Foundation deformation monitoring radar geocoding method and related equipment

Abstract

The invention discloses a ground deformation monitoring radar geocoding method and related equipment, and relates to the technical field of ground deformation monitoring radar data processing, and the method comprises the steps: obtaining a radar image and a spatial digital terrain; establishing a mapping relation between a three-dimensional point cloud in the spatial digital terrain and a pixel unit in a radar image by using Doppler projection; based on the Bayesian theorem, constructing a posterior probability function model according to the prior monitoring information of the three-dimensional point cloud; calculating a posterior probability corresponding to each three-dimensional point cloud; and taking the three-dimensional point clouds of which the posterior probabilities are greater than a preset threshold as spatial representative points of the corresponding pixel units. The problem that high-density complex space topographic data comprises monitoring shadow blind areas and multipath scattering interference, and consequently radar image overlay area pixel positioning is fuzzy is solved, and foundation deformation monitoring radar geocoding processing is accurately achieved from the probabilistic perspective.

Description

Technical Field

[0001] The present application relates to the technical field of ground deformation monitoring radar data processing, and in particular to a ground deformation monitoring radar geocoding method and related equipment. Background Art

[0002] Ground-based Synthetic Aperture Radar (GB-SAR) is a microwave remote sensing deformation monitoring system with great development potential. It is widely favored in open-pit mine slope safety monitoring and landslide geological disaster emergency rescue at home and abroad due to its advantages of all-day, all-weather and high precision. In recent years, it has become a key technical equipment in the field of mine slope engineering safety and geological disaster prevention. It uses radar active imaging remote sensing technology to repeatedly observe targets in the same area at different times, collect multiple two-dimensional images containing spatial slant range and azimuth information between ground objects and monitoring centers, and obtain high-precision deformation displacement data of targets through differential interferometry technology.

[0003] Geocoding is one of the core steps in the processing of ground deformation monitoring radar data. It improves the efficiency of radar monitoring data analysis by accurately mapping radar image information with spatial digital terrain obtained by drones and three-dimensional laser scanners. It is the "digital foundation" for slope micro-deformation safety monitoring. However, high-density and complex spatial terrain data contain monitoring shadow blind areas and multipath scattering interference, which can easily lead to pixel ambiguity in the overlapping areas of radar images. Traditional methods do not fully consider the prior monitoring conditions of slope morphology, making it difficult to accurately locate spatial information. Summary of the invention

[0004] The purpose of this application is to provide a ground deformation monitoring radar geocoding method and related equipment, which can accurately locate spatial information.

[0005] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a ground deformation monitoring radar geocoding method, comprising: Acquire radar images and spatial digital terrain.

[0006] Doppler projection is used to establish a mapping relationship between a three-dimensional point cloud in a spatial digital terrain and a pixel unit in a radar image; each pixel unit in the mapping relationship corresponds to a number of three-dimensional point clouds.

[0007] Based on Bayes' theorem, a posterior probability function model is constructed according to the prior monitoring information of the three-dimensional point cloud; the prior monitoring information includes: the probability density function of the line of sight direction angle and the probability density function of the vertical elevation angle of the antenna pattern; the prior monitoring information is calculated by the coordinates of the three-dimensional point cloud.

[0008] According to the posterior probability function model, the posterior probability of each three-dimensional point cloud in each of the mapping relationships matching the corresponding pixel unit is calculated.

[0009] The three-dimensional point cloud whose posterior probability is greater than a preset threshold is used as the spatial representative point of the corresponding pixel unit.

[0010] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described ground deformation monitoring radar geocoding methods.

[0011] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described ground deformation monitoring radar geocoding methods.

[0012] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned ground deformation monitoring radar geocoding methods.

[0013] According to the specific embodiments provided in this application, this application discloses the following technical effects: The present application provides a ground deformation monitoring radar geocoding method and related equipment, the method comprising: acquiring a radar image and a spatial digital terrain; establishing a mapping relationship between a three-dimensional point cloud in the spatial digital terrain and a pixel unit in the radar image by using Doppler projection; each pixel unit in the mapping relationship corresponds to a number of three-dimensional point clouds; based on Bayes' theorem, constructing a posterior probability function model according to prior monitoring information of the three-dimensional point cloud; the prior monitoring information comprises: a probability density function of a line of sight direction angle and a probability density function of a vertical elevation angle of an antenna pattern; the prior monitoring information is calculated by the coordinates of the three-dimensional point cloud; according to the posterior probability function model, calculating the posterior probability that each three-dimensional point cloud in each of the mapping relationships matches the corresponding pixel unit; and taking the three-dimensional point cloud whose posterior probability is greater than a preset threshold as the spatial representative point of the corresponding pixel unit. From the perspective of probability statistics, this application combines the parameters of the probability density function of the incident angle, the line of sight angle, and the probability density function of the vertical elevation angle of the antenna pattern to establish a posterior probability function model based on the Bayesian theorem to determine the optimal representative point of the pixel unit in the radar image, thereby directly avoiding the problem of pixel positioning ambiguity in the overlapping area of ​​the radar image caused by high-density and complex spatial terrain data including monitoring shadow blind spots and multipath scattering interference, thereby accurately locating spatial information. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic diagram of a ground deformation monitoring radar provided in one embodiment of the present application.

[0016] Figure 2 A schematic flow chart of a ground deformation monitoring radar geocoding method provided in one embodiment of the present application.

[0017] Figure 3 A schematic diagram of incident angle calculation provided in an embodiment of the present application.

[0018] Figure 4 A schematic diagram of the overall process provided for one embodiment of the present application.

[0019] Figure 5 A schematic diagram of a radar image provided by an embodiment of the present application.

[0020] Figure 6 A schematic diagram of spatial digital terrain provided in one embodiment of the present application.

[0021] Figure 7 A schematic diagram of geocoding processing results provided in one embodiment of the present application.

[0022] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0024] In related technologies, such as Figure 1 As shown in the figure, high-density complex spatial terrain data contains monitoring shadow blind areas and multipath scattering interference, which can easily lead to pixel positioning ambiguity in the overlap area of ​​the radar image.

[0025] From the perspective of probability statistics, this application establishes a monitoring geometric parameter probability analysis model based on the Bayesian theorem, determines the optimal representative point of the radar pixel spatial geocoding, overcomes the difficulty of fusing heterogeneous slope monitoring data and the defects of single information display, and realizes three-dimensional visualization identification of landslide deformation hazard areas. It is convenient for non-professionals such as mine safety inspectors and emergency rescue team members to understand and apply foundation deformation monitoring radar images, and gives full play to the auxiliary role of foundation deformation monitoring radar in landslide disaster warning. It is of great significance in landslide geological disaster monitoring and early warning, mine safety production, disaster prevention and mitigation, etc.

[0026] This application proposes a geocoding method and related equipment for ground deformation monitoring radar to meet the needs of optimal three-dimensional spatial representative point identification of slant range-azimuth pixel units. The geocoding input data source is optimized and screened by using the amplitude and phase scattering information, reflection intensity, and slant range of the slope object time series, and a joint prior probability model integrating the target incident angle and antenna pattern is established to establish the posterior probability of the candidate representative point of the pixel unit, solve the ambiguity problem of multi-target positioning in the image overlap area, and provide technical support for the accurate identification of landslide deformation risk areas of the ground deformation monitoring radar.

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] In an exemplary embodiment, Figure 2 As shown, a ground deformation monitoring radar geocoding method is provided, which is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation, including the following steps 201 to 208. Among them: S1. Acquire radar images and spatial digital terrain.

[0029] In this embodiment, the radar image is a slant range-azimuth two-dimensional image obtained by using a ground deformation monitoring radar; the spatial digital terrain is obtained by an unmanned aerial vehicle or a three-dimensional laser scanner.

[0030] S2. Use Doppler projection to establish a mapping relationship between the three-dimensional point cloud in the spatial digital terrain and the pixel unit in the radar image; each pixel unit in the mapping relationship corresponds to a number of three-dimensional point clouds. In this embodiment, Doppler projection can be used to map the three-dimensional point cloud in the spatial digital terrain to the pixel unit in the radar image to obtain a mapping relationship between the three-dimensional point cloud and the pixel unit; each pixel unit in the mapping relationship corresponds to a number of three-dimensional point clouds.

[0031] Specifically, S2 includes: S21. Select stable scatterer pixel units in the radar image according to temporal coherence and amplitude dispersion.

[0032] In this embodiment, the slant range-azimuth two-dimensional image obtained by the ground deformation monitoring radar is represented in a matrix form, and stable scatterer pixel units are selected according to the temporal coherence and amplitude dispersion. Among them, the pixel units with a temporal coherence of more than 0.8 and an amplitude dispersion of less than 0.15 are considered to be stable scatterer pixel units.

[0033] The slant range-azimuth two-dimensional image of ground deformation monitoring radar can be expressed as: ; in, is the radar azimuth resolution, is the radar range resolution, is the slant range-azimuth two-dimensional image, Represents the center coordinates of the pixel unit, is the initial resolution unit in azimuth, is the initial resolution unit in the range direction. In the ground deformation monitoring radar image, based on the temporal coherence , amplitude dispersion Select a stable scatterer pixel unit, that is: ; in, is the pixel unit at the mth row and nth column, is the coherence threshold, is the amplitude dispersion threshold, is the coherence of the pixel unit in the mth row and nth column, is the amplitude dispersion of the pixel unit in the mth row and nth column, M is the total number of pixel unit rows, and N is the total number of pixel unit columns.

[0034] S22, representing the three-dimensional point cloud target in the spatial digital terrain in a collection form, and selecting the three-dimensional point cloud with stable scattering characteristics in the spatial digital terrain according to the reflection intensity of the ground object and the near and far distance parameters of the radar monitoring.

[0035] In spatial digital terrain, the reflection intensity of the ground objects is , radar monitoring near and far distance , The parameters are initially selected as rock or other points with stable scattering characteristics within the radar monitoring area, namely: ; in, P represents a 3D point cloud with stable scattering properties, is the kth 3D point cloud target, K is the total number of 3D point clouds; is the reflection intensity of the 3D point cloud, is the reflection intensity threshold, R min For radar monitoring of short-range parameters, R max For radar monitoring of long-range parameters, is the slope distance of the point cloud target.

[0036] S23, mapping the three-dimensional point cloud with stable scattering characteristics onto the stable scatterer pixel unit by using Doppler projection.

[0037] The pixel unit of the ground deformation monitoring radar is the intersection of an equidistant sphere and a Doppler isocone. The Doppler projection takes into account the Doppler frequency shift caused by radar motion and more accurately describes the geometric relationship between the three-dimensional point cloud target and the resolution unit. The high-resolution spatial digital terrain point cloud target is mapped to the local coordinate system of the ground deformation monitoring radar through Doppler projection, and the candidate point cloud that falls into the target pixel unit is screened. That is: ; ; At this point, the spatial digital terrain set corresponding to the radar pixel unit can be established, that is, ; in, The pixel unit of the radar image The divided three-dimensional point cloud set is the three-dimensional point cloud set after mapping.

[0038] S3. Based on Bayes' theorem, a posterior probability function model is constructed according to the prior monitoring information of the three-dimensional point cloud; the prior monitoring information includes: the probability density function of the line of sight direction angle and the probability density function of the vertical elevation angle of the antenna pattern; the prior monitoring information is calculated by the coordinates of the three-dimensional point cloud.

[0039] In this embodiment, a probability density function of the sight direction angle and a probability density function of the vertical elevation angle of the antenna pattern are defined.

[0040] like Figure 3 As shown, the sight direction angle The normal vector and the Line of sight vector The scalar product calculation is: .

[0041] A small angle between the line of sight and the surface normal means that the line of sight of the radar is almost parallel to the surface normal vector, and the reflection structure is locally orthogonal to the radar, so the probability of having a strong reflectivity is high. Local normalized radar cross section of the monitoring unit of the ground deformation monitoring radar Angle with sight direction The relationship between can be expressed as: ; Among them, the two terms in the sum of the upper right terms in the above equation can be interpreted as the specular part and the diffuse reflection part of the signal. a Indicates the ratio of specular reflection to diffuse reflection, and b determines the sharpness of the specular reflection peak. a The reasonable values ​​of and b are and Generally, choose a =10 and b=0.2.

[0042] The probability density function of the sight direction angle can be defined as: ; in is the L1 norm.

[0043] is the vertical elevation angle of the antenna pattern, and its probability density function can be defined as: ; ; in, Point cloud target The relative elevation angle, Point cloud target An estimate of the relative elevation angle probability density function, Point cloud target Relative elevation angle probability density function, For the abbreviation .

[0044] Then, based on the above-mentioned probability density function of the sight direction angle and the probability density function of the vertical elevation angle of the antenna pattern, a joint prior probability function of the sight direction angle and the vertical elevation angle of the antenna pattern is established.

[0045] is the sight direction angle of the pixel resolution unit corresponding to the 3D point cloud Elevation angle perpendicular to the antenna pattern The joint prior probability function of in, is the relative elevation angle probability density function, is the probability density function of the sight direction angle.

[0046] Finally, based on Bayes' theorem, the posterior probability of the candidate representative point of the pixel unit can be calculated. In this embodiment, the meaning of the candidate representative point of the pixel unit is explained as follows: the mapping relationship between the three-dimensional point cloud and the pixel unit is obtained in the above step S2; each pixel unit in the mapping relationship corresponds to a number of three-dimensional point clouds, and these corresponding three-dimensional point clouds are the candidate representative points of the pixel unit.

[0047] S4. Calculate the posterior probability of each three-dimensional point cloud in each of the mapping relationships matching the corresponding pixel unit according to the posterior probability function model.

[0048] Based on Bayesian theorem, the three-dimensional coordinate estimation is optimized by using prior monitoring information such as line of sight angle and antenna pattern, and those points that cannot effectively reflect signals due to unreasonable geometric positions are excluded, ensuring that the geocoding results only retain scatterers that may physically reflect signals, thereby improving the reliability of deformation monitoring. The posterior probability function model is: ; in, Representing 3D point clouds The posterior probability of Indicates the center coordinates of the pixel unit The likelihood function of ; Represents the line of sight angle of the 3D point cloud and the vertical elevation angle of the antenna pattern The joint prior probability function of .

[0049] S5. Taking the three-dimensional point cloud whose posterior probability is greater than a preset threshold as the spatial representative point of the corresponding pixel unit.

[0050] The three-dimensional point cloud with a posterior probability greater than 80% is used as the spatial representative point of the corresponding pixel unit to avoid a single rigid allocation. When the number of spatial representative points of the same pixel unit is greater than 1, the average value of all spatial representative points of the pixel unit is taken as the spatial representative point of the corresponding pixel unit.

[0051] This embodiment provides a ground deformation monitoring radar geocoding method and related equipment, and its overall flow chart can also be shown as follows: Figure 4 As shown in FIG. 1 , in order to solve the problem of fuzzy spatial positioning of pixel units in the overlapping area of ​​radar images, the Bayesian statistical framework is introduced into the spatial geocoding of ground deformation monitoring radar for the first time. The accuracy of spatial geocoding is improved by a priori model based on the distribution of slope monitoring morphology, which has important practical significance for ensuring emergency rescue and mine safety production. The radar image in this embodiment is shown in FIG. Figure 5 As shown, the spatial digital terrain is Figure 6 As shown in the figure, the final processing result (geocoding processing result) is as follows Figure 7 shown.

[0052] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a ground deformation monitoring radar geocoding method is implemented.

[0053] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0054] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0055] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0056] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0057] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0058] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0059] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0060] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A ground deformation monitoring radar geocoding method, characterized in that: include: Acquire radar images and digital terrain from space; The mapping relationship between the three-dimensional point cloud in the spatial digital terrain and the pixel unit in the radar image is established by using Doppler projection; Each pixel unit in the mapping relationship corresponds to a number of three-dimensional point clouds; Based on Bayesian theorem, a posterior probability function model is constructed according to the prior monitoring information of the three-dimensional point cloud; the prior monitoring information includes: the probability density function of the sight direction angle and the probability density function of the vertical elevation angle of the antenna pattern; the prior monitoring information is calculated by the coordinates of the three-dimensional point cloud; Calculating the posterior probability of each three-dimensional point cloud in each of the mapping relationships matching the corresponding pixel unit according to the posterior probability function model; The three-dimensional point cloud whose posterior probability is greater than a preset threshold is used as the spatial representative point of the corresponding pixel unit.

2. The ground deformation monitoring radar geocoding method according to claim 1 is characterized in that: The posterior probability function model is: ； in, Representing 3D point clouds The posterior probability of Indicates the center coordinates of the pixel unit The likelihood function of ; Represents the sight direction angle of the 3D point cloud Elevation angle perpendicular to the antenna pattern The joint prior probability function of .

3. The ground deformation monitoring radar geocoding method according to claim 1, characterized in that: Taking the three-dimensional point cloud whose posterior probability is greater than a preset threshold as the spatial representative point of the corresponding pixel unit specifically includes: The three-dimensional point cloud with a posterior probability greater than 80% is used as a spatial representative point of the corresponding pixel unit; When the number of spatial representative points of a same pixel unit is greater than one, the average value of all spatial representative points of the pixel unit is taken as the spatial representative point of the corresponding pixel unit.

4. The ground deformation monitoring radar geocoding method according to claim 1, characterized in that: The use of Doppler projection to establish a mapping relationship between a three-dimensional point cloud in a spatial digital terrain and a pixel unit in a radar image specifically includes: Selecting stable scatterer pixel units in the radar image according to temporal coherence and amplitude dispersion; Selecting a three-dimensional point cloud with stable scattering characteristics in the spatial digital terrain according to the reflection intensity of the ground object and the near- and far-range parameters of radar monitoring; The three-dimensional point cloud with stable scattering characteristics is mapped onto the stable scatterer pixel unit by using Doppler projection.

5. The ground deformation monitoring radar geocoding method according to claim 4 is characterized in that: The step of selecting stable scatterer pixel units in the radar image according to the temporal coherence and the amplitude dispersion specifically includes: Pixel units with a temporal coherence greater than 0.8 and an amplitude dispersion less than 0.15 in the radar image are selected to obtain stable scatterer pixel units.

6. The ground deformation monitoring radar geocoding method according to claim 4, characterized in that: The formula for selecting the three-dimensional point cloud with stable scattering characteristics in the spatial digital terrain according to the reflection intensity of the ground object and the near- and long-range parameters of radar monitoring is: ； in, P represents a 3D point cloud with stable scattering properties, is the kth 3D point cloud target, K is the total number of 3D point clouds; is the reflection intensity of the 3D point cloud, is the reflection intensity threshold, R min For radar monitoring of short-range parameters, R max For radar monitoring of long-range parameters, is the slope distance of the point cloud target.

7. The ground deformation monitoring radar geocoding method according to claim 1, characterized in that: The radar image is a slant range-azimuth two-dimensional image obtained by using a ground deformation monitoring radar; The spatial digital terrain is acquired by an unmanned aerial vehicle or a three-dimensional laser scanner.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the ground deformation monitoring radar geocoding method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the ground deformation monitoring radar geocoding method described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the ground deformation monitoring radar geocoding method described in any one of claims 1 to 7 is implemented.

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