High-efficiency super-resolution imaging method and device for tomographic synthetic aperture radar

Abstract

This invention belongs to the field of radar image processing technology and provides an efficient super-resolution imaging method and apparatus for tomographic synthetic aperture radar. The method includes: acquiring a multi-baseline tomographic SAR dataset based on multiple SAR images; performing nonlocal filtering estimation on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters; constructing a tomographic SAR imaging model based on the nonlocal filtering parameters and a relaxation algorithm; and using the tomographic SAR imaging model to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain super-resolution imaging results. The method described in this embodiment improves the super-resolution imaging capability and data processing efficiency of nonlocal tomographic SAR.

Description

Technical Field

[0001] This invention relates to the field of radar image processing technology, and in particular to a highly efficient super-resolution imaging method and apparatus for tomographic synthetic aperture radar. Background Technology

[0002] Synthetic Aperture Radar (SAR) technology can utilize multi-baseline SAR data to achieve 3D reconstruction of targets, and has been well applied to 3D reconstruction of cities, forests, and other areas. However, the high-dimensional imaging resolution of SAR technology is limited by the baseline span. The baseline of multi-baseline SAR data acquired by spaceborne SAR systems during re-orbiting is limited, resulting in a high-dimensional resolution range of tens of meters. This high-dimensional resolution is significantly lower than the range and azimuth resolution of traditional SAR.

[0003] In related technologies, tomographic SAR super-resolution imaging algorithms mainly include spectral estimation (Capon, MUSIC, etc.) algorithms and compressed sensing (CS) algorithms. These algorithms all require a large amount of SAR data to achieve reliable tomographic inversion. In order to reduce the amount of data and solve the problem that the spatial resolution of SAR images will be reduced when the method of combining local multi-look and tomographic SAR is used to achieve super-resolution imaging, existing technologies also introduce nonlocal filtering (NLF) algorithms into tomographic SAR imaging. Specifically, nonlocal filtering algorithms are combined with spectral estimation algorithms and compressed sensing algorithms to achieve SAR super-resolution imaging. However, the former has weak super-resolution capability and the latter has high computational complexity. It is difficult to balance super-resolution capability and computational load when achieving SAR super-resolution imaging, and it is not suitable for large-scale scene 3D reconstruction. Summary of the Invention

[0004] This invention provides an efficient super-resolution imaging method and apparatus for tomographic synthetic aperture radar, which addresses the shortcomings of existing technologies that combine nonlocal filtering algorithms with spectral estimation algorithms and compressed sensing algorithms to achieve SAR super-resolution imaging, making it difficult to balance super-resolution capability and computational load. The method described in this invention improves the super-resolution imaging capability and data processing efficiency of nonlocal tomographic SAR.

[0005] This invention provides an efficient super-resolution imaging method for tomographic synthetic aperture radar, comprising:

[0006] A multi-baseline tomography SAR dataset is obtained from multiple synthetic aperture radar SAR images; nonlocal filtering estimation is performed on the interferometric parameters of the multiple SAR images to obtain the nonlocal filtering parameters;

[0007] A tomographic SAR imaging model is constructed based on the nonlocal filtering parameters and relaxation algorithm, and the tomographic SAR imaging model is used to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain super-resolution imaging results.

[0008] According to the present invention, an efficient tomographic synthetic aperture radar super-resolution imaging method is provided, wherein obtaining a multi-baseline tomographic SAR dataset from multiple synthetic aperture radar SAR images includes:

[0009] The multi-baseline tomography SAR dataset is obtained by performing image registration, deskewing, and phase error compensation on multiple SAR images.

[0010] According to the present invention, an efficient super-resolution imaging method for tomographic synthetic aperture radar is provided, wherein the tomographic SAR imaging model is expressed by the following equation:

[0011] ;

[0012] in, , The first The scattering coefficient and height of each scattering point K This represents the number of scattering points contained in a single pixel. , These are nonlocal interference parameters. It is a nonlinear interference; Denotes the Euclidean norm; This represents the scattering coefficient vector along the tomography direction; For the guiding matrix, M The number of scattering coefficient vectors is denoted by T, which represents the transpose.

[0013] According to the present invention, an efficient super-resolution imaging method for tomographic synthetic aperture radar is provided, wherein the super-resolution imaging result includes height information of multiple scattering points, and after obtaining the super-resolution imaging result, the method further includes:

[0014] The height information of the multiple scattering points is used to reconstruct the scene point cloud in three dimensions.

[0015] According to the present invention, an efficient super-resolution imaging method for tomographic synthetic aperture radar is provided, wherein after obtaining the reconstructed scene point cloud, the method further includes:

[0016] Based on the reconstructed scene point cloud computing target indicators, wherein the target indicators include resolution, estimation accuracy, and processing time;

[0017] The imaging performance of the reconstructed scene point cloud is evaluated based on the target metrics.

[0018] The present invention also provides a high-efficiency tomographic synthetic aperture radar super-resolution imaging device, comprising:

[0019] The data acquisition module is used to acquire a multi-baseline tomographic SAR dataset based on multiple synthetic aperture radar SAR images; and to perform nonlocal filtering estimation on the interferometric parameters of multiple SAR images to obtain nonlocal filtering parameters.

[0020] The imaging module is used to construct a tomographic SAR imaging model based on the nonlocal filtering parameters and relaxation algorithm, and to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset using the tomographic SAR imaging model to obtain super-resolution imaging results.

[0021] According to the present invention, a high-efficiency tomographic synthetic aperture radar super-resolution imaging device is provided, wherein the super-resolution imaging result includes height information of multiple scattering points, and the device further includes:

[0022] The reconstruction module is used to perform three-dimensional reconstruction of the height information of the multiple scattering points after obtaining the super-resolution imaging results, so as to obtain the reconstructed scene point cloud.

[0023] The present invention also provides an electronic device, including 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 efficient tomographic synthetic aperture radar super-resolution imaging method as described above.

[0024] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the efficient tomographic synthetic aperture radar super-resolution imaging method as described above.

[0025] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the efficient tomographic synthetic aperture radar super-resolution imaging method as described above.

[0026] The present invention provides an efficient super-resolution imaging method and apparatus for tomographic synthetic aperture radar, which acquires a multi-baseline tomographic SAR dataset by means of multiple SAR images; nonlocal filtering is performed on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters; a tomographic SAR imaging model is then constructed based on the nonlocal filtering parameters and a relaxation algorithm; and the tomographic SAR imaging model is used to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain super-resolution imaging results, thereby improving the super-resolution imaging capability and data processing efficiency of nonlocal tomographic SAR. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0028] Figure 1 This is one of the flowcharts of the high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention.

[0029] Figure 2 This is the second flowchart of the high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention.

[0030] Figure 3 This is a schematic diagram of the structure of the high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided by the present invention.

[0031] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0033] The following is combined with Figures 1-4 This invention describes a highly efficient tomographic synthetic aperture radar super-resolution imaging method and apparatus.

[0034] Figure 1 This is one of the flowcharts illustrating the efficient tomographic synthetic aperture radar super-resolution imaging method provided by this invention, such as... Figure 1 As shown, the method includes the following steps:

[0035] Step 110: Obtain a multi-baseline tomography SAR dataset based on multiple synthetic aperture radar SAR images; perform nonlocal filtering estimation on the interferometric parameters of the multiple SAR images to obtain the nonlocal filtering parameters.

[0036] In this step, multiple SAR images can be preprocessed (such as image registration, deskewing, and phase error compensation) to eliminate observation errors and ensure data consistency and quality.

[0037] In this step, the process of acquiring a multi-baseline tomography SAR dataset from multiple synthetic aperture radar SAR images is essentially mapping the observation data to an elevation scattering coefficient distribution.

[0038] Specifically, this embodiment ensures spatial alignment of SAR images with different baselines and eliminates phase distortion and phase tilt. The preprocessed SAR images meet the geometric consistency requirements of tomographic imaging, forming a multi-baseline tomographic SAR dataset that can be used for three-dimensional reconstruction.

[0039] In this embodiment, the nonlocal filtering method is used to estimate the interferometric parameters of the tomographic SAR image through the following steps:

[0040] (1) Assumption Indicates coordinates as The nonlocal filtering estimation of the interferometric parameters of the pixels, where For scattering intensity, For interference phase, For coherence, this embodiment will utilize nonlocal filtering techniques. Estimation is performed, and nonlocal filtering techniques utilize a weighted maximum likelihood estimator for... The estimation process is represented by the following formula:

[0041] ;

[0042] in, For interference parameters, For a given At that time, the observed data Log-likelihood function; weighting coefficients and Depending on the statistical characteristics of the data, nonlocal filtering techniques use the statistical characteristics of interferometric SAR for calculation; After nonlocal filtering estimation ; These are pixel coordinates.

[0043] (2) Assumption and To complete the registration of the two SAR images, If the phase is the noise interference phase, then:

[0044] ;

[0045] in, Let be the likelihood function. R The statistical expectation of the total power (or scattering intensity) of the scatterer in the SAR image; weighting coefficients. It utilizes pixel blocks (in) The similarity of all pixels within the centered matching window is estimated; this embodiment utilizes the scale-invariant similarity criterion to... The likelihood function is expressed as:

[0046] ;

[0047] in, These are the filter parameters. This is the coordinate offset. The coordinates are The interference parameters of the pixels; The coordinates are Interference parameters of pixels; The coordinates are Observational data, The coordinates are Observational data; p This is a probability value. This is a cumulative multiplication operation. Through... Maximum likelihood estimation is performed on the likelihood function, and the estimated parameters are expressed as follows:

[0048] ;

[0049] ;

[0050] ;

[0051] in, The coordinates are The scattering intensity of the pixels, for The estimated value; The coordinates are The interference phase of the pixels, for The estimated value; The coordinates are The coherence of pixels, for The estimated value; For the first SAR image in Observational data at the location, For the second SAR image in Observational data at the location, for The complex conjugate value; | is the modulo operation; in summary, we can obtain Nonlocal filtering estimation.

[0052] Step 120: Construct a tomographic SAR imaging model based on nonlocal filtering parameters and relaxation algorithm, and use the tomographic SAR imaging model to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain super-resolution imaging results.

[0053] It should be noted that the relaxation algorithm is a high-resolution spectral estimation method based on the nonlinear least squares (NLS) criterion. The relaxation algorithm does not make any restrictive assumptions about noise. It obtains the estimated values ​​of the parameters by minimizing the nonlinear variance. Even in the background of colored noise, it can obtain asymptotically effective estimates of each parameter and has good robustness.

[0054] In this step, the expression for efficient tomographic SAR super-resolution imaging based on the relaxation algorithm is:

[0055] ;

[0056] in, , The first The scattering coefficient and height of each scattering point K This represents the number of scattering points contained in a single pixel. Let Euclidean norm be denoted by ; where the penalty function is . The expression is:

[0057] ;

[0058] in, For the first n One observation data, N Let j be the total number of observed data, and j be the imaginary unit. For the first n The spatial frequency coordinates of each observation data; then... By introducing the above-mentioned efficient tomographic SAR super-resolution imaging expression based on the relaxation algorithm, the expression of the efficient tomographic synthetic aperture radar super-resolution imaging formula can be determined, that is, the tomographic SAR imaging model is expressed by the following formula:

[0059] ;

[0060] in, , The first The scattering coefficient and height of each scattering point K This represents the number of scattering points contained in a single pixel. , These are nonlocal interference parameters. It is a nonlinear interference; Denotes the Euclidean norm; This represents the scattering coefficient vector along the tomography direction; For the guiding matrix, M The number of scattering coefficient vectors is denoted by T, which represents the transpose.

[0061] In this embodiment, the multi-baseline tomographic SAR dataset is input into the aforementioned tomographic SAR imaging model, and significant peak detection and height inversion are performed sequentially to obtain... K The height information of each scattering point and the corresponding scattering intensity are used to perform three-dimensional reconstruction and obtain the point cloud of the entire scene, thus completing the super-resolution imaging of the current SAR image.

[0062] The efficient super-resolution imaging method for tomographic synthetic aperture radar provided in this invention acquires a multi-baseline tomographic SAR dataset by using multiple synthetic aperture radar SAR images; nonlocal filtering is performed on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters; a tomographic SAR imaging model is then constructed based on the nonlocal filtering parameters and a relaxation algorithm; and the tomographic SAR imaging model is used to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain super-resolution imaging results, thereby improving the super-resolution imaging capability and data processing efficiency of nonlocal tomographic SAR.

[0063] In some embodiments, obtaining a multi-baseline tomography SAR dataset from multiple synthetic aperture radar (SAR) images includes: performing image registration, deskewing, and phase error compensation on the multiple SAR images respectively to obtain a multi-baseline tomography SAR dataset.

[0064] In this embodiment, multiple SAR images are registered to ensure spatial alignment of SAR images with different baselines; multiple SAR images are de-skewed to eliminate phase tilt caused by platform movement or terrain undulation; multiple SAR images are phase error compensated to calibrate phase distortion caused by atmospheric disturbances, etc., and finally a multi-baseline tomography SAR dataset that can be used for three-dimensional reconstruction is obtained.

[0065] In this embodiment, each pixel in the SAR image is assigned an observation vector. Considering the presence of noise, the tomographic SAR imaging model can be expressed as:

[0066] ;

[0067] in, The observation vector for a given pixel; It is the scattering coefficient vector along the tomographic direction, which is often used to represent high-resolution three-dimensional structures.

[0068] In this embodiment, the guidance matrix can be constructed as follows: , For spatial frequency, auxiliary image Spatial vertical baseline of the main image For the signal wavelength, Discrete sampling of height values ​​for tomography; yes The noise vector has a mean of zero and a variance of . The Gaussian distribution.

[0069] In this embodiment, the mapping from the physical world to mathematical equations is established through the above-described tomographic SAR imaging model, thereby supporting the inversion of low-resolution observation data g (corresponding to the above-described observation vector) into mathematical equations. This model is used to provide a mathematical framework for super-resolution imaging.

[0070] The efficient tomographic synthetic aperture radar super-resolution imaging method provided in this invention obtains a multi-baseline tomographic SAR dataset by performing image registration, deskewing, and phase error compensation on multiple SAR images, thus providing reliable data support for subsequent super-resolution imaging.

[0071] In some embodiments, the super-resolution imaging result includes height information of multiple scattering points; after obtaining the super-resolution imaging result, the efficient tomographic synthetic aperture radar super-resolution imaging method further includes: performing three-dimensional reconstruction of the height information of multiple scattering points to obtain a reconstructed scene point cloud.

[0072] In this embodiment, an image pixel can correspond to one or more scattering points. The coordinates of each pixel and the height of the corresponding scattering point are combined to obtain a three-dimensional point. The additional attribute can be the scattering intensity corresponding to the scattering point (used for point cloud coloring or filtering), thereby obtaining the three-dimensional point cloud in the current SAR image scene, that is, the reconstructed scene point cloud.

[0073] This embodiment conducts the following simulation experiment: using the nonlocal relaxation algorithm, multi-view beamforming algorithm, nonlocal beamforming algorithm, and nonlocal compressed sensing algorithm of this embodiment to perform tomographic SAR imaging on the simulated area. According to the experimental results in the laboratory stage, it can be seen that: the multi-view beamforming algorithm uses a local averaging filtering algorithm, which severely degrades details such as building edges, manifested as the rounding of the square corners of buildings and the adhesion and fusion of the two building edges in region 2; from the three-dimensional point cloud results of nonlocal beamforming, nonlocal compressed sensing, and the method of this invention, it can be seen that the nonlocal filtering algorithm can better preserve details such as building edges, without the occurrence of building surface expansion and fusion.

[0074] The efficient tomographic synthetic aperture radar super-resolution imaging method provided in this invention provides a super-resolution imaging result that includes the height information of multiple scattering points. By performing three-dimensional reconstruction on the height information of multiple scattering points, a reconstructed scene point cloud is obtained, achieving high-precision three-dimensional reconstruction.

[0075] In some embodiments, after obtaining the reconstructed scene point cloud, the efficient tomographic synthetic aperture radar super-resolution imaging method further includes: computing target indicators based on the reconstructed scene point cloud, wherein the target indicators include resolution, estimation accuracy, and processing time; and evaluating the imaging performance of the reconstructed scene point cloud based on the target indicators.

[0076] In this embodiment, a method for determining the height resolution capability of tomographic SAR includes: having a probability of ≥60% that can distinguish between overlapping wall and ground target points, and having a small target point height estimation error (within 5 meters).

[0077] This embodiment uses the building in region 1 for resolution analysis. As can be seen from Table 1 below, the resolution of the sampling method in this embodiment is 34 meters, which is comparable to the resolution of 37 meters of the nonlocal compressed sensing algorithm, while the resolutions of multi-view beamforming and nonlocal beamforming algorithms are 49 meters and 48 meters, respectively. It can be concluded that the super-resolution capability of the sampling in this embodiment is comparable to that of the nonlocal compressed sensing algorithm, and is far superior to that of the multi-view beamforming and nonlocal beamforming algorithms.

[0078] In Table 1, the height estimation accuracies of the four algorithms are 1.66 meters, 1.71 meters, 1.70 meters, and 1.71 meters, respectively, indicating that the height estimation accuracy of these four algorithms is comparable. The four algorithms are processed using the same computer. The processing times for each algorithm are as follows: the multi-view beamforming algorithm takes 3.3 seconds, while the nonlocal beamforming algorithm takes a longer time because the nonlocal filtering parameter estimation takes approximately 390 seconds. The processing times for the nonlocal beamforming algorithm, the nonlocal compressed sensing algorithm, and the method used in this embodiment are 393.1 seconds, 6480 seconds, and 393.2 seconds, respectively. This indicates that the processing efficiency of the method used in this embodiment is comparable to that of the nonlocal beamforming algorithm and is far superior to that of the nonlocal compressed sensing algorithm.

[0079] Table 1. Parameters for High-Efficiency Tomographic SAR Super-Resolution Imaging of Four Algorithms

[0080]

[0081] Based on comprehensive simulation experiments and analysis, the following conclusions can be drawn: The efficient tomographic synthetic aperture radar super-resolution imaging method adopted in this embodiment has tomographic SAR super-resolution imaging capabilities comparable to nonlocal compressed sensing algorithms, and is far superior to traditional tomographic imaging algorithms. In terms of processing efficiency, the processing efficiency of the method of this invention is more than 16 times higher than that of nonlocal compressed sensing algorithms, which greatly improves data processing efficiency and is better suited for processing large-scale scenes.

[0082] The efficient tomographic synthetic aperture radar super-resolution imaging method provided in this invention provides reliable technical support for the 3D reconstruction process of scenes such as cities and forests by computing target indicators through the reconstructed scene point cloud and evaluating the imaging performance of the reconstructed scene point cloud based on the target indicators.

[0083] Figure 2 This is the second schematic diagram of the efficient tomographic synthetic aperture radar super-resolution imaging method provided by the present invention. Figure 2 In the illustrated embodiment, an efficient tomographic synthetic aperture radar super-resolution imaging method further includes: image preprocessing to obtain a tomographic SAR dataset; nonlocal interferometric parameter estimation; construction of a tomographic SAR imaging model using a nonlocal relaxation algorithm; and tomographic SAR super-resolution imaging to acquire target three-dimensional information.

[0084] The high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided by the present invention is described below. The high-efficiency tomographic synthetic aperture radar super-resolution imaging device described below and the high-efficiency tomographic synthetic aperture radar super-resolution imaging method described above can be referred to in correspondence with each other.

[0085] Figure 3 This is a schematic diagram of the structure of the high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided by the present invention, as shown below. Figure 3 As shown, the high-efficiency tomographic synthetic aperture radar super-resolution imaging device includes: a data acquisition module 310 and an imaging module 320.

[0086] The data acquisition module 310 is used to acquire a multi-baseline tomography SAR dataset based on multiple synthetic aperture radar SAR images; and to perform nonlocal filtering estimation on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters.

[0087] The imaging module 320 is used to construct a tomographic SAR imaging model based on nonlocal filtering parameters and relaxation algorithms, and to perform super-resolution imaging processing on a multi-baseline tomographic SAR dataset using the tomographic SAR imaging model to obtain super-resolution imaging results.

[0088] The high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided in this invention acquires a multi-baseline tomographic SAR dataset by using multiple synthetic aperture radar SAR images; nonlocal filtering is performed on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters; a tomographic SAR imaging model is then constructed based on the nonlocal filtering parameters and a relaxation algorithm; and the tomographic SAR imaging model is used to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain super-resolution imaging results, thereby improving the super-resolution imaging capability and data processing efficiency of nonlocal tomographic SAR.

[0089] In some embodiments, the super-resolution imaging result includes height information of multiple scattering points. The high-efficiency tomographic synthetic aperture radar super-resolution imaging device further includes: a reconstruction module, used to perform three-dimensional reconstruction of the height information of multiple scattering points after obtaining the super-resolution imaging result, to obtain the reconstructed scene point cloud.

[0090] The high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided in this embodiment of the invention includes height information of multiple scattering points in the super-resolution imaging result. By performing three-dimensional reconstruction on the height information of multiple scattering points, a reconstructed scene point cloud is obtained, thus achieving high-precision three-dimensional reconstruction.

[0091] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440. The processor 410, communications interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute an efficient tomographic synthetic aperture radar (SAR) super-resolution imaging method. This method includes: acquiring a multi-baseline tomographic SAR dataset based on multiple SAR images; performing nonlocal filtering estimation on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters; constructing a tomographic SAR imaging model based on the nonlocal filtering parameters and a relaxation algorithm; and using the tomographic SAR imaging model to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain a super-resolution imaging result.

[0092] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the efficient tomographic synthetic aperture radar super-resolution imaging method provided by the above methods. The method includes: acquiring a multi-baseline tomographic SAR dataset based on multiple synthetic aperture radar SAR images; performing nonlocal filtering estimation on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters; constructing a tomographic SAR imaging model based on the nonlocal filtering parameters and a relaxation algorithm; and using the tomographic SAR imaging model to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain a super-resolution imaging result.

[0094] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the efficient tomographic synthetic aperture radar super-resolution imaging method provided by the methods described above. The method includes: acquiring a multi-baseline tomographic SAR dataset based on multiple synthetic aperture radar SAR images; performing nonlocal filtering estimation on the interferometric parameters of the multiple SAR images to obtain nonlocal filtering parameters; constructing a tomographic SAR imaging model based on the nonlocal filtering parameters and a relaxation algorithm; and using the tomographic SAR imaging model to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain a super-resolution imaging result.

[0095] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0096] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A highly efficient super-resolution imaging method for tomographic synthetic aperture radar, characterized in that, include: A multi-baseline tomography SAR dataset was obtained from multiple synthetic aperture radar SAR images; Nonlocal filtering estimation is performed on the interferometric parameters of multiple SAR images to obtain the nonlocal filtering parameters; A tomographic SAR imaging model is constructed based on the nonlocal filtering parameters and relaxation algorithm, and the tomographic SAR imaging model is used to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset to obtain super-resolution imaging results. The nonlocal filtering estimation of interferometric parameters from multiple SAR images includes: The pixel is calculated using the following formula. parameters Make an estimate: ； in, For interference parameters, For a given At that time, the observed data Log-likelihood function; weighting coefficients and It depends on the statistical characteristics of the data; After nonlocal filtering estimation ; These are pixel coordinates; in, This can be expressed by the following formula: ； in, Let be the likelihood function. R The statistical expectation of the total power or scattering intensity of the scattering object in the SAR image; weighting coefficients. It is estimated using pixel block similarity, where the pixel block includes... All pixels within the center matching window; and To complete the registration of the two SAR images, The noise interference phase; The likelihood function is expressed as: ； in, These are the filter parameters. k This is the coordinate offset. The coordinates are The interference parameters of the pixels; The coordinates are Interference parameters of pixels; The coordinates are Observational data, The coordinates are Observational data; p This is a probability value. This is a cumulative multiplication operation; The tomographic SAR imaging model is expressed by the following formula: ； in, , The first k The scattering intensity and height of each scattering point K This represents the number of scattering points contained in a single pixel. , These are nonlocal interference parameters; Denotes the Euclidean norm; This represents the scattering coefficient vector along the tomography direction; For guiding matrix; For scattering intensity, For interference phase, This is coherence.

2. The high-efficiency tomographic synthetic aperture radar super-resolution imaging method according to claim 1, characterized in that, The process of obtaining a multi-baseline tomography SAR dataset from multiple synthetic aperture radar SAR images includes: The multi-baseline tomography SAR dataset is obtained by performing image registration, deskewing, and phase error compensation on multiple SAR images.

3. The high-efficiency tomographic synthetic aperture radar super-resolution imaging method according to claim 1, characterized in that, The super-resolution imaging results include height information of multiple scattering points; After obtaining the super-resolution imaging results, the method further includes: The height information of the multiple scattering points is used to reconstruct the scene point cloud in three dimensions.

4. The high-efficiency tomographic synthetic aperture radar super-resolution imaging method according to claim 3, characterized in that, After obtaining the reconstructed scene point cloud, the method further includes: Based on the reconstructed scene point cloud computing target indicators, wherein the target indicators include resolution, estimation accuracy, and processing time; The imaging performance of the reconstructed scene point cloud is evaluated based on the target metrics.

5. A high-efficiency tomographic synthetic aperture radar super-resolution imaging device, employing the high-efficiency tomographic synthetic aperture radar super-resolution imaging method as described in claim 1, characterized in that, include: The data acquisition module is used to acquire multi-baseline tomography SAR datasets from multiple synthetic aperture radar SAR images; Nonlocal filtering parameters are estimated by performing nonlocal filtering on the interferometric parameters of multiple SAR images to obtain the nonlocal filtering parameters; The imaging module is used to construct a tomographic SAR imaging model based on the nonlocal filtering parameters and relaxation algorithm, and to perform super-resolution imaging processing on the multi-baseline tomographic SAR dataset using the tomographic SAR imaging model to obtain super-resolution imaging results.

6. The high-efficiency tomographic synthetic aperture radar super-resolution imaging device according to claim 5, characterized in that, The super-resolution imaging result includes height information of multiple scattering points, and the device further includes: The reconstruction module is used to perform three-dimensional reconstruction of the height information of the multiple scattering points after obtaining the super-resolution imaging results, so as to obtain the reconstructed scene point cloud.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the efficient tomographic synthetic aperture radar super-resolution imaging method as described in any one of claims 1 to 4.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the efficient tomographic synthetic aperture radar super-resolution imaging method as described in any one of claims 1 to 4.

Images