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

Abstract

The invention belongs to the technical field of radar image processing, and provides an efficient tomographic synthetic aperture radar super-resolution imaging method and device, and the method comprises the steps: obtaining a multi-baseline tomographic SAR data set according to a plurality of SAR images; performing non-local filtering estimation on the interference parameters of the plurality of SAR images to obtain non-local filtering parameters; and constructing a tomographic SAR imaging model based on the non-local filtering parameters and a relaxation algorithm, and performing super-resolution imaging processing on the multi-baseline tomographic SAR data set by using the tomographic SAR imaging model to obtain a super-resolution imaging result. According to the method provided by the embodiment of the invention, the super-resolution imaging capability and the data processing efficiency of the non-local tomography SAR are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of radar image processing, and in particular to a high-efficiency tomographic synthetic aperture radar super-resolution imaging method and device. Background Art

[0002] Tomographic SAR (Synthetic Aperture Radar) technology uses multi-baseline SAR data to achieve three-dimensional reconstruction of targets and has been successfully applied to the 3D reconstruction of cities, forests, and other areas. The high-dimensional imaging resolution of Tomographic SAR technology is limited by the baseline span. Multi-baseline tomographic SAR data acquired by spaceborne SAR systems during repeated orbits is limited in baseline span, resulting in high-dimensional resolution ranging from tens to dozens of meters. This high-dimensional resolution is significantly lower than the range and azimuth resolution of SAR.

[0003] In related technologies, tomographic SAR super-resolution imaging algorithms mainly include spectral estimation algorithms (such as Capon and MUSIC) 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 local multi-view method is combined with tomographic SAR to achieve super-resolution imaging, the existing technology also introduces the non-local filter (NLF) algorithm into tomographic SAR imaging. Specifically, the non-local filter algorithm is combined with the spectral estimation algorithm and the compressed sensing algorithm 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 take into account both super-resolution capability and computational complexity when achieving SAR super-resolution imaging, and it is not suitable for three-dimensional reconstruction of large-scale scenes. Summary of the Invention

[0004] The present invention provides a high-efficiency tomographic synthetic aperture radar super-resolution imaging method and device, which are used to solve the defect that the existing technology uses a non-local filtering algorithm combined with a spectral estimation algorithm and a compressed sensing algorithm to achieve SAR super-resolution imaging, which is difficult to achieve both super-resolution capability and computational complexity. The method of the present invention improves the super-resolution imaging capability and data processing efficiency of non-local tomographic SAR.

[0005] The present invention provides a high-efficiency tomographic synthetic aperture radar super-resolution imaging method, comprising: Acquire a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar (SAR) images; perform non-local filtering estimation on the interferometric parameters of the multiple SAR images to obtain non-local filtering parameters; A tomographic SAR imaging model is constructed based on the non-local filtering parameters and the relaxation algorithm, and the tomographic SAR imaging model is used to perform super-resolution imaging processing on the multi-baseline tomographic SAR data set to obtain a super-resolution imaging result.

[0006] According to a high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention, the method of obtaining a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar SAR images includes: The multiple SAR images are respectively processed with image registration, de-skewing and phase error compensation to obtain the multi-baseline tomographic SAR data set.

[0007] According to a high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention, the tomographic SAR imaging model is expressed by the following formula: ; in, 、 Respectively The scattering coefficient and height of each scattering point, K is the number of scattering points included in a pixel; , is the nonlocal interference parameter, is nonlinear interference; represents the Euclidean norm; is the scattering coefficient vector along the tomographic direction; is the orientation matrix, M is the number of scattering coefficient vectors, and T represents the transpose.

[0008] According to a high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention, the super-resolution imaging result includes height information of multiple scattering points. After obtaining the super-resolution imaging result, the method further includes: The height information of the multiple scattering points is three-dimensionally reconstructed to obtain a reconstructed scene point cloud.

[0009] According to a high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention, after obtaining the reconstructed scene point cloud, the method further includes: Computing target indicators based on the reconstructed scene point cloud, wherein the target indicators include resolution, estimation accuracy and processing time; The imaging performance of the reconstructed scene point cloud is evaluated according to the target indicator.

[0010] The present invention also provides a high-efficiency tomographic synthetic aperture radar super-resolution imaging device, comprising: The data acquisition module is used to acquire a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar (SAR) images; perform non-local filtering estimation on the interference parameters of the multiple SAR images to obtain non-local filtering parameters; An imaging module is used to construct a tomographic SAR imaging model based on the non-local filtering parameters and the relaxation algorithm, and to perform super-resolution imaging processing on the multi-baseline tomographic SAR data set using the tomographic SAR imaging model to obtain a super-resolution imaging result.

[0011] According to a high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided by the present invention, 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 on the height information of the multiple scattering points after obtaining the super-resolution imaging result to obtain a reconstructed scene point cloud.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the high-efficiency tomographic synthetic aperture radar super-resolution imaging method as described above is implemented.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the high-efficiency tomographic synthetic aperture radar super-resolution imaging method as described above is implemented.

[0014] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned high-efficiency tomographic synthetic aperture radar super-resolution imaging methods.

[0015] The present invention provides a high-efficiency tomographic synthetic aperture radar super-resolution imaging method and device, which obtain a multi-baseline tomographic SAR data set through multiple SAR images; perform non-local filtering estimation on the interference parameters of the multiple SAR images to obtain non-local filtering parameters; then construct a tomographic SAR imaging model based on the non-local filtering parameters and a relaxation algorithm; and use the tomographic SAR imaging model to perform super-resolution imaging processing on the multi-baseline tomographic SAR data set to obtain a super-resolution imaging result, thereby improving the super-resolution imaging capability and data processing efficiency of the non-local tomographic SAR. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is one of the flow charts of the high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention.

[0018] Figure 2 This is the second flow chart of the high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention.

[0019] Figure 3 It is a structural schematic diagram of a high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0022] The following combination Figures 1-4 The present invention describes a high-efficiency tomographic synthetic aperture radar super-resolution imaging method and device.

[0023] Figure 1 This is one of the flow charts of the high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention, such as Figure 1 As shown, the method includes the following steps: Step 110: Acquire a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar (SAR) images; perform non-local filtering estimation on the interferometric parameters of the multiple SAR images to obtain non-local filtering parameters.

[0024] In this step, multiple SAR images can be preprocessed (such as image registration, de-skewing, and phase error compensation) to eliminate observation errors and ensure data consistency and quality.

[0025] In this step, the essence of the process of obtaining a multi-baseline tomographic SAR dataset based on multiple synthetic aperture radar (SAR) images is to map the observed data into an elevation scattering coefficient distribution.

[0026] 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.

[0027] In this embodiment, the non-local filtering method is used to estimate the interference parameters of the tomographic SAR image through non-local filtering by the following steps: (1) Assumption The coordinates are The interference parameters estimated by non-local filtering of pixels are is the scattering intensity, is the interference phase, For coherence, this embodiment will use non-local filtering technology to The non-local filtering technique uses a weighted maximum likelihood estimator to estimate The specific calculation process is expressed by the following formula: ; in, is the interference parameter, For a given When the observation data The log-likelihood function of and Depends on the statistical characteristics of the data, non-local filtering techniques use the statistical characteristics of interferometric SAR for calculation; is the non-local filtering estimate ; is the pixel coordinate.

[0028] (2) Assumptions and To complete the registration of the two SAR image amplitudes, is the noise interference phase, then: ; in, is the likelihood function, R is the statistical expectation value of the total power (or scattering intensity) of the scatterer in the SAR image; the weighting coefficient is to use pixel blocks (in The similarity is estimated by using the scale-invariant similarity criterion. The likelihood function is expressed as: ; in, is the filtering parameter, is the coordinate offset, The coordinates are Interference parameters of pixels; The coordinates are The interferometric parameters of the pixels; The coordinates are The observation data, The coordinates are Observational data of p is the probability value, is a cumulative multiplication operation. The likelihood function is used for maximum likelihood estimation, and the estimated parameter expression is: ; ; ; in, The coordinates are The scattering intensity of the pixel, for estimated value of; The coordinates are The interferometric phase of the pixel, for estimated value of; The coordinates are The coherence of the pixels, for estimated value of; The first SAR image is The observation data at The second SAR image is The observation data at for The complex conjugate operation value of ; | | is the modulus operation; In summary, we can get Non-local filtering estimation of .

[0029] Step 120: construct a tomographic SAR imaging model based on non-local filtering parameters and a relaxation algorithm, and use 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.

[0030] 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, and it obtains the estimated values of the parameters by minimizing the nonlinear variance; even in the background of colored noise, it can obtain asymptotically valid estimates of each parameter with good robustness.

[0031] In this step, the expression of efficient tomographic SAR super-resolution imaging based on the relaxation algorithm is: ; in, 、 Respectively The scattering coefficient and height of each scattering point, K is the number of scattering points included in a pixel, represents the Euclidean norm; where the penalty function The expression is: ; in, For the n Observation data, N is the total number of observation data, j is the imaginary unit, For the n The spatial frequency coordinates of the observation data; then By introducing the above-mentioned high-efficiency tomographic SAR super-resolution imaging expression based on the relaxation algorithm, the expression of the high-efficiency tomographic synthetic aperture radar super-resolution imaging formula can be determined, that is, the tomographic SAR imaging model is expressed by the following formula: ; in, 、 Respectively The scattering coefficient and height of each scattering point, K is the number of scattering points included in a pixel; , is the nonlocal interference parameter, is nonlinear interference; represents the Euclidean norm; is the scattering coefficient vector along the tomographic direction; is the orientation matrix, M is the number of scattering coefficient vectors, and T represents the transpose.

[0032] In this embodiment, the multi-baseline tomographic SAR data set is input into the above-mentioned tomographic SAR imaging model, and the significant peak detection and height inversion processes are performed in sequence to obtain K The height information of each scattering point and the corresponding scattering intensity are finally used to perform three-dimensional reconstruction to obtain the full-scene point cloud and complete the super-resolution imaging of the current SAR image.

[0033] An efficient tomographic synthetic aperture radar (SAR) super-resolution imaging method provided by an embodiment of the present invention obtains a multi-baseline tomographic SAR dataset using multiple SAR images; performs non-local filtering estimation on the interferometric parameters of the multiple SAR images to obtain non-local filtering parameters; then constructs a tomographic SAR imaging model based on the non-local filtering parameters and a relaxation algorithm; and uses 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, thereby improving the super-resolution imaging capability and data processing efficiency of the non-local tomographic SAR.

[0034] In some embodiments, obtaining a multi-baseline tomographic SAR dataset based on multiple synthetic aperture radar (SAR) images includes performing image registration, deskew, and phase error compensation on the multiple SAR images to obtain the multi-baseline tomographic SAR dataset.

[0035] In this embodiment, image registration is performed on multiple SAR images to ensure spatial alignment of SAR images with different baselines; de-skewing is performed on the multiple SAR images to eliminate phase tilt caused by platform motion or terrain undulations; and phase error compensation is performed on the multiple SAR images to calibrate phase distortion caused by atmospheric disturbances, etc., ultimately obtaining a multi-baseline tomographic SAR dataset that can be used for three-dimensional reconstruction.

[0036] In this embodiment, each pixel in the SAR image is assumed to have an observation vector. When considering the presence of noise, the tomographic SAR imaging model can be expressed as: ; in, is the observation vector of a certain pixel; It is the scattering coefficient vector along the tomographic direction and is often used to represent high-resolution three-dimensional structures.

[0037] In this embodiment, the steering matrix can be constructed as , is the spatial frequency, Auxiliary image The vertical baseline of the space with respect to the main image, is the signal wavelength, is a discrete sampling of tomographic height values; yes The noise vector has a mean of zero and a variance of Gaussian distribution.

[0038] In this embodiment, the mapping from the physical world to the mathematical equation is established by the above-mentioned tomographic SAR imaging model, thereby supporting the inversion of the low-resolution observation data g (corresponding to the above-mentioned observation vector) , this model is used to provide a mathematical expression framework for super-resolution imaging.

[0039] The high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the embodiment of the present invention obtains a multi-baseline tomographic SAR dataset by performing image registration, de-skewing, and phase error compensation on multiple SAR images, providing reliable data support for subsequent super-resolution imaging.

[0040] In some embodiments, the super-resolution imaging result includes height information of multiple scattering points; after obtaining the super-resolution imaging result, the high-efficiency tomography synthetic aperture radar super-resolution imaging method further includes: performing three-dimensional reconstruction of the height information of the multiple scattering points to obtain a reconstructed scene point cloud.

[0041] In this embodiment, an image pixel may correspond to one or more scattering points. The coordinates of each pixel point are combined with the height of the corresponding scattering point to obtain a three-dimensional point. An additional attribute may be the scattering intensity corresponding to the scattering point (used for point cloud coloring or filtering), thereby obtaining a three-dimensional point cloud in the current SAR image scene, that is, a reconstructed scene point cloud.

[0042] This embodiment conducts the following simulation experiments: the non-local relaxation algorithm, multi-view beamforming algorithm, non-local beamforming algorithm and non-local compressed sensing algorithm of this embodiment are used to perform tomographic SAR imaging on the simulation area. According to the experimental results in the laboratory stage, it can be seen that the multi-view beamforming algorithm adopts the local average filtering algorithm, and the details such as the edges of the building are seriously deteriorated, which is manifested as the rounded corners of the square building and the adhesion and fusion of the edges of the two buildings in area 2; from the three-dimensional point cloud results of non-local beamforming, non-local compressed sensing and the method of the present invention, it can be seen that the use of the non-local filtering algorithm can better maintain details such as the edges of the building, and there is no expansion and fusion of the building surface.

[0043] The embodiment of the present invention provides a high-efficiency tomographic synthetic aperture radar super-resolution imaging method. The super-resolution imaging results include 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, thereby achieving high-precision three-dimensional reconstruction.

[0044] In some embodiments, after obtaining the reconstructed scene point cloud, the high-efficiency 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 according to the target indicators.

[0045] In this embodiment, a method for determining the height resolution capability of a tomographic SAR includes: having a probability of ≥60% of being able to distinguish overlapping wall and ground targets, and a relatively small error value in the target point height estimation (within 5 meters).

[0046] This embodiment uses buildings in Area 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 37-meter resolution of the non-local compressed sensing algorithm. The resolutions of the multi-view beamforming and non-local beamforming algorithms are 49 meters and 48 meters, respectively. It can be concluded that the super-resolution capability of the sampling method in this embodiment is comparable to that of the non-local compressed sensing algorithm and far superior to that of the multi-view beamforming and non-local beamforming algorithms.

[0047] 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 four algorithms have comparable height estimation accuracies. The four algorithms are calculated using the same computer. The processing times for each algorithm are as follows: the multi-view beamforming algorithm takes 3.3 seconds, while the non-local beamforming algorithm takes a longer time, approximately 390 seconds, because the non-local filter parameter estimation takes longer. The processing times for the non-local beamforming algorithm, the non-local 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 non-local beamforming algorithm and is far superior to that of the non-local compressed sensing algorithm.

[0048] Table 1. High-efficiency tomographic SAR super-resolution imaging parameters of four algorithms

[0049] Comprehensive simulation experiment analysis leads to the following conclusions: the high-efficiency tomographic synthetic aperture radar super-resolution imaging method adopted in this embodiment has a tomographic SAR super-resolution imaging capability comparable to that of the non-local compressed sensing algorithm, and is far superior to the traditional tomographic imaging algorithm. In terms of processing efficiency, the processing efficiency of the method of the present invention is more than 16 times that of the non-local compressed sensing algorithm, which greatly improves the data processing efficiency and is better suitable for large-scale scene processing.

[0050] The high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the embodiment of the present invention calculates target indicators through the reconstructed scene point cloud and evaluates the imaging performance of the reconstructed scene point cloud based on the target indicators, providing reliable technical support for the three-dimensional reconstruction process of scenes such as cities and forests.

[0051] Figure 2 This is the second flow chart of the high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the present invention. Figure 2 In the illustrated embodiment, a high-efficiency tomographic synthetic aperture radar super-resolution imaging method further includes: image preprocessing to obtain a tomographic SAR data set; non-local interferometric parameter estimation; tomographic SAR imaging model construction using a non-local relaxation algorithm; and tomographic SAR super-resolution imaging to obtain three-dimensional target information.

[0052] The following describes the high-efficiency tomography synthetic aperture radar super-resolution imaging device provided by the present invention. The high-efficiency tomography synthetic aperture radar super-resolution imaging device described below and the high-efficiency tomography synthetic aperture radar super-resolution imaging method described above can be referred to each other.

[0053] Figure 3 Schematic diagram of the structure of the high-efficiency tomographic synthetic aperture radar super-resolution imaging device provided by the present invention, such as 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.

[0054] The data acquisition module 310 is used to acquire a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar (SAR) images; perform non-local filtering estimation on the interferometric parameters of the multiple SAR images to obtain non-local filtering parameters; The imaging module 320 is used to construct a tomographic SAR imaging model based on non-local filtering parameters and a relaxation algorithm, and perform super-resolution imaging processing on a multi-baseline tomographic SAR data set using the tomographic SAR imaging model to obtain a super-resolution imaging result.

[0055] An efficient tomographic synthetic aperture radar (SAR) super-resolution imaging device provided by an embodiment of the present invention acquires a multi-baseline tomographic SAR dataset using multiple SAR images; performs non-local filtering estimation on the interferometric parameters of the multiple SAR images to obtain non-local filtering parameters; then constructs a tomographic SAR imaging model based on the non-local filtering parameters and a relaxation algorithm; and uses 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, thereby improving the super-resolution imaging capability and data processing efficiency of the non-local tomographic SAR.

[0056] In some embodiments, the super-resolution imaging result includes height information of multiple scattering points, and the high-efficiency tomography synthetic aperture radar super-resolution imaging device also includes: a reconstruction module, which is used to perform three-dimensional reconstruct the height information of multiple scattering points after obtaining the super-resolution imaging result to obtain a reconstructed scene point cloud.

[0057] The embodiment of the present invention provides a high-efficiency tomographic synthetic aperture radar super-resolution imaging device. The super-resolution imaging results include 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, thereby achieving high-precision three-dimensional reconstruction.

[0058] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communications interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call logic instructions in the memory 430 to execute a high-efficiency tomographic synthetic aperture radar super-resolution imaging method, which includes: acquiring a multi-baseline tomographic SAR dataset based on multiple synthetic aperture radar (SAR) images; performing non-local filtering estimation on interferometric parameters of the multiple SAR images to obtain non-local filtering parameters; constructing a tomographic SAR imaging model based on the non-local filtering parameters and a relaxation algorithm, and performing super-resolution imaging processing on the multi-baseline tomographic SAR dataset using the tomographic SAR imaging model to obtain a super-resolution imaging result.

[0059] Furthermore, the logic 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 portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0060] On the other hand, the present invention also provides a computer program product, which includes a computer program, and the computer program 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 high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the above-mentioned methods, the method including: obtaining a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar SAR images; performing non-local filtering estimation on the interference parameters of the multiple SAR images to obtain non-local filtering parameters; constructing a tomographic SAR imaging model based on the non-local 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 data set to obtain a super-resolution imaging result.

[0061] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the high-efficiency tomographic synthetic aperture radar super-resolution imaging method provided by the above-mentioned methods, the method comprising: obtaining a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar SAR images; performing non-local filtering estimation on the interference parameters of the multiple SAR images to obtain non-local filtering parameters; constructing a tomographic SAR imaging model based on the non-local 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 data set to obtain a super-resolution imaging result.

[0062] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0063] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion 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, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A high-efficiency tomographic synthetic aperture radar super-resolution imaging method, characterized in that: include: Obtain multi-baseline tomographic SAR dataset based on multiple synthetic aperture radar SAR images; Perform non-local filtering estimation on the interference parameters of multiple SAR images to obtain non-local filtering parameters; A tomographic SAR imaging model is constructed based on the non-local filtering parameters and the relaxation algorithm, and the tomographic SAR imaging model is used to perform super-resolution imaging processing on the multi-baseline tomographic SAR data set to obtain a super-resolution imaging result.

2. The high-efficiency tomography synthetic aperture radar super-resolution imaging method according to claim 1, characterized in that: The method of obtaining a multi-baseline tomographic SAR data set according to a plurality of synthetic aperture radar SAR images comprises: The multiple SAR images are respectively processed with image registration, de-skewing and phase error compensation to obtain the multi-baseline tomographic SAR data set.

3. The high-efficiency tomography synthetic aperture radar super-resolution imaging method according to claim 1, characterized in that: The tomographic SAR imaging model is expressed by the following formula: ； in, 、 Respectively The scattering coefficient and height of each scattering point, K is the number of scattering points included in a pixel; , is the nonlocal interference parameter, is nonlinear interference; represents the Euclidean norm; is the scattering coefficient vector along the tomographic direction; is the orientation matrix, M is the number of scattering coefficient vectors, and T represents the transpose.

4. The high-efficiency tomography synthetic aperture radar super-resolution imaging method according to claim 1, characterized in that: The super-resolution imaging result includes height information of multiple scattering points; After obtaining the super-resolution imaging result, the method further includes: The height information of the multiple scattering points is three-dimensionally reconstructed to obtain a reconstructed scene point cloud.

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

6. A high-efficiency tomographic synthetic aperture radar super-resolution imaging device, characterized in that: include: A data acquisition module is used to acquire a multi-baseline tomographic SAR data set based on multiple synthetic aperture radar SAR images; Perform non-local filtering estimation on the interference parameters of multiple SAR images to obtain non-local filtering parameters; An imaging module is used to construct a tomographic SAR imaging model based on the non-local filtering parameters and the relaxation algorithm, and to perform super-resolution imaging processing on the multi-baseline tomographic SAR data set using the tomographic SAR imaging model to obtain a super-resolution imaging result.

7. The high-efficiency tomography synthetic aperture radar super-resolution imaging device according to claim 6, 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 on the height information of the multiple scattering points after obtaining the super-resolution imaging result to obtain a reconstructed scene point cloud.

8. 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, the high-efficiency tomography synthetic aperture radar super-resolution imaging method according to any one of claims 1 to 5 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the high-efficiency tomographic synthetic aperture radar super-resolution imaging method according to any one of claims 1 to 5 is implemented.

Images