Double-frequency tomography SAR precise three-dimensional reconstruction method and system

Abstract

The invention provides a double-frequency tomographic SAR precise three-dimensional reconstruction method and system, and aims to solve the problems that a single-frequency tomographic SAR is difficult to reconstruct accurately and multiple false targets exist under the conditions of irregularity, sparseness and insufficient sampling, two SAR complex image data sets of different frequency bands are constructed, a double-frequency tomographic SAR joint sparse spectrum estimation model is constructed by taking adjacent equal-height pixels as prior, and the reconstruction precision of the single-frequency tomographic SAR is improved. According to the method, the scattering intensity contour of a reference pixel is obtained through reconstruction, finally, the order of a double-frequency tomography SAR point cloud model is calculated, the scattering intensity value of the double-frequency tomography SAR point cloud is estimated, geocoding is carried out to generate a double-frequency 3D point cloud, and the function of improving the density and reliability of sparse array SAR three-dimensional reconstruction is achieved. Compared with a traditional Beamforming method, a Capon method, a Music multi-view method, an SVD-Wiener method and an SL1MMER single-view method, the method provided by the invention has the highest point cloud reconstruction number; compared with the SVD-Wiener and the SL1MMER super-resolution single-view method, the method has the advantages that the side lobe noise suppression is better, and the reconstruction precision and integrity of the tomography SAR point cloud are further improved.

Description

Technical Field

[0001] The invention belongs to the technical field of high-dimensional synthetic aperture radar imaging system and data processing, and specifically relates to a dual-frequency tomography SAR precise three-dimensional reconstruction method and system. 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. Tomographic SAR, developed on the basis of 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 in 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.

[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. At the same time, by adding frequency bands to the SAR system, the sampling rate of the third dimension can be multiplied, effectively reducing the number of distributed satellites, which is of great significance for high-precision and high-efficiency three-dimensional reconstruction.

[0005] Therefore, developing a DCS-based dual-frequency tomographic SAR three-dimensional reconstruction system and method is of great significance for reducing the requirement of tomographic SAR on the number of satellites and improving the timeliness of three-dimensional reconstruction. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a dual-frequency tomography SAR precise three-dimensional reconstruction method and system for improving the density and reliability of sparse array SAR three-dimensional reconstruction.

[0007] The technical solution adopted by the present invention to solve the above technical problems is: a dual-frequency tomographic SAR precise three-dimensional reconstruction method, comprising the following steps:

[0008] S1: Obtain sparse array dual-frequency SAR complex image datasets of two different frequency bands, and perform registration based on the SAR complex images selected from each dataset that are most coherent with other SAR complex images in the same dataset to obtain SAR complex image datasets of two frequency bands; then obtain the preprocessed dual-frequency tomographic SAR complex image dataset through amplitude and phase preprocessing;

[0009] S2: With the reference pixel as the center, search and identify adjacent contour pixels in the preprocessed dual-frequency tomography SAR complex image data set, perform SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent contour pixels to obtain a reduced-dimensional dual-frequency tomography SAR data stack, calculate the noise energy of the DCS spectrum estimation model through the reduced-dimensional dual-frequency tomography SAR data stack, and reconstruct the scattering intensity profile of the reference pixel;

[0010] S3: Calculate the order of the dual-frequency tomography SAR point cloud model and the scatterer position information according to the scattering intensity profile, estimate the scattering intensity value of the dual-frequency tomography SAR point cloud, perform geocoding, and output the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

[0011] According to the above scheme, in step S1, the specific steps are:

[0012] S11: Acquire a dual-frequency SAR complex image data set, select a SAR complex image data set of one frequency band as a main data set, and select the SAR complex image that is most coherent with other SAR complex images in the same data set as a first main image; perform coherent complex image registration on the remaining dual-frequency SAR complex images in the same data set and the first main image to obtain a first dual-frequency SAR complex image data set after registration;

[0013] S12: selecting a SAR complex image dataset on the same platform as the main dataset but in a different frequency band from the acquired dual-frequency SAR complex image dataset, and selecting a SAR complex image that is most coherent with other SAR complex images in the same dataset as a second main image of another frequency band SAR complex image dataset; performing coherent complex image registration on the remaining dual-frequency SAR complex images in the same dataset and the second main image, to obtain a second dual-frequency SAR complex image dataset after registration;

[0014] S13: Input reference terrain data, take the first main image and the second main image as references, perform tomoSAR de-skew and phase error correction on the registered dual-frequency SAR complex image data sets of the two frequency bands respectively; take the first main image as reference, perform amplitude error correction on the dual-frequency SAR complex image data set; obtain the preprocessed dual-frequency tomoSAR complex image data set.

[0015] According to the above scheme, in step S2, the specific steps are:

[0016] S21: With the reference pixel as the center, within the window range defined by the preprocessed dual-frequency tomographic SAR complex image data set, the average coherence threshold and average amplitude constraint criteria are used to search and identify adjacent pixels of equal height pixel by pixel;

[0017] S22: constructing a dual-frequency tomographic SAR data stack with adjacent equal-height pixels and performing SVD decomposition to obtain a dual-frequency tomographic SAR data stack after dimensionality reduction;

[0018] S23: Substitute the dual-frequency tomographic SAR data stack after dimension reduction into the noise energy estimation model, calculate the noise energy of the DCS spectrum estimation model and use it as the noise energy size at the reference pixel;

[0019] S24: The dual-frequency tomographic SAR data stack after dimension reduction is brought into the DCS spectrum estimation model to reconstruct the scattering intensity profile of the reference pixel.

[0020] Furthermore, in step S21, the specific steps are:

[0021] S211: Based on the weighted maximum likelihood coherence estimation principle, construct a similarity measurement index between the reference pixel and the adjacent pixels;

[0022] S212: in the coherence calculation window, calculating the similarity measurement index between the reference pixel image block and the corresponding pixels of the adjacent pixel image block one by one, calculating the average similarity measurement index, and using it as the similarity measurement index between the reference pixel image block and the adjacent pixel image block;

[0023] S213: Calculate the average amplitude of dual-frequency tomographic SAR data at the reference pixel and the adjacent pixels;

[0024] S214: Identify as adjacent pixels of equal height the average amplitude of the reference pixel being greater than 0.5 times the average amplitude of the adjacent pixels, the average amplitude of the reference pixel being less than 2 times the average amplitude of the adjacent pixels, and the central pixel of the adjacent pixel image block whose similarity measurement index with the reference pixel image block is not less than the coherence threshold;

[0025] S215: Traverse all pixels as reference pixels, execute steps S211 to S214, and find adjacent pixels of equal height of all pixels.

[0026] Furthermore, in step S22, the specific steps are:

[0027] S221: decomposing the dual-frequency tomographic SAR data stack composed of adjacent equal-height pixels into subspaces corresponding to signals and noises by SVD and retaining only the signal subspace;

[0028] S222: Calculate the dual-frequency tomographic SAR data stack after dimensionality reduction through signal subspace.

[0029] According to the above scheme, in step S24, the specific steps are:

[0030] S241: Substitute the dual-frequency tomographic SAR data stack after dimension reduction to build a DCS spectrum estimation model;

[0031] S242: Estimating the spectrum matrix using a spectral gradient projection method;

[0032] S243: Calculate the scattering intensity profile of the dual-frequency tomographic SAR at the reference pixel.

[0033] According to the above scheme, in step S3, the specific steps are:

[0034] S31: bringing the peak position information of the scattering intensity profile into the scatterer number detection model of the information constraint criterion to determine the order of the dual-frequency tomography SAR point cloud model and the scatterer position information;

[0035] S32: Estimate the scattering intensity value of the dual-frequency tomographic SAR point cloud using the non-local least squares method according to the order of the model and the position information of the scatterer;

[0036] S33: geocoding the dual-frequency tomography SAR point cloud data in the range-Doppler-elevation coordinate system, and outputting the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

[0037] According to the above scheme, in step S31, the specific steps are:

[0038] S311: Construct the model order solution model constrained by information theory, i.e. the scatterer number detection model;

[0039] S312: The peak position information of the combined scattering intensity profile is combined from large to small, and the model order is substituted into the model through exhaustive method to solve the model and calculate the cost function, and the number of effective scattering targets and the elevation position corresponding to the target distribution situation with the minimum cost function are used as the number of effective targets and the elevation position information at the reference pixel.

[0040] According to the above scheme, in step S32, the specific steps are:

[0041] S321: constructing a target three-dimensional complex scattering coefficient optimization solution model;

[0042] S322: Solve the target three-dimensional complex scattering coefficient optimization solution model through the least square method, and estimate the scattering intensity value of the dual-frequency tomography SAR point cloud.

[0043] A dual-frequency tomographic SAR precise 3D reconstruction system.

[0044] The dual-frequency tomography SAR preprocessing submodule is used to obtain sparse array dual-frequency SAR complex image data sets of two different frequency bands, and to align the SAR complex images selected from each data set with the most coherent SAR complex images of other SAR complex images in the same data set to obtain SAR complex image data sets of two frequency bands; and then obtain the preprocessed dual-frequency tomography SAR complex image data sets through amplitude and phase preprocessing;

[0045] The dual-frequency tomography SAR spectrum estimation submodule is used to search and identify adjacent contour pixels in the preprocessed dual-frequency tomography SAR complex image data set with the reference pixel as the center, perform SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent contour pixels to obtain a reduced-dimensional dual-frequency tomography SAR data stack, calculate the noise energy of the DCS spectrum estimation model through the reduced-dimensional dual-frequency tomography SAR data stack, and reconstruct the scattering intensity profile of the reference pixel;

[0046] The dual-frequency 3D point cloud generation submodule is used to calculate the order of the dual-frequency tomography SAR point cloud model and the scatterer position information according to the scattering intensity profile, estimate the scattering intensity value of the dual-frequency tomography SAR point cloud, perform geocoding, and output the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

[0047] The beneficial effects of the present invention are:

[0048] 1. A dual-frequency tomography SAR precise three-dimensional reconstruction method and system of the present invention aims at the problem that single-frequency tomography SAR is difficult to accurately reconstruct and has many false targets under irregular, sparse and insufficient sampling conditions. By constructing SAR complex image data sets of two different frequency bands, a dual-frequency tomography SAR joint sparse spectrum estimation model is constructed with adjacent equi-height pixels as priors, and the scattering intensity profile of the reference pixel is reconstructed. Finally, the order of the dual-frequency tomography SAR point cloud model is calculated, the scattering intensity value of the dual-frequency tomography SAR point cloud is estimated, and geocoding is performed to generate a dual-frequency 3D point cloud, thereby achieving the function of improving the density and reliability of sparse array SAR three-dimensional reconstruction.

[0049] 2. Compared with the traditional beamforming, capon, music multi-view methods, and SVD-Wiener, SL1MMER single-view methods, the present invention has the highest number of point cloud reconstructions;

[0050] 3. Compared with the SVD-Wiener and SL1MMER super-resolution single-view methods, the present invention suppresses sidelobe noise better, thereby improving the accuracy and completeness of tomographic SAR point cloud reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of an embodiment of the present invention.

[0052] Figure 2 is an optical image of a certain building according to an embodiment of the present invention.

[0053] Figure 3 It is an X-band SAR image of a certain building in an embodiment of the present invention.

[0054] Figure 4 It is a Ku-band SAR image of a certain building in an embodiment of the present invention.

[0055] Figure 5 This is the Ku-TomoSAR SVD-Wiener point cloud reconstruction result.

[0056] Figure 6 This is the X-TomoSAR SVD-Wiener point cloud reconstruction result.

[0057] Figure 7 This is the Ku-TomoSAR SL1MMER point cloud reconstruction result.

[0058] Figure 8 This is the X-TomoSAR SL1MMER point cloud reconstruction result.

[0059] Fig. 9This is the Ku-TomoSAR Beamforming point cloud reconstruction result.

[0060] Fig.10 This is the X-TomoSAR Beamforming point cloud reconstruction result.

[0061] Fig.11 This is the Ku-TomoSAR Capon point cloud reconstruction result.

[0062] Fig.12 This is the X-TomoSAR Capon point cloud reconstruction result.

[0063] Fig.13 This is the Ku-TomoSAR MUSIC point cloud reconstruction result.

[0064] Fig.14 This is the X-TomoSAR MUSIC point cloud reconstruction result.

[0065] Fig.15 3 is a dual-frequency TomoSAR DCS point cloud reconstruction result diagram of an embodiment of the present invention.

[0066] Fig.16 It is a real result diagram of the embodiment of the present invention.

[0067] Fig.17 It is an evaluation diagram of the three-dimensional reconstruction results of the single-frequency and dual-frequency tomographic SAR methods according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0069] Example 1

[0070] See also Figure 1 ,The specific steps of a dual-frequency tomographic SAR precise three-dimensional reconstruction method are as follows:

[0071] S1: Obtain sparse array dual-frequency SAR complex image datasets of two different frequency bands, and perform registration based on the SAR complex images selected from each dataset that are most coherent with other SAR complex images in the same dataset to obtain SAR complex image datasets of two frequency bands; then obtain the preprocessed dual-frequency tomographic SAR complex image dataset through amplitude and phase preprocessing;

[0072] S2: With the reference pixel as the center, search and identify adjacent contour pixels in the preprocessed dual-frequency tomography SAR complex image data set, perform SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent contour pixels to obtain a reduced-dimensional dual-frequency tomography SAR data stack, calculate the noise energy of the DCS spectrum estimation model through the reduced-dimensional dual-frequency tomography SAR data stack, and reconstruct the scattering intensity profile of the reference pixel;

[0073] S3: Calculate the order of the dual-frequency tomography SAR point cloud model and the scatterer position information according to the scattering intensity profile, estimate the scattering intensity value of the dual-frequency tomography SAR point cloud, perform geocoding, and output the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

[0074] Furthermore, in step S1, the specific steps are:

[0075] S11: Acquire a dual-frequency SAR complex image data set, select a SAR complex image data set of one frequency band as a main data set, and select the SAR complex image that is most coherent with other SAR complex images in the same data set as a first main image; perform coherent complex image registration on the remaining dual-frequency SAR complex images in the same data set and the first main image to obtain a first dual-frequency SAR complex image data set after registration;

[0076] S12: selecting a SAR complex image dataset on the same platform as the main dataset but in a different frequency band from the acquired dual-frequency SAR complex image dataset, and selecting a SAR complex image that is most coherent with other SAR complex images in the same dataset as a second main image of another frequency band SAR complex image dataset; performing coherent complex image registration on the remaining dual-frequency SAR complex images in the same dataset and the second main image, to obtain a second dual-frequency SAR complex image dataset after registration;

[0077] S13: Input reference terrain data, take the first main image and the second main image as references, perform tomoSAR de-skew and phase error correction on the registered dual-frequency SAR complex image data sets of the two frequency bands respectively; take the first main image as reference, perform amplitude error correction on the dual-frequency SAR complex image data set; obtain the preprocessed dual-frequency tomoSAR complex image data set.

[0078] In step S2, the specific steps are:

[0079] S21: With the reference pixel as the center, within the window range defined by the preprocessed dual-frequency tomographic SAR complex image data set, the average coherence threshold and average amplitude constraint criteria are used to search and identify adjacent pixels of equal height pixel by pixel;

[0080] S22: constructing a dual-frequency tomographic SAR data stack with adjacent equal-height pixels and performing SVD decomposition to obtain a dual-frequency tomographic SAR data stack after dimensionality reduction;

[0081] S23: Substitute the dual-frequency tomographic SAR data stack after dimension reduction into the noise energy estimation model, calculate the noise energy of the DCS spectrum estimation model and use it as the noise energy size at the reference pixel;

[0082] S24: The dual-frequency tomographic SAR data stack after dimension reduction is brought into the DCS spectrum estimation model to reconstruct the scattering intensity profile of the reference pixel.

[0083] Furthermore, in step S21, the specific steps are:

[0084] S211: Based on the weighted maximum likelihood coherence estimation principle, construct a similarity measurement index between the reference pixel and the adjacent pixels;

[0085] S212: in the coherence calculation window, calculating the similarity measurement index between the reference pixel image block and the corresponding pixels of the adjacent pixel image block one by one, calculating the average similarity measurement index, and using it as the similarity measurement index between the reference pixel image block and the adjacent pixel image block;

[0086] S213: Calculate the average amplitude of dual-frequency tomographic SAR data at the reference pixel and the adjacent pixels;

[0087] S214: Identify as adjacent pixels of equal height the average amplitude of the reference pixel being greater than 0.5 times the average amplitude of the adjacent pixels, the average amplitude of the reference pixel being less than 2 times the average amplitude of the adjacent pixels, and the central pixel of the adjacent pixel image block whose similarity measurement index with the reference pixel image block is not less than the coherence threshold;

[0088] S215: Traverse all pixels as reference pixels, execute steps S211 to S214, and find adjacent pixels of equal height of all pixels.

[0089] In step S22, the specific steps are:

[0090] S221: decomposing the dual-frequency tomographic SAR data stack composed of adjacent equal-height pixels into subspaces corresponding to signals and noises by SVD and retaining only the signal subspace;

[0091] S222: Calculate the dual-frequency tomographic SAR data stack after dimensionality reduction through signal subspace.

[0092] In step S24, the specific steps are:

[0093] S241: Substitute the dual-frequency tomographic SAR data stack after dimension reduction to build a DCS spectrum estimation model;

[0094] S242: Estimating the spectrum matrix using a spectral gradient projection method;

[0095] S243: Calculate the scattering intensity profile of the dual-frequency tomographic SAR at the reference pixel.

[0096] In step S3, the specific steps are:

[0097] S31: bringing the peak position information of the scattering intensity profile into the scatterer number detection model of the information constraint criterion to determine the order of the dual-frequency tomography SAR point cloud model and the scatterer position information;

[0098] S32: Estimate the scattering intensity value of the dual-frequency tomographic SAR point cloud using the non-local least squares method according to the order of the model and the position information of the scatterer;

[0099] S33: geocoding the dual-frequency tomography SAR point cloud data in the range-Doppler-elevation coordinate system, and outputting the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

[0100] Furthermore, in step S31, the specific steps are:

[0101] S311: Construct the model order solution model constrained by information theory, i.e. the scatterer number detection model;

[0102] S312: The peak position information of the combined scattering intensity profile is combined from large to small, and the model order is substituted into the model through exhaustive method to solve the model and calculate the cost function, and the number of effective scattering targets and the elevation position corresponding to the target distribution situation with the minimum cost function are used as the number of effective targets and the elevation position information at the reference pixel.

[0103] In step S32, the specific steps are:

[0104] S321: constructing a target three-dimensional complex scattering coefficient optimization solution model;

[0105] S322: Solve the target three-dimensional complex scattering coefficient optimization solution model through the least square method, and estimate the scattering intensity value of the dual-frequency tomography SAR point cloud.

[0106] In this embodiment, the single-frequency tomography SAR is difficult to accurately reconstruct and has many false targets in irregular, sparse and under-sampling situations. By constructing a SAR complex image data set of two different frequency bands, the scattering intensity profile of the reference pixel is reconstructed by adjacent equal-height pixels and dual-frequency tomography SAR spectrum estimation. Finally, the order of the dual-frequency tomography SAR point cloud model is calculated to estimate the scattering intensity value of the dual-frequency tomography SAR point cloud, and geocoding is performed to generate a dual-frequency 3D point cloud, thereby achieving the function of improving the density and reliability of sparse array SAR three-dimensional reconstruction.

[0107] Example 2

[0108] The steps of this embodiment are the same as those of embodiment 1, except that each step is applied to a specific example. Specifically, the following steps are included:

[0109] S1: Obtain sparse array dual-frequency SAR complex image datasets of two different frequency bands, and perform registration based on the SAR complex images selected from each dataset that are most coherent with other SAR complex images in the same dataset to obtain SAR complex image datasets of two frequency bands; then obtain the preprocessed dual-frequency tomographic SAR complex image dataset through amplitude and phase preprocessing; the specific steps are as follows:

[0110] S11: collecting a dual-frequency SAR complex image data set, selecting a SAR complex image data set of one frequency band as a main data set, selecting a SAR complex image that is most coherent with other SAR complex images as a first main image; performing coherent complex image registration with the remaining dual-frequency SAR complex images to obtain a registered dual-frequency SAR complex image data set;

[0111] S12: Selecting a SAR complex image with the same platform as the main data set but with a different frequency band as the second main image of another frequency band SAR complex image data set;

[0112] S13: inputting reference terrain data, taking the first and second main images as references, respectively performing tomoSAR de-skewing and phase error correction on the SAR complex image data sets of the two frequency bands after registration; then, taking the first main image as reference, performing amplitude error correction on the dual-frequency tomoSAR complex image data set; obtaining a dual-frequency tomoSAR complex image data set with amplitude and phase preprocessing;

[0113] S2: Search and identify adjacent contour pixels in the preprocessed dual-frequency tomography SAR complex image data set, perform SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent contour pixels to obtain a reduced-dimensional dual-frequency tomography SAR data stack, calculate the noise energy of the DCS spectrum estimation model through the reduced-dimensional dual-frequency tomography SAR data stack, and reconstruct the scattering intensity profile of the reference pixel; the specific steps are:

[0114] S21: With the reference pixel as the center, within the window range defined by the preprocessed dual-frequency tomographic SAR complex image data set, the average coherence threshold and average amplitude constraint criterion method are used to search and identify adjacent pixels of equal height pixel by pixel; the specific steps are as follows:

[0115] S211: Based on the weighted maximum likelihood coherence estimation principle, construct a similarity measurement index between the reference pixel x and the adjacent pixel y

[0116]

[0117] in

[0118]

[0119] in The distribution range is between (0,1], M is the number of frames of sparse array SAR images, x i (m) represents the complex value of the mth preprocessed dual-frequency tomographic SAR image 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 dual-frequency tomographic SAR image. * Represents the conjugation operation of a complex number.

[0120] S212: In the coherent calculation window, the similarity measurement index between the reference pixel image block and the corresponding pixels of the adjacent pixel image block is calculated one by one, and the average similarity measurement index is calculated, which is used as the similarity measurement index between the reference pixel image block and its adjacent pixel image block:

[0121]

[0122] Where N is the total number of pixels in the SAR image block.

[0123] S213: Calculate the average amplitude A of the dual-frequency tomographic SAR data at the reference pixel and its adjacent pixels x , A y .

[0124] S214: 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 not less than C 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 =0.3.

[0125] S215: Traverse all pixels as reference pixels, and find the adjacent pixels of equal height of all pixels according to steps 4.1 to 4.4.

[0126] S22: performing SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent equal-height pixels to obtain the dual-frequency tomography SAR data stack after the data dimension is reduced; the specific steps are:

[0127] S221: Decompose the dual-frequency tomographic SAR data stack Y consisting of adjacent equal-height pixels into subspaces corresponding to signals and noises by SVD and only retain the signal subspace, that is,

[0128] Y=USV H=[U M×K ,U M×(M-K) ]S M×L [V L×K ,V L×(L-K) ] H (4)

[0129] 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.

[0130] S222: Order Among them I K×K is the K×K unit matrix, 0 (L-K)×K is a zero matrix of (LK)×L; the dual-frequency tomographic SAR data stack after dimension reduction is calculated as

[0131] S23: Substituting the dimension-reduced dual-frequency tomographic SAR data stack into the noise energy estimation model to obtain the noise energy of the DCS spectrum estimation model;

[0132] The formula for calculating noise energy is as follows:

[0133]

[0134] in Represents the dual-frequency tomographic SAR data stack after dimensionality reduction The lth column vector of is used as the energy of the noise at the reference pixel, which is used for the noise energy constraint of DCS spectrum estimation and the order determination of the dual-frequency tomography SAR point cloud model.

[0135] S24: Bring the dimension-reduced dual-frequency tomographic SAR data stack into the DCS spectrum estimation model to reconstruct the scattering intensity profile of the reference pixel;

[0136] The steps of dual-frequency tomographic SAR spectrum estimation based on DCS are:

[0137] S241: Substitute the reduced-dimensional dual-frequency tomographic SAR data stack to build a DCS spectrum estimation model:

[0138]

[0139] where || || 2,1 、|| || F They represent the L1-L2 mixed norm and F norm of the matrix respectively. A1 and A2 are the observation matrices of tomographic SAR in two frequency bands respectively.

[0140] S242: Estimate the frequency spectrum matrix X using a spectral gradient projection method.

[0141] S243: Give the scattering intensity result of dual-frequency tomographic SAR at the final reference pixel in Represents the estimated spectrum matrix The i-th row vector of .

[0142] S3: Calculate the dual-frequency tomography SAR point cloud model order and scatterer position information according to the scattering intensity profile, estimate the dual-frequency tomography SAR point cloud scattering intensity value, perform geocoding, and output the dual-frequency tomography SAR point cloud spatial position and scattering intensity data; the specific steps are:

[0143] S31: Bring the peak position information of the scattering intensity profile into the scatterer number detection model of the information constraint criterion to determine the order of the dual-frequency tomography SAR point cloud model and the scatterer position information; the specific steps are:

[0144] S311: Constructing a model for solving the model order of information theory constraints:

[0145]

[0146] in is the number of valid targets at the reference pixel, z is the previous The location of the maximum spectral peak, Indicates the dual-frequency tomographic SAR The three-dimensional scattering coefficient corresponding to the maximum spectrum peak position, and the information constraint term adopts the BIC and MDL models:

[0147]

[0148] Where c is the regularization parameter for model order discrimination.

[0149] S312: 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 situation with the minimum cost function are used as the number of effective targets at the reference pixel. And elevation position information.

[0150] S32: Estimate the scattering intensity value of the dual-frequency tomography SAR point cloud using the non-local least squares method according to the model order and the scatterer position information; the specific steps are:

[0151] S321: Construct the target three-dimensional complex scattering coefficient optimization solution model:

[0152]

[0153] S322: Solve the above equation by the least square method and estimate the scattering intensity value of the dual-frequency tomography SAR point cloud by the following equation:

[0154]

[0155] S33: geocoding the dual-frequency tomography SAR point cloud data in the range-Doppler-elevation coordinate system, and outputting the dual-frequency tomography SAR point cloud spatial position and scattering intensity data.

[0156] 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.

[0157] The Bird's Nest simulation data set includes 4 X-band and 4 Ku-band SAR data sets. Figure 2 is an optical image of a building, Figure 3 , Figure 4 They are X- and Ku-band SAR images, respectively. The left side shows the building mask pixels, and the central pixel circled by the white frame is the simulation control point, which is used to objectively evaluate the building elevation reconstruction accuracy and completeness of different tomographic SAR 3D reconstruction algorithms.

[0158] Using X- and Ku-band SAR data sets, and comparing with several classic single-frequency tomographic SAR 3D reconstruction methods, the superiority of the proposed algorithm in sparse array tomographic SAR 3D reconstruction is verified. Figure 5-16 The three-dimensional reconstruction results of several classic methods in X and Ku bands and the dual-frequency tomographic SAR method of this embodiment are shown in Figure 2. By comparison, it can be seen that in the case of X and Ku single-band tomographic SAR, the single-view SVD-Wiener ( Figure 5 , 6 ), based on CS's German Aerospace SL1MMER ( Figure 7 , 8 ) method has good reconstruction coverage, but it is accompanied by a large number of false targets; multi-view Beamforming ( Fig. 9 , 10 )、Capon( Fig.11 , 12 )、Music( Fig.13 , 14 ) method greatly suppressed the sidelobe noise and reduced the false targets, but the reconstruction coverage was poor, especially in Fig.16 The ellipse marks the area; and the dual-frequency weighted DCS method of this embodiment ( Fig.15 ) not only has the best tomographic SAR reconstruction accuracy and reconstruction completeness, but also the sidelobe noise and false targets are effectively suppressed. Fig.17 The table in compares the evaluation results of 3D reconstruction by single-frequency and dual-frequency tomographic SAR methods. It can be seen that all index results of the dual-frequency DCS method proposed in this embodiment are better than those of single-frequency tomographic SAR.

[0159] Example 3

[0160] This embodiment is used to implement the principle construction system of the above method embodiment, including a dual-frequency tomography SAR preprocessing submodule, a dual-frequency tomography SAR spectrum estimation submodule and a dual-frequency 3D point cloud generation submodule.

[0161] The dual-frequency tomography SAR preprocessing submodule is used to obtain sparse array dual-frequency SAR complex image data sets of two different frequency bands, and to align the SAR complex images selected from each data set with the most coherent SAR complex images of other SAR complex images in the same data set to obtain SAR complex image data sets of two frequency bands; and then obtain the preprocessed dual-frequency tomography SAR complex image data sets through amplitude and phase preprocessing;

[0162] The dual-frequency tomography SAR spectrum estimation submodule is used to search and identify adjacent contour pixels in the preprocessed dual-frequency tomography SAR complex image data set with the reference pixel as the center, perform SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent contour pixels to obtain a reduced-dimensional dual-frequency tomography SAR data stack, calculate the noise energy of the DCS spectrum estimation model through the reduced-dimensional dual-frequency tomography SAR data stack, and reconstruct the scattering intensity profile of the reference pixel;

[0163] The dual-frequency 3D point cloud generation submodule is used to calculate the order of the dual-frequency tomography SAR point cloud model and the scatterer position information according to the scattering intensity profile, estimate the scattering intensity value of the dual-frequency tomography SAR point cloud, perform geocoding, and output the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

[0164] Each sub-module is mainly used to implement each step of the method embodiment, which will not be described in detail here.

[0165] It should be pointed out that, according to the needs of implementation, the various steps / components described in this application can be split into more steps / components, and two or more steps / components or partial operations of steps / components can be combined into new steps / components to achieve the purpose of the present invention.

[0166] This embodiment also includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a computer program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a dual-frequency tomography SAR precise three-dimensional reconstruction method.

[0167] This embodiment also provides a computer-readable storage medium on which executable instructions are stored. When the instructions are executed by a processor, the processor implements a dual-frequency tomography SAR precise three-dimensional reconstruction method.

[0168] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0169] Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0170] The present application is described with reference to the block diagram of the device (system) according to Embodiment 1 of the present application and the flowchart of the method and computer program product according to Embodiment 2. It should be understood that each process or box in the flowchart or block diagram, and the combination of the processes or boxes in the flowchart or block diagram can be implemented by computer program instructions.

[0171] These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process Figure 1 A process or multiple processes or boxes Figure 1 A system of functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes or boxes Figure 1 A function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes or boxes Figure 1 The steps of a dual-frequency tomography SAR precise three-dimensional reconstruction method are specified in one or more boxes.

[0174] 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 dual-frequency tomographic SAR precise three-dimensional reconstruction method, characterized by: The following steps are involved: S1: Obtain sparse array dual-frequency SAR complex image datasets of two different frequency bands, and perform registration based on the SAR complex images selected from each dataset that are most coherent with other SAR complex images in the same dataset to obtain SAR complex image datasets of two frequency bands; then obtain the preprocessed dual-frequency tomographic SAR complex image dataset through amplitude and phase preprocessing; S2: With the reference pixel as the center, search and identify adjacent contour pixels in the preprocessed dual-frequency tomography SAR complex image data set, perform SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent contour pixels to obtain a reduced-dimensional dual-frequency tomography SAR data stack, calculate the noise energy of the DCS spectrum estimation model through the reduced-dimensional dual-frequency tomography SAR data stack, and reconstruct the scattering intensity profile of the reference pixel; S3: Calculate the order of the dual-frequency tomography SAR point cloud model and the scatterer position information according to the scattering intensity profile, estimate the scattering intensity value of the dual-frequency tomography SAR point cloud, perform geocoding, and output the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

2. The dual-frequency tomographic SAR precise three-dimensional reconstruction method according to claim 1, characterized in that: In the step S1, the specific steps are: S11: Acquire a dual-frequency SAR complex image data set, select a SAR complex image data set of one frequency band as a main data set, and select the SAR complex image that is most coherent with other SAR complex images in the same data set as a first main image; perform coherent complex image registration on the remaining dual-frequency SAR complex images in the same data set and the first main image to obtain a first dual-frequency SAR complex image data set after registration; S12: selecting a SAR complex image dataset on the same platform as the main dataset but in a different frequency band from the acquired dual-frequency SAR complex image dataset, and selecting a SAR complex image that is most coherent with other SAR complex images in the same dataset as a second main image of another frequency band SAR complex image dataset; performing coherent complex image registration on the remaining dual-frequency SAR complex images in the same dataset and the second main image, to obtain a second dual-frequency SAR complex image dataset after registration; S13: inputting reference terrain data, taking the first main image and the second main image as references, respectively performing tomoSAR de-skewing and phase error correction on the registered dual-frequency SAR complex image data sets of the two frequency bands; taking the first main image as reference, performing amplitude error correction on the dual-frequency SAR complex image data set; The preprocessed dual-frequency tomographic SAR complex image data set is obtained.

3. The dual-frequency tomographic SAR precise 3D reconstruction method according to claim 1, characterized in that: In the step S2, the specific steps are: S21: With the reference pixel as the center, within the window range defined by the preprocessed dual-frequency tomographic SAR complex image data set, the average coherence threshold and average amplitude constraint criteria are used to search and identify adjacent pixels of equal height pixel by pixel; S22: constructing a dual-frequency tomographic SAR data stack with adjacent equal-height pixels and performing SVD decomposition to obtain a dual-frequency tomographic SAR data stack after dimensionality reduction; S23: Substitute the dual-frequency tomographic SAR data stack after dimension reduction into the noise energy estimation model, calculate the noise energy of the DCS spectrum estimation model and use it as the noise energy size at the reference pixel; S24: The dual-frequency tomographic SAR data stack after dimension reduction is brought into the DCS spectrum estimation model to reconstruct the scattering intensity profile of the reference pixel.

4. The dual-frequency tomographic SAR precise three-dimensional reconstruction method according to claim 3, characterized in that: In the step S21, the specific steps are: S211: Based on the weighted maximum likelihood coherence estimation principle, construct a similarity measurement index between the reference pixel and the adjacent pixels; S212: in the coherence calculation window, calculating the similarity measurement index between the reference pixel image block and the corresponding pixels of the adjacent pixel image block one by one, calculating the average similarity measurement index, and using it as the similarity measurement index between the reference pixel image block and the adjacent pixel image block; S213: Calculate the average amplitude of the dual-frequency tomographic SAR data at the reference pixel and the adjacent pixels; S214: Identify as adjacent pixels of equal height the average amplitude of the reference pixel being greater than 0.5 times the average amplitude of the adjacent pixels, the average amplitude of the reference pixel being less than 2 times the average amplitude of the adjacent pixels, and the central pixel of the adjacent pixel image block whose similarity measurement index with the reference pixel image block is not less than the coherence threshold; S215: Traverse all pixels as reference pixels, execute steps S211 to S214, and find adjacent pixels of equal height of all pixels.

5. The dual-frequency tomographic SAR precise 3D reconstruction method according to claim 3, characterized in that: In the step S22, the specific steps are: S221: decomposing the dual-frequency tomographic SAR data stack composed of adjacent equal-height pixels into subspaces corresponding to signals and noises by SVD and retaining only the signal subspace; S222: Calculate the dual-frequency tomographic SAR data stack after dimensionality reduction through signal subspace.

6. The dual-frequency tomographic SAR precise three-dimensional reconstruction method according to claim 1, characterized in that: In the step S24, the specific steps are: S241: Substitute the dual-frequency tomographic SAR data stack after dimension reduction to build a DCS spectrum estimation model; S242: Estimating the spectrum matrix using a spectral gradient projection method; S243: Calculate the scattering intensity profile of the dual-frequency tomographic SAR at the reference pixel.

7. The dual-frequency tomographic SAR precise three-dimensional reconstruction method according to claim 1, characterized in that: In the step S3, the specific steps are: S31: bringing the peak position information of the scattering intensity profile into the scatterer number detection model of the information constraint criterion to determine the order of the dual-frequency tomography SAR point cloud model and the scatterer position information; S32: Estimate the scattering intensity value of the dual-frequency tomographic SAR point cloud using the non-local least squares method according to the order of the model and the position information of the scatterer; S33: geocoding the dual-frequency tomography SAR point cloud data in the range-Doppler-elevation coordinate system, and outputting the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

8. The dual-frequency tomographic SAR precise three-dimensional reconstruction method according to claim 1, characterized in that: In the step S31, the specific steps are: S311: Construct the model order solution model constrained by information theory, i.e. the scatterer number detection model; S312: The peak position information of the combined scattering intensity profile is combined from large to small, and the model order is substituted into the model through exhaustive method to solve the model and calculate the cost function, and the number of effective scattering targets and the elevation position corresponding to the target distribution situation with the minimum cost function are used as the number of effective targets and the elevation position information at the reference pixel.

9. The dual-frequency tomographic SAR precise three-dimensional reconstruction method according to claim 1, characterized in that: In the step S32, the specific steps are: S321: constructing a target three-dimensional complex scattering coefficient optimization solution model; S322: Solve the target three-dimensional complex scattering coefficient optimization solution model through the least square method, and estimate the scattering intensity value of the dual-frequency tomography SAR point cloud.

10. A dual-frequency tomographic SAR precise three-dimensional reconstruction system, characterized by: The dual-frequency tomography SAR preprocessing submodule is used to obtain sparse array dual-frequency SAR complex image data sets of two different frequency bands, and to align the SAR complex images selected from each data set with the most coherent SAR complex images of other SAR complex images in the same data set to obtain SAR complex image data sets of two frequency bands; and then obtain the preprocessed dual-frequency tomography SAR complex image data sets through amplitude and phase preprocessing; The dual-frequency tomography SAR spectrum estimation submodule is used to search and identify adjacent contour pixels in the preprocessed dual-frequency tomography SAR complex image data set with the reference pixel as the center, perform SVD decomposition on the dual-frequency tomography SAR data stack composed of adjacent contour pixels to obtain a reduced-dimensional dual-frequency tomography SAR data stack, calculate the noise energy of the DCS spectrum estimation model through the reduced-dimensional dual-frequency tomography SAR data stack, and reconstruct the scattering intensity profile of the reference pixel; The dual-frequency 3D point cloud generation submodule is used to calculate the order of the dual-frequency tomography SAR point cloud model and the scatterer position information according to the scattering intensity profile, estimate the scattering intensity value of the dual-frequency tomography SAR point cloud, perform geocoding, and output the spatial position and scattering intensity data of the dual-frequency tomography SAR point cloud.

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