settlement measurement methods, systems, equipment and media are urgently needed to solve the problems in the existing technology. Summary of the invention

[0005] The present invention aims to provide an unmanned aerial vehicle-mounted InSAR road subsidence measurement method, system, equipment and medium. The specific technical scheme is as follows:

[0006] The UAV-mounted InSAR road settlement measurement method using an UAV-mounted X-band radar includes the following steps:

[0007] Drones collect road image data;

[0008] Perform image registration on the road image data to accurately match the positions of corresponding pixels in the main image and the auxiliary image;

[0009] Calling multi-view processing algorithm to process road image data;

[0010] Perform differential interference processing on road surface image data to generate differential interference graphs;

[0011] Perform coherence estimation and baseline estimation on the differential interferogram;

[0012] The differential interferogram is subjected to horizon correction, filtering and phase unwrapping in sequence;

[0013] Convert the phase into relative deformation of the road surface;

[0014] Obtain road deformation results.

[0015] Optionally, drones can collect road image data, specifically:

[0016] The four polarization channels of H, HV, VH and VV of the X-band SAR sensor onboard the UAV are used to obtain the surface echo signal with ultra-high spatial resolution and obtain the road surface image data.

[0017] Optionally, image registration includes coarse registration and fine registration:

[0018] Rough registration: Select a control point at the center of the main image, and then use the Fourier transform-based correlation coefficient method to calculate the selected control point to obtain the rough registration result of the reference image and the image to be registered;

[0019] Fine registration: multiple control points are evenly selected on the main image, and then the Fourier transform-based correlation coefficient method is used to calculate the coarse registration result for the selected control points and extract the correlation coefficient of the 3×3 neighborhood around the offset as the center. A binary quadratic correlation coefficient surface is constructed for it, and the partial derivative of the obtained binary quadratic correlation coefficient surface is obtained to obtain its extreme value position, which is the final offset of each control point.

[0020] Optionally, the multi-view processing algorithm is specifically: performing N-view averaging processing and M-view averaging processing in the azimuth direction and the slant range direction respectively, and the size of the image after the multi-view processing is 1 / NM of the original image.

[0021] Optionally, the differential interference processing is specifically: reading the parameters and data of the single-view complex image, calculating the Doppler frequency correction parameters; performing Fourier transform: using fast Fourier transform, and obtaining the differential interference pattern by complex multiplication.

[0022] Optionally, the coherence estimation specifically includes: using a coherence formula to calculate a coherence image, where the coherence image is used to represent the coherence degree of different regions;

[0023] The baseline estimation is specifically: using the orbital state vectors of the primary image and the auxiliary image to estimate the baseline, by calculating the interpolation of position and velocity, and using zero point search to optimize the baseline parameters.

[0024] Optionally, the filtering process specifically includes: using a Goldstein filter to filter the differential interference pattern, processing the phase jump caused by noise or other errors, and obtaining accurate phase information.

[0025] In addition, the present invention also discloses a road deformation measurement system, including: an unmanned aerial vehicle platform, an X-band synthetic aperture radar sensor, a high-precision integrated navigation system and X-band data processing software, wherein the X-band data processing software is used to implement the steps of the above-mentioned unmanned aerial vehicle InSAR road surface settlement measurement method.

[0026] In addition, the present invention also discloses a computer device, including a memory and a processor;

[0027] The memory is used to store a computer program executable on the processor;

[0028] The processor is used to implement the steps of the above-mentioned UAV-mounted InSAR road surface settlement measurement method when executing the computer program.

[0029] In addition, the present invention also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned unmanned aerial vehicle-mounted InSAR road surface settlement measurement method are implemented.

[0030] The application of the technical solution of the present invention has the following beneficial effects:

[0031] The present invention makes full use of the high spatial resolution advantage of InSAR technology for earth observation and carries out multiple field flight tests, verifying the high sensitivity of X-band synthetic aperture radar full-polarization re-orbit interferometry measurement technology to subtle deformations. It has the characteristics of high resolution, high flexibility and fast response speed, and provides an efficient and reliable solution for terrain monitoring and deformation monitoring.

[0032] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 is a flowchart of the steps of the UAV-mounted InSAR road surface settlement measurement method in a preferred embodiment of the present invention;

[0035] Figure 2 It is the flight plane and vertical track diagram recorded in the UAV flight experiment in the preferred embodiment of the present invention.

[0036] Figure 3 It is a UAV-mounted full-polarization SAR image and a differential interference fringe pattern in a preferred embodiment of the present invention.

[0037] Figure 4 It is a multi-view intensity map of the UAV-mounted SAR in the preferred embodiment of the present invention.

[0038] Figure 5 1 is a diagram of the coherence and coherence coefficient of the UAV-mounted SAR interferometer pair in the preferred embodiment of the present invention.

[0039] Figure 6 It is a UAV-mounted SAR differential interferogram with the flat ground phase removed in a preferred embodiment of the present invention.

[0040] Figure 7 It is a filtering processing result diagram of the UAV-mounted SAR differential interferogram in the preferred embodiment of the present invention.

[0041] Figure 8 It is a phase unwrapping result diagram of the UAV-mounted SAR differential interferogram in the preferred embodiment of the present invention.

[0042] Fig. 9 It is a real deformation map measured by differential interferometry of UAV-mounted SAR in the preferred embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] like Figure 1As shown, this embodiment discloses a drone-borne InSAR road subsidence measurement method, system, device and medium. The method is specifically a drone-borne InSAR road subsidence measurement method of an X-band drone-borne full-polarization heavy-track interferometric SAR.

[0045] The method of this embodiment includes the following steps:

[0046] S1: UAV collects road image data.

[0047] It should be noted that if Figure 2 The UAV trajectory shown in the figure increases the track accuracy constraint parameters when the UAV is flying a repeated route. spatial The calculation formula, the parameters of the UAV re-orbit flight constraint conditions include: radar side view incident angle θ, the tilt angle α between two re-orbit observations, the spatial baseline B, the distance R from the antenna to the ground target, the wavelength λ of the electromagnetic signal emitted by the SAR sensor, and the SAR signal bandwidth B w , c is the speed of light, spatial incoherence ρ spatial The calculation formula is as follows:

[0048]

[0049] Spatial incoherence ρ spatial Related to the spatial baseline B, when the spatial baseline B value exceeds the critical baseline, ρ spatial When it approaches zero, complete decoherence will occur. Therefore, the track deviation of the drone's two re-orbit flights cannot exceed the critical baseline. The critical baseline calculation formula is as follows:

[0050]

[0051] In this embodiment, the wavelength of the X-band SAR sensor is 31.2mm, the adjustment range of the UAV-borne radar side-view incident angle θ is 20° to 60°, the signal bandwidth is 600MHz, and the flight altitude is calculated as 1km. According to the critical baseline condition requirements of heavy track differential interference, the heavy track space baseline B needs to be less than 18m. Usually, high-precision deformation measurement requires the space baseline to be about one-tenth of the critical baseline, that is, the space baseline needs to be controlled within 2m. An innovative advantage feature of the present invention is that the accuracy of the UAV's repeated tracks is better than 1m, which ensures the accuracy of small deformation measurement values, and the deformation monitoring accuracy can be better than 1mm.

[0052] Furthermore, the drone collects road image data, specifically:

[0053] The four polarization channels of HH, HV, VH and VV of the X-band SAR sensor onboard the UAV are used to obtain the surface echo signal with ultra-high spatial resolution and obtain the road surface image data.

[0054] Among them, for all interference image pairs, the main image M and the auxiliary image S are both K×l matrices, and the pixel information in the main image and the auxiliary image is represented by complex numbers:

[0055]

[0056] M k,l and S k,l Respectively represent the pixel information of the main image and the auxiliary image. These pixel information are complex matrices, representing the complex values ​​of radar echoes. k,l and b k,l Respectively represent the coefficients corresponding to the pixel positions k and l, which are usually scalars representing the amplitude and phase information of the radar signal. VV , M VH , M HV , N HH and S VV , S VH , S HV , S HH is the data of the polarization channel of the main image and the auxiliary image. The generated full polarization SAR image and the differential interference fringes are as follows Figure 3 shown.

[0057] In this embodiment, the advantage of the unmanned aerial vehicle X-band SAR system is the fully polarized interferometric imaging of the four polarization channels HH, HV, VH, and VV. The spacing between the azimuth and range pixels k=1, 2, ..., K; l=1, 2, ..., L is 0.18 meters, which can support fully polarized ultra-high resolution InSAR deformation monitoring.

[0058] S2: Perform image registration on the road surface image data to accurately match the positions of corresponding pixels in the main image and the auxiliary image.

[0059] It should be noted that when performing SAR image registration, offsets are created to resolve the spatial position differences between different images and ensure that they are aligned in the same coordinate system. Specifically, an offset (or offset vector) is a vector that describes the pixel-level displacement between two images. This displacement can be caused by a variety of factors, including slight changes in the platform attitude (such as a slight change in the orbit of an aircraft or satellite), atmospheric disturbances, terrain and objects on the earth's surface, and other factors.

[0060] Furthermore, image registration includes coarse registration and fine registration:

[0061] Rough registration: Select a control point at the center of the main image, and then use the Fourier transform-based correlation coefficient method to calculate the selected control point to obtain the rough registration result of the reference image and the image to be registered;

[0062] Fine registration: multiple control points are evenly selected on the main image, and then the Fourier transform-based correlation coefficient method is used to calculate the coarse registration result for the selected control points and extract the correlation coefficient of the 3×3 neighborhood around the offset as the center. A binary quadratic correlation coefficient surface is constructed for it, and the partial derivative of the obtained binary quadratic correlation coefficient surface is obtained to obtain its extreme value position, which is the final offset of each control point.

[0063] Furthermore, the construction of the offset polynomial is specifically that the positional relationship between the slave image and the main image can be obtained by obtaining the relationship between the offset and the position of the control point, each control point is substituted into the polynomial to obtain the polynomial coefficient through least squares, a mapping relationship between the reference image and the image to be registered is established, and resampling is performed through bilinear interpolation.

[0064] S3: Call the multi-view processing algorithm to process the road image data.

[0065] It should be noted that the process of the multi-look processing algorithm is similar to the average of N independent sampling signals. It can be processed by multi-look averaging of independent SAR intensity images, or by multi-look averaging of the real and imaginary parts of the SAR complex image.

[0066] Furthermore, the multi-view processing algorithm in this embodiment is specifically: N-view averaging processing and M-view averaging processing are performed in the azimuth direction and the slant range direction respectively, and the image size after multi-view processing is 1 / NM of the original image. Multi-view processing is currently the most effective method to suppress speckle noise. After N-view processing, the variance of speckle noise can be reduced. The strength of multi-view processing is as follows: Figure 4 shown.

[0067] Specifically, azimuth multi-view synthesis: In azimuth, adjacent pixels are weighted averaged, which can be expressed by the following formula:

[0068]

[0069] Among them, A(i, j) is the amplitude value of the original image at position i, j; N is the azimuth view number, indicating how many pixels are averaged in the azimuth direction; mli(i, j) represents the pixel value of the image at position i, j after multi-view processing, which is obtained by weighted averaging of N adjacent pixels; k is the index of the pixel in the azimuth direction, ranging from 0 to N-1.

[0070] Slant range multi-view synthesis: In the slant range direction, the pixels in the slant range direction are weighted averaged. The formula is as follows:

[0071]

[0072] Where M is the distance view number, indicating how many pixels are averaged in the distance direction. l is the index of the pixel in the distance direction, ranging from 0 to M-1.

[0073] S4: Perform differential interference processing on the road surface image data to generate a differential interference map.

[0074] It should be noted that the differential interferometry processing is specifically: reading the parameters and data of the single-view complex image, calculating the Doppler frequency correction parameters; performing Fourier transform: using fast Fourier transform and Kaiser window function for bandpass filtering; and obtaining the differential interferogram by complex multiplication.

[0075] The main expressions for differential interferometry processing are as follows:

[0076] Phase difference int: int = M1 × S1 * ; Where M1 represents the main image; S1 * It means to find the conjugate of the auxiliary image. The phase difference is the phase difference of the same area taken at two different times, which is used for topographic measurement and surface deformation monitoring.

[0077] Doppler frequency estimation: dop2 = dopc (p11, rs1); where p11 includes at least the following: airborne platform speed: the radar's flight speed, which will affect the calculation of Doppler frequency shift; flight altitude: the height of the airborne platform relative to the ground, which affects the slant range and field of view; pitch angle and roll angle: the radar's attitude angle, which will affect the geometric relationship observed by the radar; radar transmission parameters: such as the radar's operating frequency, pulse repetition frequency, etc.; rs1 represents the slant range (Slant Range, rs1), which is the straight-line distance from the radar airborne platform to the ground target, that is, the path length of the radar wave propagation. dop2 is the Doppler frequency; dopc is the function for calculating the Doppler frequency.

[0078] Variable update: Among them, α is the variable used to update the time offset; λ is the radar wavelength; lv1*v1 is the dot product of vector lv1 and velocity vector f1; v*focus is the focus velocity, that is, the speed from the drone to the target point.

[0079] Fourier transform: four1(data,nn,isign); data is the input data; nn is the data length; isign is the transformation direction, input 1 indicates positive transformation, and -1 indicates inverse transformation.

[0080] S5: Perform coherence estimation and baseline estimation on the differential interferogram.

[0081] Furthermore, the coherence estimation is specifically: using the coherence formula, a coherence image is calculated, and the coherence image is used to represent the coherence degree of different regions; the value range in the coherence image is between 0 and 1, representing the coherence degree of different regions. The coherence coefficient of the UAV-borne SAR re-track interferometry is above 0.9, such as Figure 5 shown.

[0082] The baseline estimation is to use the orbit state vectors of the primary and secondary images to estimate the baseline, calculate the interpolation of position and velocity, and use zero point search to optimize the baseline parameters. This method does not rely on offset parameters or FFT methods and is suitable for situations with accurate orbit data.

[0083] S6: performing horizon correction processing, filtering processing and phase unwrapping on the differential interferogram in sequence.

[0084] Furthermore, the horizon correction process is also called removing the horizon effect. In interferometric measurement, the interferometric phase contains the horizon phase, which is caused by the reference ellipsoid. In order to accurately perform subsequent analysis and data accuracy, the horizon effect needs to be removed. The horizon correction diagram after removing the horizon effect is shown in the figure below. Figure 6 shown.

[0085] Furthermore, for InSAR interferograms, the fringes have a certain spatial frequency. The magnitude of the fringes frequency depends on factors such as terrain undulation or deformation gradient, baseline length, etc., so that the frequency components of the fringes are not necessarily concentrated in a specific range in the spectrum. Therefore, it is difficult to determine whether to use a low-pass filter, a high-pass filter, or a bandpass (block) filter when filtering the interferometric phase image. This embodiment uses a Goldstein filter to filter it, and processes the phase jump caused by noise or other errors to ensure accurate phase information. The filtering processing result is as follows: Figure 7 shown.

[0086] Furthermore, the phase unwrapping adopts the path tracking method, the minimum norm method or the network flow method. In this embodiment, the phase unwrapping can also adopt other methods, which will not affect the results of the method of this embodiment. Figure 8As shown, the phase unwrapping of this embodiment forms a total of 8 interference pairs, 124010_160136, 124010_163000, 124010_164208, 124010_165698, 124010_170136, 124010_175896, 124010_177400, 124010_196318. It is used as the main image, and the other 8 groups of images are subjected to differential interference. In the 8 groups of images, the road settlement is simulated by changing the number of wooden strips under the settlement plate. The number of wooden blocks on both sides is indicated in brackets: 1 and 2 circles do not change the wooden blocks on both sides (4+4); 3 circles reduce one layer (3+3); 4 circles reduce one layer (2+2); 5 circles reduce one side (2+1); 6 circles reduce the other side (1+1); 7 circles reduce one side (1+0); 8 and 9 circles remove all the wooden boards (0+0).

[0087] S7: Convert the phase into the relative deformation of the road surface; during the propagation of the radar signal, its phase change is proportional to the propagation distance.

[0088] S8: Obtain road surface deformation results.

[0089] In this embodiment, the drone-mounted SAR data processing software can process 0.1-meter ultra-high resolution full-polarization SAR images. The difference between the obtained deformation result and the true value is less than 1 mm. The true deformation map of each point in the obtained image is as follows: Fig. 9 As shown. In this experiment, the deformation in the SAR radar line of sight (LOS) direction of a certain deformation is -2.62cm, and the vertical direction of the settlement plate changes by -3.8cm. Projected to the LOS direction (45°), the change is -2.68cm. The difference between the UAV SAR measurement value and the actual settlement value is 0.6mm. The experimental results show that SAR technology can effectively monitor surface deformation, and the deformation monitoring accuracy can be better than 1mm, which further verifies its effectiveness in practical applications; the experiment confirms the feasibility and accuracy of surface deformation monitoring using single-antenna heavy-track interferometry, and the accuracy can reach millimeter level. This level of accuracy is of great significance to the fields of geological disaster early warning and urban infrastructure monitoring; it proves the feasibility and advantages of the method of this embodiment in practical applications.

[0090] In addition, this embodiment also includes a road deformation measurement system, including: an unmanned aerial vehicle platform, an X-band synthetic aperture radar sensor, a high-precision integrated navigation system and X-band data processing software, and the X-band data processing software is used to implement the steps of the unmanned aerial vehicle InSAR road settlement measurement method as described above.

[0091] In addition, the present embodiment also includes a computer device, including a memory and a processor;

[0092] The memory is used to store a computer program executable on the processor;

[0093] The processor is used to implement the steps of the above-mentioned UAV-mounted InSAR road surface settlement measurement method when executing the computer program.

[0094] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer device.

[0095] The computer device may be a computing device such as a mobile phone, a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may include, but is not limited to, a processor and a memory. For example, the computer device may also include an input / output device, a network access device, a bus, etc.

[0096] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0097] The memory can be used to store the computer program and / or module, and the processor implements the computer program by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0098] Wherein, if the module / unit integrated in the computer device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0099] In addition, this embodiment also discloses a computer device, including a memory and a processor;

[0100] The memory is used to store a computer program executable on the processor;

[0101] The processor is used to implement the steps of the above-mentioned unmanned aerial vehicle-mounted InSAR road surface settlement measurement method when executing the computer program.

[0102] The method of the present invention uses an unmanned aerial vehicle platform to carry an X-band synthetic aperture radar sensor to fly, collect ground data, use a high-precision integrated navigation system IMU to record flight attitude, use imaging data processing software to perform full polarization SAR imaging, and perform data processing registration, multi-view processing, differential interference, baseline estimation, de-horizontal effect, coherence estimation, filtering and phase unwrapping, phase conversion relative deformation on the collected multi-scene SAR images to obtain high-precision surface deformation information. It has the advantages of high spatial resolution and high precision. The full polarization re-track interferometry measurement system strengthens the application capability of InSAR technology in high-precision terrain mapping and deformation monitoring through the application of unmanned aerial vehicle platforms, and provides a more flexible, efficient, economical and real-time monitoring solution than the traditional cross-track system.

[0103] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] 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 measuring road subsidence using InSAR onboard an unmanned aerial vehicle, characterized in that: The steps include: Drones collect road image data; Perform image registration on the road image data to accurately match the positions of corresponding pixels in the main image and the auxiliary image; Calling multi-view processing algorithm to process road image data; Perform differential interference processing on road surface image data to generate differential interference graphs; Perform coherence estimation and baseline estimation on the differential interferogram; The differential interferogram is subjected to horizon correction, filtering and phase unwrapping in sequence; Convert the phase into relative deformation of the road surface; Obtain road deformation results.

2. The unmanned aerial vehicle InSAR road settlement measurement method according to claim 1, characterized in that: The drone collects road image data, specifically: The four polarization channels of H, HV, VH and VV of the X-band SAR sensor onboard the UAV are used to obtain the surface echo signal with ultra-high spatial resolution and obtain the road surface image data.

3. The UAV-mounted InSAR road subsidence measurement method according to claim 2, characterized in that: Image registration includes coarse registration and fine registration: Rough registration: Select a control point at the center of the main image, and then use the Fourier transform-based correlation coefficient method to calculate the selected control point to obtain the rough registration result of the reference image and the image to be registered; Fine registration: multiple control points are evenly selected on the main image, and then the Fourier transform-based correlation coefficient method is used to calculate the coarse registration result for the selected control points and extract the correlation coefficient of the 3×3 neighborhood around the offset as the center. A binary quadratic correlation coefficient surface is constructed for it, and the partial derivative of the obtained binary quadratic correlation coefficient surface is obtained to obtain its extreme value position, which is the final offset of each control point.

4. The UAV-mounted InSAR road subsidence measurement method according to claim 3, characterized in that: The multi-view processing algorithm is specifically: N-view averaging processing and M-view averaging processing are performed in the azimuth direction and the slant range direction respectively, and the size of the image after multi-view processing is 1 / NM of the original image.

5. The unmanned aerial vehicle-mounted InSAR road subsidence measurement method according to claim 4, characterized in that: The differential interference processing is specifically: reading the parameters and data of the single-view complex image, calculating the Doppler frequency correction parameters; performing Fourier transform: using fast Fourier transform, and obtaining the differential interference pattern by complex multiplication.

6. The UAV-mounted InSAR road settlement measurement method according to claim 5, characterized in that: The coherence estimation is specifically: using the coherence formula to calculate a coherence image, wherein the coherence image is used to represent the coherence degree of different regions; The baseline estimation is specifically: using the orbit state vectors of the main image and the auxiliary image to estimate the baseline, by calculating the interpolation of position and velocity, and using zero point search to optimize the baseline parameters.

7. The UAV-mounted InSAR road settlement measurement method according to claim 6, characterized in that: The filtering process is specifically: using a Goldstein filter to filter the differential interference pattern, processing the phase jump caused by noise or other errors, and obtaining accurate phase information.

8. A road deformation measurement system, characterized in that: include: An unmanned aerial vehicle platform, an X-band synthetic aperture radar sensor, a high-precision integrated navigation system and X-band data processing software, wherein the X-band data processing software is used to implement the steps of the unmanned aerial vehicle InSAR road subsidence measurement method as described in any one of claims 1 to 7.

9. A computer device, characterized in that: including memory and processor; The memory is used to store a computer program executable on the processor; The processor is used to implement the steps of the UAV-mounted InSAR road subsidence measurement method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the UAV-mounted InSAR road surface settlement measurement method according to any one of claims 1 to 7 are implemented.

Images