Sparse array chromatography SAR three-dimensional reconstruction method and system based on SVD-DCS

Abstract

The invention provides a sparse array tomography SAR three-dimensional reconstruction method and system based on SVD-DCS, and the method comprises the steps: carrying out the preprocessing of complex image registration, phase dechirp processing, and amplitude-phase error correction processing on sparse array SAR image data for solving the problems that the tomography SAR is difficult to reconstruct accurately and there are many false targets under the condition of irregular and sparse sampling; the method comprises the steps of recognizing adjacent equal-height pixels, reducing DCS data processing dimensions through SVD decomposition, and reconstructing a scattering intensity contour of a reference pixel through noise energy estimation and SVD-DCS spectrum estimation; by selecting a model order, estimating a target parameter, carrying out geocoding on data, and outputting a tomography SAR point cloud space position and scattering intensity data, a function of improving density and reliability of sparse array SAR three-dimensional reconstruction is realized. According to the method, the point cloud reconstruction density is high, side lobe noise suppression is better, and the reliability of tomography SAR point cloud reconstruction is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of high-dimensional synthetic aperture radar imaging data processing, and in particular relates to a sparse array tomography SAR three-dimensional reconstruction method and system based on SVD-DCS. Background Art

[0002] Synthetic Aperture Radar (SAR) is an all-day, all-weather, active microwave imaging radar with a certain degree of penetration into the ground. Developed on the basis of SAR, tomographic SAR is a new technology for high-efficiency, true stereoscopic, and high-precision three-dimensional reconstruction. It has broad application prospects in the fields of urban real-scene three-dimensional construction, field exploration and rescue, environmental reconnaissance in non-cooperative areas, and forest biomass and ice reserve estimation. Tomographic SAR achieves third-dimensional resolution and information recovery of overlapping areas by adding multiple antennas or multiple flights in the normal direction of the azimuth-slant range plane and performing a third-dimensional elevation synthetic aperture. With the development of a new generation of spaceborne SAR constellations, many satellites are able to obtain multiple high-resolution SAR images of the same area in a short time interval, which provides reliable data support for tomographic SAR three-dimensional reconstruction technology.

[0003] However, when the tomographic SAR performs the third-dimensional synthetic aperture along the normal direction of the azimuth-slant range plane, the multi-pass or multi-antenna sampling forms an uneven and sparse array. When the traditional spectral analysis methods such as DFT, BF, and Capon are used for elevation inversion of the overlapped area, the elevation signal sampling is required to be uniform, and the sampling frequency must satisfy the Nyquist sampling theorem, resulting in the tomographic resolution being unable to break through the limitations of the Rayleigh resolution. Although the MUSIC, singular value decomposition (SVD) and other methods are used for de-overlapping, it can surpass the Rayleigh resolution in elevation resolution, but at the expense of spatial resolution. Although the tomographic SAR system has elevation resolution, it is limited by the number of antennas and the length of the synthetic aperture in elevation is small, resulting in the elevation resolution being much smaller than the azimuth and slant range resolutions. In order to meet the requirements of third-dimensional super-resolution imaging under sparse and irregular baseline distribution conditions, the SL1MMER method based on compressed sensing (CS) came into being. It uses the sparse characteristics of the target signal structure to perform non-correlated measurements on low-dimensional space, low-resolution, and sub-Nyquist sampling data to achieve accurate signal reconstruction. However, when the number of baselines is small and unevenly distributed, the SL1MMER method is difficult to suppress the third-dimensional sidelobe noise, thereby introducing a large number of false targets.

[0004] As an extension of CS theory, in 2005, Baron et al. proposed the theory of distributed compressed sensing (DCS) for the joint sparsity between multiple signals. Based on the principle in information theory that the joint entropy of two sources is less than the sum of their respective entropies, DCS defines the concept of joint sparsity, fully exploits the correlation between multiple signals, and performs joint CS. Its performance must be better than the performance of the individual CS of each signal, thereby reducing the system's requirements for the sampling rate. Given that DCS is superior to CS, studying the DCS-based tomographic SAR 3D reconstruction method is of great significance for reducing the requirements of tomographic SAR on the system and improving the timeliness of tomographic SAR 3D reconstruction. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a sparse array tomographic SAR three-dimensional reconstruction method and system based on SVD-DCS, so as to improve the density and reliability of sparse array SAR three-dimensional reconstruction.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a sparse array tomographic SAR three-dimensional reconstruction method based on SVD-DCS, comprising the following steps:

[0007] S1: Acquire sparse array SAR images and preprocess them to obtain preprocessed tomographic SAR stack data;

[0008] S2: With the reference pixel as the center, within the defined window range, the average logarithmic coherence threshold and average amplitude constraint criteria are adopted to search and identify adjacent pixels of equal height pixel by pixel in the preprocessed tomographic SAR stack data;

[0009] S3: Adaptive weight design is performed based on the coherence coefficient index weight, and SVD decomposition is performed on the tomographic SAR data stack composed of adjacent equal-height pixels to reduce the DCS data processing dimension;

[0010] S4: Observe the SVD projection through MMV and estimate the noise energy at the reference pixel of the reduced-dimensional tomographic SAR data stack as the parameter of the SVD-DCS spectrum estimation model;

[0011] S5: Substitute the reduced-dimensional tomographic SAR data stack, estimate the spectrum matrix using the SVD-DCS spectrum estimation model, and reconstruct the reference pixel scattering intensity profile;

[0012] S6: Constructing the model order solution model with information theory constraints through the spectrum matrix to determine the order of the tomographic SAR point cloud model;

[0013] S7: Construct the target three-dimensional complex scattering coefficient optimization solution model through the spectrum matrix to estimate the scattering intensity value of the tomographic SAR point cloud;

[0014] S8: geocode the tomographic SAR point cloud data in the range-Doppler-elevation coordinate system, and output the spatial position and scattering intensity data of the tomographic SAR point cloud.

[0015] According to the above scheme, in step S1, the specific steps are:

[0016] S11: performing complex image registration on the input sparse array SAR image to obtain a SAR complex image;

[0017] S12: inputting reference terrain data, performing phase de-skewing processing on the SAR complex image to obtain a de-skewing SAR complex image;

[0018] S13: performing amplitude and phase error correction on the de-slanted SAR complex image to obtain pre-processed tomographic SAR stack data.

[0019] According to the above scheme, in step S2, the specific steps are:

[0020] S21: Let M be the number of frames of the sparse array SAR image, x i (m) represents the complex value of the mth preprocessed tomographic SAR stack data at the reference pixel i, y i (m) represents the complex value of the pixel adjacent to the reference pixel i in the mth preprocessed tomographic SAR stack data. * Represents the conjugate operation of complex numbers; constructs a similarity measure index between the reference pixel x and the adjacent pixel y The distribution range is between (0,1]:

[0021]

[0022] S22: Take the negative logarithm of the similarity measure index between the reference pixel x and the adjacent pixel y, and the distribution range is between [0,∞):

[0023]

[0024] S23: Let N be the total number of pixels in the SAR image block; in the coherent calculation window, calculate the similarity measurement index between the reference pixel image block and the corresponding pixels of the adjacent pixel image block one by one, calculate the average similarity measurement index and use it as the similarity measurement index between the reference pixel image block and the adjacent pixel image block:

[0025]

[0026] S24: Calculate the average amplitude A of the tomographic SAR data at the reference pixel and the adjacent pixels x , A y ;

[0027] S25: average amplitude A at the reference pixel xGreater than 0.5 times the average amplitude A of the adjacent pixels y , the average amplitude A at the reference pixel x Less than 2 times the average amplitude A of the adjacent pixels y , and the similarity measure index D(x,y) with the reference pixel image block is less than -lnC thresh The central pixel of the adjacent pixel image block is identified as the adjacent equal-height pixel, where the coherence threshold C is set thresh =0.5;

[0028] S26: Traverse all pixels as reference pixels, and find out the adjacent pixels of the same height of all pixels according to steps S41 to S45.

[0029] Furthermore, in step S3, the specific steps are:

[0030] S31: Let K be the rank of the matrix, L be the number of adjacent pixels of equal height plus 1, S be a diagonal matrix consisting of singular value elements arranged from large to small, K < L, K < M, U M×K 、V L×K The eigenvectors corresponding to the first K singular values ​​constitute the signal subspace, and the eigenvectors corresponding to the remaining singular values ​​constitute the noise subspace. The tomographic SAR data stack Y composed of adjacent equal-height pixels is decomposed into subspaces corresponding to signals and noises by SVD, and only the signal subspace is retained:

[0031] Y=USV H =[U M×K ,U M×(M-K) ]S M×L [V L×K ,V L×(L-K) ] H (4);

[0032] S32: Order Among them I K×K is the K×K unit matrix, 0 (L-K)×K is a zero matrix of (LK)×L; the tomographic SAR data stack after dimensionality reduction is calculated as

[0033] Furthermore, in step S4, the specific steps are:

[0034] S41: Set represents the diagonal matrix of singular values, represents the matrix composed of singular value vectors; the compressed sensing matrix A is decomposed into singular values ​​as follows:

[0035]

[0036] S42: Set represents the nth singular value vector, Represents the tomographic SAR data stack after dimensionality reduction The i-th column vector of , the obtained parameter is used as the energy of the noise at the reference pixel, which is used for the noise energy constraint of SVD-DCS spectrum estimation and the order determination of the tomographic SAR point cloud model; the formula for calculating the noise energy is as follows:

[0037]

[0038] Furthermore, in step S5, the specific steps are:

[0039] S51: Assume || || 2,1 、|| || F They represent the L1-L2 mixed norm and F norm of the matrix respectively; substitute the reduced-dimensional tomographic SAR data stack to construct the SVD-DCS spectrum estimation model:

[0040]

[0041] S52: Estimation of the spectrum matrix using the spectral gradient projection method

[0042] S53: Set Represents the estimated spectrum matrix The i-th row vector of the final reference pixel gives the scattering intensity result

[0043] Furthermore, in step S6, the specific steps are:

[0044] S61: Set is the number of valid targets at the reference pixel, z is the previous The location of the maximum spectral peak, Before The three-dimensional scattering coefficient corresponding to the maximum spectrum peak position; construct the model order solution model constrained by information theory:

[0045]

[0046] The information constraint items adopt BIC and MDL models:

[0047]

[0048] S62: According to the combination of spectrum peaks from large to small, the cost function is substituted and calculated by exhaustive method, and the number of effective scattering targets and the elevation position corresponding to the target distribution with the minimum cost function are used as the number of effective targets at the reference pixel And elevation position information.

[0049] Furthermore, in step S7, the specific steps are:

[0050] S71: Construct the target three-dimensional complex scattering coefficient optimization solution model:

[0051]

[0052] S72: Set express The i-th column vector of is ; the least square method is used to solve formula (10), and the scattering intensity value of the tomographic SAR point cloud is estimated to be:

[0053]

[0054] A sparse array tomographic SAR three-dimensional reconstruction system based on SVD-DCS, including an SLC image registration module, a phase de-skew module, an amplitude and phase error correction module, an adjacent contour pixel recognition module, an adaptive weight design module, a noise energy estimation module, an SVD-DCS spectrum estimation module, a model order selection module, a target parameter estimation module and a geocoding module;

[0055] The SLC image registration module is used to acquire sparse array SAR images and perform complex image registration to obtain SAR complex images;

[0056] The phase de-skewing module is used to perform phase de-skewing processing on the SAR complex image according to the reference terrain data to obtain a de-skewing SAR complex image;

[0057] The amplitude and phase error correction module is used to perform amplitude and phase error correction on the de-slanted SAR complex image to obtain pre-processed tomographic SAR stack data;

[0058] The adjacent contour pixel recognition module is used to search and identify adjacent contour pixels pixel by pixel in the preprocessed tomographic SAR stack data within the defined window range, taking the reference pixel as the center and adopting the average logarithmic coherence threshold and average amplitude constraint criteria.

[0059] The adaptive weight design module is used to perform adaptive weight design according to the coherence coefficient index weight, and perform SVD decomposition on the tomographic SAR data stack composed of adjacent equal-height pixels to reduce the DCS data processing dimension;

[0060] The noise energy estimation module is used to estimate the noise energy at the reference pixel of the tomographic SAR data stack after dimension reduction by observing the SVD projection through MMV as the parameter of the SVD-DCS spectrum estimation model;

[0061] The SVD-DCS spectrum estimation module is used to estimate the spectrum matrix after substituting the reduced-dimensional tomographic SAR data stack and reconstruct the reference pixel scattering intensity profile;

[0062] The model order selection module is used to construct the model order solution model constrained by information theory through the spectrum matrix to determine the order of the tomographic SAR point cloud model;

[0063] The target parameter estimation module is used to construct the target three-dimensional complex scattering coefficient optimization solution model through the spectrum matrix and estimate the scattering intensity value of the tomographic SAR point cloud;

[0064] The geocoding module is used to geocode the tomographic SAR point cloud data in the range-Doppler-elevation coordinate system and output the spatial position and scattering intensity data of the tomographic SAR point cloud.

[0065] A computer memory stores a computer program executable by a computer processor, wherein the computer program executes a sparse array tomography SAR three-dimensional reconstruction method based on SVD-DCS.

[0066] The beneficial effects of the present invention are:

[0067] 1. A sparse array tomographic SAR three-dimensional reconstruction method and system based on SVD-DCS of the present invention aims at the problem that tomographic SAR is difficult to accurately reconstruct and has many false targets under irregular and sparse sampling conditions. The sparse array SAR image data is preprocessed including complex image registration, phase de-skew processing, and amplitude and phase error correction processing to obtain preprocessed tomographic SAR stack data; the DCS data processing dimension is reduced by identifying adjacent equal-height pixels and SVD decomposition, and the scattering intensity profile of the reference pixel is reconstructed through noise energy estimation and SVD-DCS spectrum estimation; the model order is selected, the target parameters are estimated, and the data is geocoded to output the spatial position and scattering intensity data of the tomographic SAR point cloud, thereby realizing the function of improving the density and reliability of sparse array SAR three-dimensional reconstruction.

[0068] 2. Compared with the traditional Beamforming, Capon, Music multi-view methods, and the SVD-Wiener, SL1MMER single-view methods, the method of the present invention has the highest number of point cloud reconstructions and a high density of 3D reconstruction.

[0069] 3. Compared with the existing SVD-Wiener and SL1MMER super-resolution single-view methods, the method of the present invention suppresses sidelobe noise better and improves the reliability of tomographic SAR point cloud reconstruction through precise reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 is a flow chart of an embodiment of the present invention.

[0071] Figure 2 This is a comparison of the 3D reconstruction results of sparse array tomographic SAR using various methods;

[0072] in:

[0073] Figure 2 (a) is the result of 3D reconstruction of sparse array tomographic SAR using the beamforming method;

[0074] Figure 2 (b) is the result of sparse array tomographic SAR 3D reconstruction using the Capon method;

[0075] Figure 2 (c) is the result of sparse array tomographic SAR 3D reconstruction using the Music method;

[0076] Figure 2 (d) is the result of 3D reconstruction of sparse array tomographic SAR using the single-view SVD-Wiener method of German Aerospace;

[0077] Figure 2 (e) is the result of sparse array tomographic SAR 3D reconstruction using the SL1MMER method;

[0078] Figure 2 (f) is a diagram showing the sparse array tomographic SAR 3D reconstruction result using the SVD-DCS method of this embodiment.

[0079] Figure 3 This is a local enlarged image comparing the tomographic SAR 3D reconstruction results of various methods.

[0080] in:

[0081] Figure 3 (a) is a partial enlarged view of the tomographic SAR 3D reconstruction result of the German Aerospace single-view SVD-Wiener method;

[0082] Figure 3 (b) is a partial enlarged image of the tomographic SAR 3D reconstruction result of the SL1MMER method; Figure 3 (c) is a partially enlarged view of the tomographic SAR 3D reconstruction result of the SVD-DCS method of this embodiment. DETAILED DESCRIPTION

[0083] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0084] Example 1

[0085] See also Figure 1 The embodiment of the present invention includes three big data processing processes, data preprocessing, spectrum estimation and tomographic 3D point cloud generation, which specifically include the following steps:

[0086] Step 1: Acquire sparse array SAR images and preprocess them to obtain preprocessed tomographic SAR stack data;

[0087] Step 1.1: Acquire sparse array SAR image data and perform complex image registration on the sparse array SAR image data;

[0088] Step 1.2: Input the reference terrain data and perform phase de-skewing on the registered SAR complex image;

[0089] Step 1.3: Perform amplitude and phase error correction on the de-slanted SAR complex image to obtain pre-processed tomographic SAR stack data;

[0090] Step 2: With the reference pixel as the center, search and identify adjacent pixels of equal height pixel by pixel within the defined window range; the method used to identify adjacent pixels of equal height is the method of average logarithmic coherence threshold and average amplitude constraint criterion, and the specific steps are as follows:

[0091] Step 2.1: Construct a similarity measure index between the reference pixel x and the adjacent pixel y

[0092]

[0093] Its distribution range is between (0,1], where M is the number of frames of the sparse array SAR image, x is i (m) represents the complex value of the mth preprocessed tomographic SAR stack data at the reference pixel i, y i (m) represents the complex value of the pixel adjacent to the reference pixel i in the mth preprocessed tomographic SAR stack data. * Represents the conjugation operation of a complex number.

[0094] Step 2.2: Take the negative logarithm of the similarity measure index between the reference pixel x and the adjacent pixel y:

[0095]

[0096] Its distribution range is between [0,∞).

[0097] Step 2.3: In the coherence calculation window, calculate the similarity measurement index between the reference pixel image block and the corresponding pixels of the adjacent pixel image block one by one, calculate the average similarity measurement index, and use it as the similarity measurement index between the reference pixel image block and its adjacent pixel image block:

[0098]

[0099] Where N is the total number of pixels in the SAR image block.

[0100] Step 2.4: Calculate the average amplitude A of the tomographic SAR data at the reference pixel and its adjacent pixels x , A y .

[0101] Step 2.5: Set the average amplitude A at the reference pixel x Greater than 0.5 times the average amplitude A of the adjacent pixels y , and the average amplitude A at the reference pixel x Less than 2 times the average amplitude A of the adjacent pixels y , and the similarity measurement index D(x,y) with the reference pixel image block is less than -lnC thresh The central pixel of the adjacent pixel image block is identified as the adjacent equal-height pixel, where the coherence threshold C is set thresh =0.5.

[0102] Step 2.6: Traverse all pixels as reference pixels and find the adjacent pixels of equal height for all pixels according to steps 4.1 to 4.5.

[0103] Step 3: Perform SVD decomposition on the tomographic SAR data stack composed of adjacent equal-height pixels to reduce the DCS data processing dimension;

[0104] Step 3.1: Decompose the tomographic SAR data stack Y consisting of adjacent equal-height pixels into subspaces corresponding to signals and noises through SVD and only retain the signal subspace, that is,

[0105] Y=USV H =[U M×K ,U M×(M-K) ]S M×L [V L×K ,V L×(L-K) ] H (4)

[0106] Where K is the rank of the matrix, L is the number of adjacent pixels of equal height plus 1, S is a diagonal matrix composed of singular value elements arranged from large to small, K<L, K<M, U M×K 、V L×K It means that the eigenvectors corresponding to the first K singular values ​​constitute the signal subspace, and the eigenvectors corresponding to the remaining singular values ​​constitute the noise subspace.

[0107] Step 3.2: Order Among them I K×K is the K×K unit matrix, 0 (L-K)×K is a zero matrix of (LK)×L; the tomographic SAR data stack after dimensionality reduction is calculated as

[0108] Step 4: Estimate the noise energy of the SVD-DCS spectrum estimation model;

[0109] Step 4.1: Perform singular value decomposition on the compressed sensing matrix A as follows:

[0110]

[0111] in represents the diagonal matrix of singular values, Represents the matrix of singular value vectors.

[0112] Step 4.2: The formula for calculating noise energy is as follows:

[0113]

[0114] in represents the nth singular value vector, Represents the tomographic SAR data stack after dimensionality reduction The i-th column vector of is used as the energy of the noise at the reference pixel, which is used for the noise energy constraint of SVD-DCS spectrum estimation and the order determination of the tomographic SAR point cloud model.

[0115] Step 5: Substitute the reduced-dimensional tomographic SAR data stack and reconstruct the reference pixel scattering intensity profile using the SVD-DCS spectrum estimation model;

[0116] Step 5.1: Substitute the reduced-dimensional tomographic SAR data stack and construct the SVD-DCS spectrum estimation model:

[0117]

[0118] where || || 2,1 、|| || F They represent the L1-L2 mixed norm and F norm of the matrix respectively.

[0119] Step 5.2: Use the spectral gradient projection method to estimate the spectrum matrix

[0120] Step 5.3: Give the final scattering intensity result at the reference pixel in Represents the estimated spectrum matrix The i-th row vector of .

[0121] Step 6: Determine the order of the tomographic SAR point cloud model;

[0122] Step 6.1: Construct the model order solution model for information theory constraints:

[0123]

[0124] in is the number of valid targets at the reference pixel, z is the previous The location of the maximum spectral peak, Before The three-dimensional scattering coefficient corresponding to the maximum spectrum peak position, and the information constraint term adopts the BIC and MDL models:

[0125]

[0126] Step 6.2: Substitute and calculate the cost function by exhaustive method according to the combination of spectrum peaks from large to small, and use the number of effective scattering targets and elevation positions corresponding to the target distribution with the minimum cost function as the number of effective targets at the reference pixel. And elevation position information.

[0127] Step 7: Estimate the scattering intensity value of the tomographic SAR point cloud;

[0128] Step 7.1: Construct the target three-dimensional complex scattering coefficient optimization solution model:

[0129]

[0130] Step 7.2: Solve the above equation by the least squares method and estimate the scattering intensity value of the tomographic SAR point cloud using the following equation:

[0131]

[0132] in express The i-th column vector of .

[0133] Step 8: Geocode the tomographic SAR point cloud data in the range-Doppler-elevation coordinate system and output the spatial position and scattering intensity data of the tomographic SAR point cloud.

[0134] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0135] Example 2

[0136] This embodiment uses the method of embodiment 1 to process the airborne 12-channel X-band array SAR data of a certain city area. The data baseline is horizontally distributed along the cross-track direction, and the bandwidth is 810 MHz.

[0137] Figure 2The 3D reconstruction results of several classic tomographic SAR methods are compared, including: traditional multi-view Beamforming, Capon, Music, De Aerospace single-view SVD-Wiener, SL1MMER, and the SVD-DCS method of this embodiment. It can be seen from the results that: SVD-Wiener and SL1MMER single-view methods have better point cloud reconstruction coverage, but large sidelobe noise; while Beamforming, Capon, and Music multi-view methods have better sidelobe noise suppression capabilities, but poor coverage of point cloud reconstruction.

[0138] Figure 3 for Figure 2 From the local area magnification of the results of the last three methods, it can be seen that in the case of sparse arrays, the SVD-DCS method of this embodiment has better monitoring sidelobe noise suppression capability, point cloud coverage and density.

[0139] Table 1 shows the statistical results of the number of point cloud recovery rates of several methods. The benchmark number of point cloud recovery is 1860755. It can be seen that the method of this embodiment has the highest recovery rate, even better than the benchmark level.

[0140] Table 1 Number of point clouds reconstructed by tomographic SAR using different methods

[0141] method Number of point clouds Beamforming 929,267 Capon 863,326 Music 900,179 SVD-Wiener 1,569,377 SL1MMER 1,652,245 DCS 2,153,021

[0142] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. A sparse array tomographic SAR 3D reconstruction method based on SVD-DCS, characterized by: The following steps are involved: S1: Acquire sparse array SAR images and preprocess them to obtain preprocessed tomographic SAR stack data; S2: With the reference pixel as the center, within the defined window range, the average logarithmic coherence threshold and average amplitude constraint criteria are adopted to search and identify adjacent pixels of equal height pixel by pixel in the preprocessed tomographic SAR stack data; S3: Adaptive weight design is performed based on the coherence coefficient index weight, and SVD decomposition is performed on the tomographic SAR data stack composed of adjacent equal-height pixels to reduce the DCS data processing dimension; S4: Observe the SVD projection through MMV and estimate the noise energy at the reference pixel of the reduced-dimensional tomographic SAR data stack as the parameter of the SVD-DCS spectrum estimation model; S5: Substitute the reduced-dimensional tomographic SAR data stack, estimate the spectrum matrix using the SVD-DCS spectrum estimation model, and reconstruct the reference pixel scattering intensity profile; S6: Constructing the model order solution model with information theory constraints through the spectrum matrix to determine the order of the tomographic SAR point cloud model; S7: Construct the target three-dimensional complex scattering coefficient optimization solution model through the spectrum matrix to estimate the scattering intensity value of the tomographic SAR point cloud; S8: geocode the tomographic SAR point cloud data in the range-Doppler-elevation coordinate system, and output the spatial position and scattering intensity data of the tomographic SAR point cloud.

2. The sparse array tomographic SAR 3D reconstruction method based on SVD-DCS according to claim 1, characterized in that: In the step S1, the specific steps are: S11: Acquire a sparse array SAR image and perform complex image registration to obtain a SAR complex image; S12: performing phase de-skewing processing on the SAR complex image according to the reference terrain data to obtain a de-skewing SAR complex image; S13: performing amplitude and phase error correction on the de-slanted SAR complex image to obtain pre-processed tomographic SAR stack data.

3. The sparse array tomographic SAR 3D reconstruction method based on SVD-DCS according to claim 1, characterized in that: In the step S2, the specific steps are: S21: Let M be the number of frames of the sparse array SAR image, x i (m) represents the complex value of the mth preprocessed tomographic SAR stack data at the reference pixel i, y i (m) represents the complex value of the pixel adjacent to the reference pixel i in the mth preprocessed tomographic SAR stack data. * Represents the conjugation operation of complex numbers; Construct a similarity measure index between the reference pixel x and the adjacent pixel y The distribution range is between (0,1]: S22: Take the negative logarithm of the similarity measure index between the reference pixel x and the adjacent pixel y, and the distribution range is between [0,∞): S23: Let N be the total number of pixels in the SAR image block; in the coherent calculation window, calculate the similarity measurement index between the reference pixel image block and the corresponding pixels of the adjacent pixel image block one by one, calculate the average similarity measurement index and use it as the similarity measurement index between the reference pixel image block and the adjacent pixel image block: S24: Calculate the average amplitude A of the tomographic SAR data at the reference pixel and the adjacent pixels x , A y ; S25: average amplitude A at the reference pixel x Greater than 0.5 times the average amplitude A of the adjacent pixels y , the average amplitude A at the reference pixel x Less than 2 times the average amplitude A of the adjacent pixels y , and the similarity measure index D(x,y) with the reference pixel image block is less than -lnC thresh The central pixel of the adjacent pixel image block is identified as the adjacent equal-height pixel, where the coherence threshold C is set thresh =0.5; S26: Traverse all pixels as reference pixels, and find out the adjacent pixels of the same height of all pixels according to steps S41 to S45.

4. The sparse array tomographic SAR 3D reconstruction method based on SVD-DCS according to claim 3, characterized in that: In the step S3, the specific steps are: S31: Let K be the rank of the matrix, L be the number of adjacent pixels of equal height plus 1, S be a diagonal matrix consisting of singular value elements arranged from large to small, K<L, K<M, U M×K 、V L×K The eigenvectors corresponding to the first K singular values ​​constitute the signal subspace, and the eigenvectors corresponding to the remaining singular values ​​constitute the noise subspace. The tomographic SAR data stack Y composed of adjacent equal-height pixels is decomposed into subspaces corresponding to signals and noises by SVD, and only the signal subspace is retained: Y=USV H =[U M×K ,U M×(M-K) ]S M×L [V L×K ,V L×(L-K)] H (4); S32: Order Among them I K×K is the K×K unit matrix, 0 (L-K)×K is a zero matrix of (LK)×L; The tomographic SAR data stack after dimensionality reduction is calculated as 5. The sparse array tomographic SAR 3D reconstruction method based on SVD-DCS according to claim 4, characterized in that: In the step S4, the specific steps are: S41: Set represents the diagonal matrix of singular values, represents the matrix composed of singular value vectors; the compressed sensing matrix A is decomposed into singular values ​​as follows: S42: Set represents the nth singular value vector, Represents the tomographic SAR data stack after dimensionality reduction The i-th column vector of , calculates the noise energy at the reference pixel:

6. The sparse array tomographic SAR 3D reconstruction method based on SVD-DCS according to claim 5, characterized in that: In the step S5, the specific steps are: S51: Assume |||| 2,1 、|||| F They represent the L1-L2 mixed norm and F norm of the matrix respectively; substitute the reduced-dimensional tomographic SAR data stack to construct the SVD-DCS spectrum estimation model: S52: Estimation of the spectrum matrix using the spectral gradient projection method S53: Set Represents the estimated spectrum matrix The i-th row vector of the final reference pixel gives the scattering intensity result 7. The SVD-DCS-based sparse array tomographic SAR 3D reconstruction method according to claim 6, characterized in that: In the step S6, the specific steps are: S61: Set is the number of valid targets at the reference pixel, z is the previous The location of the maximum spectral peak, Before The three-dimensional scattering coefficient corresponding to the maximum spectrum peak position; construct the model order solution model constrained by information theory: The information constraint items adopt BIC and MDL models: S62: According to the combination of spectrum peaks from large to small, the cost function is substituted and calculated by exhaustive method, and the number of effective scattering targets and the elevation position corresponding to the target distribution with the minimum cost function are used as the number of effective targets at the reference pixel And elevation position information.

8. The sparse array tomographic SAR 3D reconstruction method based on SVD-DCS according to claim 7, characterized in that: In the step S7, the specific steps are: S71: Construct the target three-dimensional complex scattering coefficient optimization solution model: S72: Set express The i-th column vector of is ; the least square method is used to solve formula (10), and the scattering intensity value of the tomographic SAR point cloud is estimated to be:

9. A sparse array tomographic SAR 3D reconstruction system based on SVD-DCS, characterized by: It includes SLC image registration module, phase de-skew module, amplitude and phase error correction module, adjacent contour pixel recognition module, adaptive weight design module, noise energy estimation module, SVD-DCS spectrum estimation module, model order selection module, target parameter estimation module and geocoding module; The SLC image registration module is used to acquire sparse array SAR images and perform complex image registration to obtain SAR complex images; The phase de-skewing module is used to perform phase de-skewing processing on the SAR complex image according to the reference terrain data to obtain a de-skewing SAR complex image; The amplitude and phase error correction module is used to perform amplitude and phase error correction on the de-slanted SAR complex image to obtain pre-processed tomographic SAR stack data; The adjacent contour pixel recognition module is used to search and identify adjacent contour pixels pixel by pixel in the preprocessed tomographic SAR stack data within the defined window range, taking the reference pixel as the center and adopting the average logarithmic coherence threshold and average amplitude constraint criteria. The adaptive weight design module is used to perform adaptive weight design according to the coherence coefficient index weight, and perform SVD decomposition on the tomographic SAR data stack composed of adjacent equal-height pixels to reduce the DCS data processing dimension; The noise energy estimation module is used to estimate the noise energy at the reference pixel of the tomographic SAR data stack after dimension reduction by observing the SVD projection through MMV as the parameter of the SVD-DCS spectrum estimation model; The SVD-DCS spectrum estimation module is used to estimate the spectrum matrix after substituting the reduced-dimensional tomographic SAR data stack and reconstruct the reference pixel scattering intensity profile; The model order selection module is used to construct the model order solution model constrained by information theory through the spectrum matrix to determine the order of the tomographic SAR point cloud model; The target parameter estimation module is used to construct the target three-dimensional complex scattering coefficient optimization solution model through the spectrum matrix and estimate the scattering intensity value of the tomographic SAR point cloud; The geocoding module is used to geocode the tomographic SAR point cloud data in the range-Doppler-elevation coordinate system and output the spatial position and scattering intensity data of the tomographic SAR point cloud.

10. A computer memory, characterized in that: A computer program executable by a computer processor is stored therein, and the computer program executes a sparse array tomographic SAR three-dimensional reconstruction method based on SVD-DCS as described in any one of claims 1 to 8.

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