Ground-based synthetic aperture radar geocoding method and related device

Abstract

The present invention discloses a ground-based synthetic aperture radar geocoding method and related device, which relates to the technical field of ground-based synthetic aperture radar data processing. The method includes: acquiring a radar image and a spatial digital terrain; establishing a mapping relationship between a 3D point cloud of the spatial digital terrain and a pixel unit of the radar image using Doppler projection; constructing a posterior probability function model according to the prior monitoring information of the 3D point cloud based on Bayes' theorem; calculating the posterior probability corresponding to each 3D point cloud; and adopting the 3D point cloud whose posterior probability is greater than a predetermined threshold as the spatial representative point of the corresponding pixel unit. The present invention avoids the problem of pixel positions being easily blurred in overlapping areas of radar images due to the blind spots of monitoring shadows and multiple scattering interference in dense and complex spatial terrain data, thereby accurately realizing ground-based synthetic aperture radar geocoding processing from a probabilistic perspective.

Description

[Technical Field]

[0001] The present invention relates to the technical field of ground-based synthetic aperture radar data processing, and in particular to a ground-based synthetic aperture radar geocoding method and related device. [Background technology]

[0002] Ground-based synthetic aperture radar (GB-SAR) is a microwave remote sensing deformation monitoring system with great potential for development. Its advantages, including daytime observation, all-weather observation, and high-precision observation, mean that it has been widely used both domestically and internationally for open-cut mine slope safety monitoring and landslide and geological disaster emergency rescue. In recent years, it has become an important technical tool in the fields of mine slope construction safety management and geological disaster prevention and management. It uses radar active imaging remote sensing technology to repeatedly observe targets within the same area at different times, collecting multiple 2D images containing spatial slope distance and azimuth information between the feature target and the monitoring center, and then uses differential interferometry to obtain high-precision deformation and displacement data of the target.

[0003] Geocoding is one of the core steps in data processing for ground-based synthetic aperture radar. Accurately mapping radar image information with spatial digital terrain data acquired by drones or 3D laser scanners improves the analysis efficiency of radar monitoring data. This is the "digital foundation" for safely monitoring minute slope deformations. However, high-density and complex spatial terrain data includes blind spots and multiple scattering interference caused by monitoring shadows, making pixel positions easily blurred in overlapping areas of radar images. Conventional methods have difficulty accurately identifying spatial information because they do not fully consider pre-monitoring conditions such as slope shape. Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention aims to provide a ground-based synthetic aperture radar geocoding method and related device that can accurately identify spatial information. [Means for solving the problem]

[0005] To achieve the above objectives, the present invention provides the following solutions:

[0006] In a first aspect, the present invention provides a ground-based synthetic aperture radar geocoding method. acquiring radar imagery and spatial digital terrain; Establishing a mapping relationship between the three-dimensional point cloud of the spatial digital terrain and pixel units of the radar image using Doppler projection, wherein each pixel unit in the mapping relationship corresponds to a plurality of three-dimensional point clouds; constructing a posterior probability function model according to the pre-monitoring information of the three-dimensional point cloud based on Bayes' theorem, wherein the pre-monitoring information includes a probability density function of a line of sight angle and a probability density function of a vertical elevation angle of an antenna pattern, and the pre-monitoring information is calculated from the coordinates of the three-dimensional point cloud; calculating a posterior probability that each 3D point cloud in each of the mapping relationships matches a corresponding pixel unit based on the posterior probability function model; and adopting the 3D point cloud whose posterior probability is greater than a preset threshold as a spatial representative point of the corresponding pixel unit.

[0007] In a second aspect, the present invention provides a computer device, comprising: a memory; a processor; and a computer program stored in the memory and executable on the processor, the computer program executing the processor to implement the ground-based synthetic aperture radar geocoding method according to any one of the preceding claims.

[0008] In a third aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, realizes the ground-based synthetic aperture radar geocoding method according to any one of the preceding claims.

[0009] In a fourth aspect, the present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the ground-based synthetic aperture radar geocoding method according to any one of the preceding claims. [Effects of the Invention]

[0010] According to the specific embodiments provided in the present invention, the present invention discloses the following technical effects. The present invention provides a ground-based synthetic aperture radar geocoding method and related apparatus, the method including: acquiring a radar image and a spatial digital terrain; establishing a mapping relationship between a 3D point cloud of the spatial digital terrain and a pixel unit of the radar image using Doppler projection; each pixel unit in the mapping relationship corresponds to a plurality of 3D point clouds; constructing a posterior probability function model according to prior monitoring information of the 3D point clouds based on Bayes' theorem, the prior monitoring information including a probability density function of a line of sight angle and a probability density function of a vertical elevation angle of an antenna pattern, the prior monitoring information being calculated from the coordinates of the 3D point clouds; calculating a posterior probability of each 3D point cloud in the mapping relationship matching a corresponding pixel unit based on the posterior probability function model; and adopting a 3D point cloud whose posterior probability is greater than a predetermined threshold as a spatial representative point of the corresponding pixel unit. The present invention starts from the perspective of probability statistics, combines the parameters of the incidence angle, the probability density function of the line of sight angle, and the probability density function of the vertical elevation angle of the antenna pattern, and establishes a posterior probability function model based on Bayes' theorem to determine the optimal representative point of the pixel unit in the radar image. This directly avoids the problem that the pixel position is easily blurred in the overlapping area of ​​the radar image due to the blind spots of surveillance shadows and multiple scattering interference in the high-density and complex spatial terrain data, and enables the spatial information to be accurately identified. [Brief explanation of the drawings]

[0011] In order to more clearly describe the embodiments of the present invention or the technical solutions of the prior art, the drawings necessary for use in the embodiments are briefly introduced below. Obviously, the drawings described below only show multiple embodiments of the present invention. Those skilled in the art can obtain other drawings based on these drawings without any creative efforts.

[0012] [Figure 1] 1 is a schematic diagram of a ground-based synthetic aperture radar provided by one embodiment of the present invention; [Figure 2]FIG. 1 is a schematic diagram illustrating a flowchart of a ground-based synthetic aperture radar geocoding method provided by an embodiment of the present invention. [Figure 3] FIG. 1 is a schematic diagram of an incidence angle calculation provided by one embodiment of the present invention. [Figure 4] 1 is a schematic diagram of the overall process provided by one embodiment of the present invention; [Figure 5] 1 is a schematic diagram of a radar image provided by one embodiment of the present invention; [Figure 6] 1 is a schematic diagram of a spatial digital terrain provided by an embodiment of the present invention; [Figure 7] 2 is a schematic diagram of a geocoding process result provided by an embodiment of the present invention; [Figure 8] 1 is a schematic diagram illustrating the structure of a computer device provided by an embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0013] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the drawings in the embodiments of the present invention, and obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments, and all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without any creative efforts fall within the protection scope of the present invention.

[0014] In the conventional technology, as shown in Figure 1, the dense and complex spatial terrain data contains the blind spots of surveillance shadows and multiple scattering interference, which makes the pixel positions easily blurred in the overlapping areas of radar images.

[0015] Starting from the perspective of probability statistics, this invention establishes a probability analysis model for geometric parameter monitoring based on Bayes' theorem, determines the optimal representative point for radar pixel spatial geocoding, overcomes the difficulties in integrating heterogeneous slope monitoring data and the deficiencies of single-information display, and realizes 3D visualization and identification of landslide deformation danger areas. This makes it convenient for non-professionals such as mine safety inspectors and emergency rescue personnel to understand and apply ground-based synthetic aperture radar images, and fully plays a supporting role in landslide early warning, which is of great significance for landslide geological disaster monitoring and early warning, mine production safety, disaster prevention and mitigation, etc.

[0016] This invention addresses the need to identify the optimal 3D spatial representative point for a ground-based synthetic aperture radar (SAR) slope range-azimuth pixel unit, and provides a ground-based SAR geocoding method and related device. By using the target's temporal amplitude and phase scattering information, reflection intensity, and slope distance of a sloped feature, the geocoding input data source is optimized and selected, and a joint prior probability model is established that integrates the target's incident angle and antenna pattern to establish the posterior probability of a candidate representative point for the pixel unit. This solves the problem of multiple target positions being blurred in image overlap areas, and provides technical support for the accurate identification of landslide deformation risk areas using ground-based SAR.

[0017] In order to make the above objects, features and advantages of the present invention more clearly and easily understandable, the present invention will be described in more detail below with reference to the drawings and specific embodiments.

[0018] In one exemplary embodiment, a ground-based synthetic aperture radar geocoding method is provided, as shown in Figure 2. The method is executed by a computer device, and specifically, may be executed by a computer device such as a terminal or a server alone, or may be executed by a terminal and a server together. In the embodiment of the present invention, the method is described by taking an example of applying it to a server, and includes the following steps 201 to 208. S1, acquire radar images and spatial digital terrain; In this embodiment, the radar image is a slant range-azimuth two-dimensional image acquired using a ground-based synthetic aperture radar, and the spatial digital terrain is acquired by a drone or a three-dimensional laser scanner. S2. Using Doppler projection, establish a mapping relationship between the 3D point cloud of the spatial digital terrain and the pixel units of the radar image. Each pixel unit in the mapping relationship corresponds to a plurality of 3D point clouds. In this embodiment, specifically, Doppler projection is used to map the 3D point cloud in the spatial digital terrain to the pixel units in the radar image, and obtain the mapping relationship between the 3D point cloud and the pixel units. Each pixel unit in the mapping relationship corresponds to a plurality of 3D point clouds.

[0019] Specifically, S2 includes the following: S21, selecting stable scatterer pixel units in the radar image according to their temporal coherence and amplitude discreteness. In this embodiment, the slant range-azimuth two-dimensional image acquired by the ground-based synthetic aperture radar is represented in a matrix format, and stable scatterer pixel units are selected based on the temporal coherence and amplitude discreteness, where pixel units with temporal coherence of 0.8 or more and amplitude discreteness of 0.15 or less are considered to be stable scatterer pixel units. The slant range-azimuth two-dimensional image of a ground-based synthetic aperture radar is expressed as follows: JPEG0007770000000002.jpg19134JPEG0007770000000003.jpg66170JPEG0007770000000004.jpg19160. JPEG0007770000000005.jpg67170

[0020] S22, representing three-dimensional point cloud targets in the spatial digital terrain in a set format, and selecting three-dimensional point clouds with stable scattering characteristics in the spatial digital terrain according to the reflection intensity of the ground object and the short-range and long-range parameters of radar monitoring; and selecting three-dimensional point clouds with stable scattering characteristics in the spatial digital terrain according to the reflection intensity of the ground object and the short-range and long-range parameters of radar monitoring. JPEG0007770000000006.jpg65170JPEG0007770000000007.jpg80170

[0021] S23, using Doppler projection to map the 3D point cloud with stable scattering characteristics to the stable scatterer pixel units. The ground-based synthetic aperture radar pixel unit is the intersection of the equidistant sphere and the Doppler isocone, and Doppler projection takes into account the Doppler frequency shift caused by radar movement to more accurately represent the geometric relationship between the 3D 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-based synthetic aperture radar using Doppler projection, and candidate point clouds corresponding to the target pixel unit are selected. JPEG0007770000000008.jpg65170JPEG0007770000000009.jpg23170

[0022] S3: Based on Bayes' theorem, construct a posterior probability function model according to the 3D point cloud prior monitoring information, where the prior monitoring information includes a probability density function of the line of sight angle and a probability density function of the vertical elevation angle of the antenna pattern, and the prior monitoring information is calculated from the coordinates of the 3D point cloud. In this embodiment, a probability density function of the line of sight angle and a probability density function of the vertical elevation angle of the antenna pattern are defined. JPEG0007770000000010.jpg69170A small line-of-sight angle means that the radar line of sight and the surface normal vector are nearly parallel, and the reflective structure is locally perpendicular to the radar, making it highly likely to have strong reflectivity. JPEG0007770000000011.jpg30170JPEG0007770000000012.jpg20126Here, the two terms in the sum in the upper right corner of the above equation can be interpreted as the specular and diffuse portions of the signal. a represents the ratio of specular to diffuse components, and b determines the sharpness of the specular peak. JPEG0007770000000013.jpg21170The probability density function of the gaze direction angle is defined as follows: JPEG0007770000000014.jpg27147JPEG0007770000000015.jpg116170JPEG0007770000000016.jpg68170

[0023] Then, based on 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, a joint prior probability function of the line of sight direction angle and the vertical elevation angle of the antenna pattern is established. Finally, the posterior probability of the candidate representative points of the pixel unit can be calculated based on Bayes' theorem. The meaning of the candidate representative points of the pixel unit in this embodiment is explained as follows: In step S2, a mapping relationship between the 3D point cloud and the pixel unit is obtained, and each pixel unit in the mapping relationship corresponds to a plurality of 3D point clouds, and these corresponding plurality of 3D point clouds are the candidate representative points of the pixel unit.

[0024] S4, based on the posterior probability function model, calculate the posterior probability of each 3D point cloud matching the corresponding pixel unit in each of the mapping relationships. Based on Bayes' theorem, the prior monitoring information such as line of sight angle and antenna pattern is used to optimize the 3D coordinate estimation, and the points that cannot effectively reflect the signal due to unreasonable geometric positions are excluded, so that the geocoding result only retains scatterers that can physically reflect the signal, thereby improving the reliability of deformation monitoring, and the posterior probability function model is JPEG0007770000000018.jpg12170. JPEG0007770000000019.jpg59170

[0025] S5, the 3D point cloud whose posterior probability is greater than a preset threshold is adopted as the spatial representative point of the corresponding pixel unit. Here, the 3D point cloud with the posterior probability greater than 80% is adopted as the spatial representative point of the corresponding pixel unit to avoid a single hard assignment. If 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.

[0026] This embodiment provides a ground-based synthetic aperture radar geocoding method and related apparatus, the overall flowchart of which is also shown in Figure 4. To address the issue of pixel-level spatial position blurring in overlapping radar image regions, a Bayesian statistical framework is first introduced into ground-based synthetic aperture radar spatial geocoding. Using a prior model based on the morphological distribution of slope monitoring to improve the accuracy of spatial geocoding is of great practical significance for emergency rescue and ensuring safe mine production. The radar image in this embodiment is shown in Figure 5, the spatial digital terrain in Figure 6, and the final processing result (geocoding processing result) in Figure 7.

[0027] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure is shown in FIG. 8. The computer device includes a processor, a memory, an input / output interface (abbreviated as Input / Output, I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is used to provide calculation and control functions. 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 operating environment for the operating system and the computer program stored in the non-volatile storage medium. The I / O 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 via a network connection. A ground-based synthetic aperture radar geocoding method is realized by executing the computer program on the processor.

[0028] Those skilled in the art will appreciate that the configuration shown in Figure 8 is merely a block diagram of a partial configuration related to the solution of the present invention, and does not limit the computer device to which the solution of the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, and specific components may be combined or the components may be arranged in a different manner.

[0029] In one exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in each of the above method embodiments are realized.

[0030] In one exemplary embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, implements the steps of each of the method embodiments described above.

[0031] In one exemplary embodiment, a computer program product is provided that includes a computer program that, when executed by a processor, performs the steps of each of the method embodiments described above.

[0032] Furthermore, all 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.) related to this invention are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0033] Those skilled in the art will understand that all or part of the processes in the above-described exemplary methods can be implemented by instructing relevant hardware via a computer program. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, can include processes such as those in the above-described exemplary methods. Herein, all references to memory, databases, or other media used in the exemplary embodiments of the present invention may include at least one of non-volatile memory and volatile memory. Non-volatile memory includes read-only memory (ROM), magnetic tape, floppy disks, flash memory, optical storage, 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 includes random access memory (RAM), external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as Static Random Access Memory (SRAM) and Dynamic Random Access Memory (DRAM).

[0034] The databases related to the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a distributed database based on blockchain. The processors related to the embodiments provided herein may include, but are not limited to, a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, etc.

[0035] The technical features of the above embodiments can be combined in any way, and for the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, but as long as there is no contradiction in the combinations of these technical features, all should be considered within the scope described in this specification.

[0036] In this specification, the principles and embodiments of the present invention are described by applying specific examples, and the description of the above examples is only useful for understanding the method of the present invention and its core idea. At the same time, those skilled in the art may change the specific embodiments and application scope according to the idea of ​​the present invention. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. 1. A ground-based synthetic aperture radar geocoding method, comprising: acquiring radar imagery and spatial digital terrain; Establishing a mapping relationship between the three-dimensional point cloud of the spatial digital terrain and pixel units of the radar image using Doppler projection, wherein each pixel unit in the mapping relationship corresponds to a plurality of three-dimensional point clouds; constructing a posterior probability function model according to the pre-monitoring information of the three-dimensional point cloud based on Bayes' theorem, wherein the pre-monitoring information includes a probability density function of a line of sight angle and a probability density function of a vertical elevation angle of an antenna pattern, and the pre-monitoring information is calculated from the coordinates of the three-dimensional point cloud; calculating a posterior probability that each of the three-dimensional point clouds in each of the mapping relationships matches a corresponding pixel unit based on the posterior probability function model; adopting the three-dimensional point cloud having the posterior probability greater than a predetermined threshold as a spatial representative point of the corresponding pixel unit; Including, The posterior probability function model is Establishing a mapping relationship between the three-dimensional point cloud of the spatial digital terrain and the pixel unit of the radar image using the Doppler projection specifically includes: selecting stable scatterer pixel units within said radar image according to their temporal coherence and amplitude discreteness; selecting a 3D point cloud with stable scattering characteristics in the spatial digital terrain according to the reflectivity of the ground object and the near-distance and long-distance parameters of the radar monitoring; and mapping the 3D point cloud with stable scattering characteristics to the stable scatterer pixel units using Doppler projection, i.e.

2. Specifically, adopting the three-dimensional point cloud having the posterior probability greater than a predetermined threshold as the spatial representative point of the corresponding pixel unit includes the following steps: adopting the three-dimensional point cloud having a posterior probability greater than 80% as a spatial representative point of the corresponding pixel unit; 2. The ground-based synthetic aperture radar geocoding method of claim 1, further comprising: if the number of spatial representative points of the same pixel unit is greater than 1, taking the average value of all spatial representative points of the pixel unit as the spatial representative point of the corresponding pixel unit.

3. Selecting stable scatterer pixel units in the radar image according to the temporal coherence and amplitude discreteness specifically comprises:

2. The ground-based synthetic aperture radar geocoding method of claim 1, further comprising: selecting pixel units in the radar image having a temporal coherence of 0.8 or more and an amplitude discreteness of 0.15 or less to obtain stable scatterer pixel units.

4.

5. the radar image is a slant range-azimuth two-dimensional image acquired using a ground-based synthetic aperture radar; The ground-based synthetic aperture radar geocoding method of claim 1 , wherein the spatial digital terrain is acquired by a drone or a 3D laser scanner.

6. 6. 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-based synthetic aperture radar geocoding method according to any one of claims 1 to 5.

7. 6. A computer-readable storage medium having a computer program stored thereon, the computer program being characterized in that, when executed by a processor, the computer program implements the ground-based synthetic aperture radar geocoding method according to any one of claims 1 to 5.

8. 6. A computer program product comprising a computer program, the computer program being characterized in that, when executed by a processor, the computer program product implements the ground-based synthetic aperture radar geocoding method according to any one of claims 1 to 5.

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