High-precision neighborhood joint chromatographic sar super-resolution three-dimensional imaging method and device

Abstract

The application provides a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method and device, relates to the radar signal processing technical field, and its technical points are: carrying out local windowing operation on the multi-channel SAR image after registration processing, and constructing a multi-measurement vector (MMV) observation matrix; a Hankel-Toeplitz mixed matrix structure is introduced to implement double constraints on the MMV matrix: the Hankel matrix structure is used for enhancing the time domain low rank characteristics of each pixel signal in the MMV, and the Toeplitz matrix structure ensures that multiple pixels share consistent height frequency information; the time-frequency domain double constraints are converted into a joint optimization problem, a neighborhood joint tomography imaging model is established; a projection gradient descent algorithm based on matrix decomposition is used to realize efficient solution of the model, high-precision SAR three-dimensional super-resolution reconstruction results are obtained, and land exploration No. 1 satellite data is used to realize high-resolution three-dimensional SAR imaging verification.

Description

Technical Field

[0001] This application relates to the field of radar signal processing technology, and in particular to a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method and apparatus. Background Technology

[0002] As remote sensing observation demands shift towards more refined 3D mapping, traditional Synthetic Aperture Radar (SAR) 2D imaging technology is no longer sufficient to meet future applications such as high-precision terrain exploration and urban modeling. TomoSAR, by constructing a synthetic aperture along the elevation direction, possesses the capability to reconstruct 3D scenes from observed environments. It effectively solves inherent problems in SAR images caused by side-view imaging geometry, such as overlay and perspective contraction, providing a new technical approach for applications such as urban digital modeling and geological hazard assessment.

[0003] The resolution of TomoSAR in the elevation direction is essentially a common spectral estimation problem in signal processing. Its resolution capability depends on the Rayleigh resolution of the observation system and the super-resolution performance of the spectral estimation algorithm. Due to limitations imposed by radar platforms, the synthetic aperture length and number of observation channels in the elevation direction are usually limited in practical systems, resulting in a significantly lower elevation resolution than the azimuth and range directions, typically by an order of magnitude. This bottleneck has made super-resolution algorithms a core research direction in TomoSAR imaging. Considering the sparse scattering characteristics of urban buildings in the elevation direction, compressed sensing (CS) theory has been widely applied to TomoSAR 3D reconstruction, overcoming Rayleigh resolution by applying sparsity regularization. However, traditional CS algorithms suffer from grid discretization errors. To address this issue, meshless CS algorithms, represented by Atomic Norm Minimization (ANM), have emerged. These algorithms construct a completely new signal framework, achieving meshless reconstruction of the observed signal, thus completely avoiding grid effects and providing higher reconstruction accuracy for TomoSAR 3D imaging. However, these algorithms typically require more observation samples and have high computational complexity.

[0004] Meanwhile, for large-scale urban TomoSAR 3D imaging, the high diversity of building structures and scattering characteristics leads to reduced robustness of elevation inversion based on pixel-by-pixel spectral estimation. Current methods utilize auxiliary information to construct multi-channel observation vectors to improve the stability of spectral estimation. However, the available auxiliary information is limited, and its utilization efficiency is often low, making it difficult to effectively meet the joint requirements. The key challenge in the current development of tomographic SAR imaging technology lies in how to significantly improve the accuracy of elevation estimation while maintaining high computational efficiency. Summary of the Invention

[0005] The embodiments of this application provide a high-precision neighborhood joint tomographic SAR super-resolution three-dimensional imaging method and apparatus, which at least have the technical effect of improving the reconstruction performance of three-dimensional point clouds of tomographic SAR, while improving efficiency through high-precision computation.

[0006] To address the aforementioned technical problems, according to one aspect of this application, a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method is provided, comprising:

[0007] Windowing processing is performed on the registered multi-channel SAR image, and a multi-observation vector (MMV) observation matrix is ​​constructed using the neighboring pixel signals;

[0008] The constructed MMV matrix is ​​subjected to dual structural constraints using the Hankel-Toeplitz matrix. The Hankel structure is used to enhance the low-rank characteristics of the time domain of each pixel signal within the MMV, while the Toeplitz structure is used to constrain the consistent elevation frequency shared by multiple pixels.

[0009] The aforementioned dual structural constraints are modeled as a joint optimization problem, and a neighborhood-based joint tomography model is constructed.

[0010] The joint tomography model is solved using a matrix factorization-based projection gradient descent algorithm to obtain the three-dimensional super-resolution reconstruction results of the SAR image.

[0011] According to another aspect of this application, a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging device is also claimed, comprising:

[0012] The acquisition unit is configured to acquire multi-channel SAR images and perform preprocessing.

[0013] The first processing unit is configured to perform windowing processing on the registered multi-channel SAR image and construct a multi-observation vector (MMV) observation matrix using neighborhood pixel signals.

[0014] The second processing unit is configured to apply a dual structural constraint to the constructed MMV matrix using a Hankel-Toeplitz matrix. The Hankel structure is configured to enhance the low-rank characteristics of the time domain of each pixel signal within the MMV, while the Toeplitz structure is configured to constrain the consistent elevation frequency shared by multiple pixels.

[0015] The third processing unit is configured to model the above dual structural constraints as a joint optimization problem and construct a neighborhood-based joint tomography model.

[0016] The unit is configured to use a matrix factorization-based projection gradient descent algorithm to solve the joint tomography model and obtain the three-dimensional super-resolution reconstruction result of the SAR image.

[0017] According to another aspect of this application, a computer-readable storage medium is also claimed, the computer-readable storage medium including a stored program wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method described in any one of the preceding claims.

[0018] According to another aspect of this application, an electronic device is also claimed, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method described in any of the preceding claims.

[0019] This application has the following beneficial effects:

[0020] This application presents a high-precision neighborhood joint tomographic SAR super-resolution 3D imaging method. Compared with existing technologies, this invention models the tomographic SAR super-resolution problem as an optimization problem of neighborhood pixel-assisted center pixel elevation estimation. Based on time-frequency dual-domain joint pixel constraints, it enhances the compactness of the signal's internal structure, driving neighborhood information to be tightly focused on the center pixel elevation estimation, significantly improving sample utilization and outlier resistance, while maintaining high overall computational accuracy. Compared with traditional tomographic SAR imaging methods, this invention can improve the super-resolution performance and solution efficiency of 3D reconstruction. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 A flowchart illustrating a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to an embodiment of this application is shown.

[0023] Figure 2 This is a schematic diagram of the overall process of the high-precision joint neighborhood tomography SAR super-resolution three-dimensional imaging method proposed in this invention.

[0024] Figure 3 The paper presents a comparison of the processing effects of the method of the present invention and the traditional compressed sensing algorithm under measured building data. (a) shows the processing result of the traditional compressed sensing algorithm, (b) shows the processing result of the method of the present invention, (c) shows the result of the traditional compressed sensing algorithm after structured projection processing, and (d) shows the result of the method of the present invention after structured projection processing.

[0025] Figure 4 The paper presents a comparison of the processing effects of the method of the present invention and the traditional compressed sensing algorithm on satellite data, where (a) is the processing result of the traditional compressed sensing algorithm and (b) is the processing result of the method of the present invention.

[0026] Figure 5 A structural block diagram of a simplified high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging device according to an embodiment of this application is shown. Detailed Implementation

[0027] As introduced in the background section, a key challenge in the development of current tomographic SAR imaging technology lies in how to significantly improve the accuracy of elevation estimation while maintaining high computational efficiency. To address this technical challenge, the neighborhood joint technique significantly improves the statistical characteristics of signal processing by effectively increasing the amount of data samples available for spectral estimation, thus demonstrating significant research value and application prospects in the field of tomographic SAR three-dimensional imaging. Embodiments of this application provide a high-precision neighborhood joint tomographic SAR super-resolution three-dimensional imaging method, apparatus, computer, readable storage medium, and electronic device.

[0028] The technical solutions of the embodiments of this application 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 application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] This embodiment provides a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] Figure 1 This is a flowchart illustrating a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to an embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0031] Step S101: Windowing is performed on the registered multi-channel SAR image, and a multi-observation vector MMV observation matrix is ​​constructed using the neighboring pixel signals.

[0032] Step S102: The constructed MMV matrix is ​​subjected to dual structural constraints using the Hankel-Toeplitz matrix. The Hankel structure is used to enhance the low-rank characteristics of the time domain of each pixel signal within the MMV, and the Toeplitz structure is used to constrain the consistent elevation frequency shared by multiple pixels.

[0033] Step S103: The above dual structural constraints are modeled as a joint optimization problem, and a neighborhood-based joint tomography model is constructed.

[0034] Step S104: The joint tomography model is solved using a matrix factorization-based projection gradient descent algorithm to obtain the three-dimensional super-resolution reconstruction result of the SAR image.

[0035] refer to Figures 1-2 In one embodiment, the step of windowing the registered multi-channel SAR image and constructing a multi-observation vector (MMV) observation matrix using neighboring pixel signals specifically includes:

[0036] The multi-observation vector MMV observation matrix is ​​represented as size matrix

[0037]

[0038] in, For the number of SAR images, The number of neighboring pixels. For the first The observation vector corresponding to each pixel This is the matrix transpose symbol.

[0039] In one embodiment, the step of applying a dual structural constraint to the constructed MMV matrix using a Hankel-Toeplitz matrix specifically includes:

[0040] The Hankel-Toeplitz matrix constraint is expressed as follows:

[0041]

[0042] in, For multi-pixel observation matrix The corresponding noise-free data is of size . , For matrix The first matrix Column, i.e., the first Data corresponding to each pixel For the Hankel-Toeplitz matrix transformation operator, This is the Toeplitz vector corresponding to the shared elevation frequency of multiple pixels, with a length of... , For vectors Take the conjugate operation. and These are the Toeplitz operator and the Hankel operator, respectively;

[0043] The length is The vectors are transformed into vectors of size 1. The Toeplitz matrix and Hankel matrix are expressed as follows:

[0044]

[0045] in For the selected pencil parameters, ,

[0046] Hankel-Toeplitz matrix constraint operator The observation matrix of the neighborhood multi-pixel is subject to dual time-frequency constraints: the Hankel matrix constrains the signal structure of the pixel in the time domain, and the Toeplitz matrix constrains its frequency domain spectrum vector. The structure.

[0047] In one embodiment, the step of constructing a neighborhood-based joint tomography model includes:

[0048]

[0049] in, Denotes the square of the 2-norm of a matrix. Representation matrix The List.

[0050] In one embodiment, the step of solving the joint tomography model using a matrix factorization-based projection gradient descent algorithm specifically includes:

[0051] Handling positive semidefinite problems with high computational complexity;

[0052] The Hankel-Toeplitz matrix is ​​reduced in dimensionality using matrix factorization, decomposing each pixel into two parts of size 1. submatrix and express;

[0053]

[0054] in, Representing the The first submatrix corresponding to each pixel. Similarly, This is the conjugate transpose. The preset number of overlays;

[0055] Only the solution submatrix is ​​required and That is, the entire imaging model is now resolved. At this point, the neighborhood joint tomographic imaging model is updated to the following submatrix representation.

[0056] .

[0057] Step S103 further includes: in order to ensure the weight of the target center pixel in the joint neighborhood of multiple pixels, a weighted processing is performed, and the Hankel operator and Toeplitz operator in the Hankel-Toeplitz matrix transformation operator are updated, requiring a new Hankel operator. Toeplitz operator ,in For weighted operators, Modeling is performed based on the number of original vector elements on the diagonal of the corresponding Hankel / Toeplitz matrix;

[0058] To ensure the accuracy of matrix decomposition, the matrix decomposition is modeled accordingly:

[0059]

[0060] in, q For a summation index variable, and The new Hankel operator and Toeplitz operator The adjoint operator, It is an identity matrix.

[0061] Furthermore, the above imaging models are uniformly modeled as objective optimization functions:

[0062]

[0063] The projection gradient descent algorithm based on matrix factorization is used to efficiently solve the joint imaging model, including:

[0064] Iteratively update the target variable and two submatrices. and :

[0065]

[0066] in and The result of the t-th iteration of the two submatrices in the above equation is... For projection operators, The iteration step size, and The gradient for the corresponding t-th iteration can be expressed as:

[0067]

[0068] When the two sub-matrices of the target variable are completed and After solving, the matrix multiplication relationship is used. Reconstruct the spectral Toeplitz matrix of the target neighborhood pixel data sharing.

[0069] In one embodiment, the step of obtaining the three-dimensional super-resolution reconstruction result of the SAR image includes:

[0070] Based on the reconstructed Toeplitz matrix, the root MUSIC algorithm is used for gridless frequency estimation, which accurately retrieves the normalized elevation frequency corresponding to the center pixel within the neighborhood pixel window. The obtained elevation frequency is combined with the target distance index information in the SAR image, and through three-dimensional coordinate transformation, the high-precision three-dimensional imaging result of the scene is finally obtained.

[0071] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method of this application will be described in detail below with reference to specific embodiments.

[0072] To verify the beneficial effects of the present invention, the following experiments were conducted:

[0073] 1. Using the SARMV3D-1.0 Yuncheng airborne dataset released by the Aerospace Information Research Institute of the Chinese Academy of Sciences, 3D imaging processing was performed using both traditional compressed sensing algorithms and the algorithm of this invention. The processed results were then subjected to structured projection to obtain 3D point cloud projection results. 2. Based on SAR data from the LuTan-1 satellite, different algorithms were used for 3D imaging processing to achieve high-resolution 3D SAR imaging verification. The reconstruction quality of each algorithm was evaluated by comparing indicators such as building detail features, overall structural integrity, and clutter distribution.

[0074] The experimental results are as follows: (Refer to the diagram) Figure 3 , Figure 3 The image shows a comparison of imaging results between traditional compressed sensing methods and the algorithm of this invention for the Yuncheng dataset. The algorithm of this invention achieves clear reconstruction of fine structures such as building windows, with more prominent structural features at key locations such as rooftop transitions, and significantly reduces the number of noise and artifacts in the reconstruction results. (See reference...) Figure 4 , Figure 4Based on the 3D imaging results of the LuTan-1 dataset, the algorithm of this invention reconstructs a complete and clear building structure, significantly improving the overall imaging quality.

[0075] This application also provides a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging device. It should be noted that the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging device of this application embodiment can be used to execute the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method provided in this application embodiment. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0076] The following describes the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging device provided in the embodiments of this application.

[0077] Figure 5 This is a structural block diagram of a high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging device provided according to an embodiment of this application.

[0078] like Figure 5 As shown, the device includes:

[0079] Acquisition unit 51 is configured to acquire multi-channel SAR images and perform preprocessing.

[0080] The first processing unit 52 is configured to perform windowing processing on the registered multi-channel SAR image and construct a multi-observation vector MMV observation matrix using neighborhood pixel signals.

[0081] The second processing unit 53 is configured to apply a dual structural constraint to the constructed MMV matrix using a Hankel-Toeplitz matrix. The Hankel structure is configured to enhance the low-rank characteristics of the time domain of each pixel signal within the MMV, and the Toeplitz structure is configured to constrain the consistent elevation frequency shared by multiple pixels.

[0082] The third processing unit 54 is configured to model the above-mentioned dual structural constraints as a joint optimization problem and construct a neighborhood-based joint tomography model.

[0083] Unit 55 is configured to use a matrix factorization-based projection gradient descent algorithm to solve the joint tomography model and obtain the three-dimensional super-resolution reconstruction result of the SAR image.

[0084] The aforementioned high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method and apparatus includes a processor and a memory. The acquisition unit, first processing unit, second processing unit, third processing unit, and determination unit are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve the corresponding functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0085] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and adjusting kernel parameters can address the low accuracy and efficiency of existing coarse registration methods for single-feature roadways.

[0086] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0087] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method.

[0088] This invention provides a processor for running a program, wherein the program executes the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method.

[0089] This application also provides an electronic device comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include methods for performing any of the above-described high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging methods.

[0090] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0091] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0096] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0097] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0098] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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. Such 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 this application.

Claims

1. A high-precision neighborhood joint chromatography SAR super-resolution three-dimensional imaging method, characterized in that, include: Windowing processing is performed on the registered multi-channel SAR image, and a multi-observation vector (MMV) observation matrix is ​​constructed using the neighboring pixel signals; The constructed MMV matrix is ​​subjected to dual structural constraints using the Hankel-Toeplitz matrix. The Hankel structure is used to enhance the low-rank characteristics of the time domain of each pixel signal within the MMV, while the Toeplitz structure is used to constrain the consistent elevation frequency shared by multiple pixels. The dual structural constraints are modeled as a joint optimization problem, and a neighborhood-based joint tomography model is constructed. The joint tomography model is solved using a matrix factorization-based projection gradient descent algorithm to obtain the three-dimensional super-resolution reconstruction results of the SAR image; wherein, The steps of windowing the registered multi-channel SAR image and constructing a multi-observation vector (MMV) observation matrix using neighborhood pixel signals specifically include: The multiple measurement vector (MMV) observation matrix is denoted as a matrix of size ​ in, For the number of SAR images, The number of neighboring pixels. For the first The observation vector corresponding to each pixel This is the matrix transpose symbol; The step of applying a dual structural constraint to the constructed MMV matrix using the Hankel-Toeplitz matrix specifically includes: The Hankel-Toeplitz matrix constraint is expressed as follows: ， in, For multi-pixel observation matrix The corresponding noise-free data is of size . , For matrix The Column, i.e., the first Data corresponding to each pixel For the Hankel-Toeplitz matrix constraint operator, This is the spectral vector corresponding to the shared elevation frequency of multiple pixels, with a length of... , For vectors Take the conjugate operation. and These are the Toeplitz operator and the Hankel operator, respectively; The length is The vectors are transformed into vectors of size 1. The Toeplitz matrix and Hankel matrix are expressed as follows: ， in For the selected pencil parameters, Hankel-Toeplitz matrix constraint operator The observation matrix of the neighborhood multi-pixel is subject to dual time-frequency constraints: the Hankel matrix constrains the signal structure of the pixel in the time domain, and the Toeplitz matrix constrains its spectral vector in the frequency domain. The structure.

2. The high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to claim 1, characterized in that, The steps for constructing a neighborhood-based joint tomography model include: ， in, Denotes the square of the 2-norm of a matrix. Representation matrix The List.

3. The high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to claim 2, characterized in that, The step of solving the joint tomography model using a matrix factorization-based projection gradient descent algorithm specifically includes: Handling positive semidefinite problems with high computational complexity; The Hankel-Toeplitz matrix is ​​reduced in dimensionality using matrix factorization, decomposing each pixel into two parts of size 1. submatrix and express; ， in, Representing the The first submatrix corresponding to each pixel. Similarly, This is the conjugate transpose. The preset number of overlays; Only the solution submatrix is ​​required and This completes the analysis of the entire imaging model. At this point, the neighborhood joint tomographic imaging model is updated to the following submatrix representation: 。 4. The high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to claim 3, characterized in that, Also includes: To ensure the weight of the target center pixel in the joint neighborhood of multiple pixels, a weighted processing is performed. This involves updating the Hankel and Toeplitz operators in the Hankel-Toeplitz matrix transformation, requiring a new Hankel operator. Toeplitz operator ,in For weighted operators, Modeling is performed based on the number of original vector elements on the diagonal of the corresponding Hankel / Toeplitz matrix; to ensure the accuracy of matrix decomposition, corresponding modeling is performed on the matrix decomposition: ， in, and The new Hankel operator and Toeplitz operator The adjoint operator, It is an identity matrix.

5. The high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to claim 1, characterized in that, The steps for obtaining the three-dimensional super-resolution reconstruction results of the SAR image include: Based on the reconstructed Toeplitz matrix, the root MUSIC algorithm is used for gridless frequency estimation, which accurately retrieves the normalized elevation frequency corresponding to the center pixel within the neighborhood pixel window. The obtained elevation frequency is combined with the target distance index information in the SAR image, and through three-dimensional coordinate transformation, the high-precision three-dimensional imaging result of the scene is finally obtained.

6. A high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging device, used to implement the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method as described in any one of claims 1-5, characterized in that, include: The acquisition unit is configured to acquire multi-channel SAR images and perform preprocessing. The first processing unit is configured to perform windowing processing on the registered multi-channel SAR image and construct a multi-observation vector (MMV) observation matrix using neighborhood pixel signals. The second processing unit is configured to apply a dual structural constraint to the constructed MMV matrix using a Hankel-Toeplitz matrix. The Hankel structure is configured to enhance the low-rank characteristics of the time domain of each pixel signal within the MMV, while the Toeplitz structure is configured to constrain the consistent elevation frequency shared by multiple pixels. The third processing unit is configured to model the above dual structural constraints as a joint optimization problem and construct a neighborhood-based joint tomography model. The unit is configured to use a matrix factorization-based projection gradient descent algorithm to solve the joint tomography model and obtain the three-dimensional super-resolution reconstruction result of the SAR image.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to any one of claims 1 to 5.

8. An electronic device, characterized in that, It includes: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing the high-precision neighborhood joint tomography SAR super-resolution three-dimensional imaging method according to any one of claims 1 to 5.

Images