Image phase error compensation method and device, computer equipment and storage medium

Abstract

The invention belongs to the technical field of SAR image correction, and discloses an image phase error compensation method and device, computer equipment and a storage medium, and the method comprises the steps: S1, selecting a plurality of strong scattering points according to energy and contrast; s2, screening each strong scattering point to obtain a target scattering point; s3, calculating candidate scattering points according to a preset distance range and a preset azimuth range; s4, screening the candidate scattering point indexes according to a preset threshold value and a preset window length gap to obtain screening result points; s5, processing the screening result points point by point to obtain effective result points; s6, processing the effective result points to obtain effective parameters; s7, obtaining a compensation main image and a compensation auxiliary image according to the effective parameters; and S8, circulating the steps S1-S7, and outputting the compensation main image and the compensation auxiliary image after a preset number of iterations is reached. The stability of the interferometric phase can be maintained, and the quality of the output image is improved.

Description

Technical Field

[0001] This application relates to the field of SAR image correction technology, and in particular to an image phase error compensation method, apparatus, computer equipment and storage medium. Background Technology

[0002] Currently, in the field of airborne interferometric processing, the PGA algorithm is generally not used for SAR image processing. The PGA algorithm causes phase distortions in the primary and secondary images, leading to decoherence and disorder in the interference fringes. However, defocused SAR images also affect the subsequent interference phase, introducing errors. To address this issue, some research has proposed that when the motion error does not exceed one-eighth of a wavelength, the phase error estimated by the self-focusing of the primary image can be used to compensate for the same amount of error in both channels, improving the focusing performance of the primary and secondary images without affecting the interference phase. However, when the motion error exceeds this threshold, existing methods struggle to maintain the stability of the interference phase while improving the focusing performance of the primary and secondary images. Summary of the Invention

[0003] This application provides an image phase error compensation method, apparatus, computer device, and storage medium, which can establish the relationship between multiple preset conditions and the phase errors of the main image and auxiliary image under the interference mode, and use the phase errors obtained from the two images to compensate for the main image and auxiliary image; in scenarios with large motion errors, it can not only improve the focusing performance of the main image and auxiliary image, but also effectively maintain the stability of the interference phase, thereby improving the quality of the output main image and auxiliary image.

[0004] In a first aspect, embodiments of this application provide an image phase error compensation method, the method comprising:

[0005] Step S1: Select multiple strong scattering points from the preset main image and preset auxiliary image based on energy and contrast;

[0006] Step S2: Select each strong scattering point according to preset constraints to obtain the target scattering point; wherein, the target scattering point includes the target scattering point of the main image and the target scattering point of the auxiliary image;

[0007] Step S3: Calculate candidate scattering points that meet the preset error range among the target scattering points in the main image and the target scattering points in the auxiliary image, based on the preset distance range and preset orientation range;

[0008] Step S4: Filter candidate scattering point indices based on the difference between a preset threshold and a preset window length to obtain the filtered result points;

[0009] Step S5: Process and filter the result points one by one to obtain the valid result points;

[0010] Step S6: Process valid result points to obtain valid parameters;

[0011] Step S7: Calculate the phase error corresponding to the preset main image and the preset auxiliary image based on the effective parameters, compensate the phase error into the corresponding preset main image and the preset auxiliary image and perform azimuth compression to obtain the compensated main image and the compensated auxiliary image.

[0012] Step S8 repeats steps S1-S7 until the preset number of iterations is reached, then outputs the compensated main image and the compensated auxiliary image.

[0013] Furthermore, based on preset constraints, each strong scattering point is filtered to obtain the target scattering points, including:

[0014] Check if the candidate point is a maximum value within the preset window range; if not, delete the candidate point.

[0015] Check whether the main lobe widths in the distance and azimuth directions of the candidate points meet the preset limits; if not, delete the candidate points.

[0016] Check if the main lobe-to-side lobe ratio of the candidate points reaches the preset value; if not, delete the candidate points.

[0017] The remaining strong scattering points are taken as target scattering points.

[0018] Further, point-by-point processing includes:

[0019] The windowing, circular displacement, phase decompression, phase gradient calculation, smoothing and noise reduction, and weight assignment operations are performed sequentially.

[0020] Furthermore, valid result points are processed to obtain valid parameters, including:

[0021] Determine whether the number of valid result points has reached the preset number of valid points;

[0022] If so, then a weighted least squares estimation is performed on a preset number of valid result points to obtain the first parameter;

[0023] Integrating the first parameter yields the effective parameter.

[0024] Furthermore, a weighted least squares estimation is performed on a preset number of valid result points to obtain the first parameter, including:

[0025] Substitute the valid result points into the preset weighted least squares estimation formula to obtain the first parameter;

[0026] The pre-defined weighted least squares estimation formula is as follows:

[0027] B = (A T WA) -1 A T WΦ

[0028] Where B is the first parameter; W = diag[w1,…,w N [ ] is the diagonal matrix of weighting factors, w n is the weight of the nth valid result point; A is the dual-channel joint phase error coefficient matrix constructed based on the lower viewpoint of the valid result points.

[0029] Furthermore, the phase error corresponding to the preset main image and the preset auxiliary image is calculated based on the effective parameters, including:

[0030] Substitute the effective parameters into the preset formula and preset distance range and calculate to obtain the phase error corresponding to the preset main image and preset auxiliary image.

[0031] Furthermore, the preset distance range and preset orientation range are determined based on the geometric relationship between the preset main image and the preset auxiliary image;

[0032] The geometric relationships include resolution and imaging parameters.

[0033] Secondly, embodiments of this application provide an image phase error compensation device, the device comprising:

[0034] The selection module allows you to choose multiple strong scattering points from a preset main image and a preset secondary image based on energy and contrast.

[0035] The filtering module filters each strong scattering point according to preset constraints to obtain target scattering points; among which, target scattering points include target scattering points in the main image and target scattering points in the auxiliary image;

[0036] The calculation module calculates candidate scattering points that meet the preset error range among the target scattering points in the main image and the target scattering points in the auxiliary image, based on the preset distance range and preset orientation range.

[0037] The secondary filtering module filters candidate scattering point indices based on a preset threshold and a preset window length difference, and obtains the filtered result points.

[0038] The point-by-point processing module processes and filters result points one by one to obtain valid result points;

[0039] The secondary processing module processes valid result points to obtain valid parameters;

[0040] The orientation compression module calculates the phase error corresponding to the preset main image and the preset auxiliary image based on the effective parameters, compensates the phase error into the preset main image and the preset auxiliary image and performs orientation compression to obtain the compensated main image and the compensated auxiliary image.

[0041] The loop module executes the selection module and the orientation compression module in a loop. After reaching the preset number of iterations, it outputs the compensated main image and the compensated auxiliary image.

[0042] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the steps of the image phase error compensation method as described in any of the above embodiments.

[0043] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the image phase error compensation method as described in any of the above embodiments.

[0044] In summary, compared with the prior art, the beneficial effects of the technical solution provided in this application include at least the following:

[0045] The image phase error compensation method provided in this application embodiment can establish the relationship between multiple preset conditions and the phase errors of the main image and auxiliary image under the interference mode, and use the phase error obtained from the two images to compensate for the main image and auxiliary image; in scenarios with large motion errors, it can not only improve the focusing performance of the main image and auxiliary image, but also effectively maintain the stability of the interference phase, thereby improving the quality of the output main image and auxiliary image. Attached Figure Description

[0046] Figure 1 A flowchart of an image phase error compensation method provided as an exemplary embodiment of this application.

[0047] Figure 2 This is a schematic diagram of the cross-track interference geometry of an image phase error compensation method provided as an exemplary embodiment of this application when attitude error exists.

[0048] Figure 3 This is a structural diagram of an image phase error compensation device provided as an exemplary embodiment of this application. Detailed Implementation

[0049] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0050] Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] Please see Figure 1 This application provides an image phase error compensation method, which specifically includes the following steps:

[0052] Step S1: Select multiple strong scattering points from the preset main image and preset auxiliary image based on energy and contrast.

[0053] The strategy for selecting strong scattering points includes global preliminary selection and secondary screening: First, a comprehensive evaluation using energy and contrast criteria is conducted across the entire image to perform global preliminary selection, identifying scattering points with strong energy and contrast. Then, the initially selected target points are traversed for secondary screening, filtering out those that do not meet the requirements through various constraints.

[0054] "Stronger" energy is calculated based on the histogram distribution of a specific image, selecting the highest-energy portions (e.g., the top 0.1%). The specific value is dynamically determined based on the image characteristics. "Stronger" contrast is determined by comparing the energy ratio of the central region to its surrounding regions. For example, when the energy of the central region is more than 10 times higher than the surrounding regions, it is considered to have "stronger" contrast. The specific value depends on the image characteristics and target distribution. The 0.1% and 10 times values ​​here are empirical values ​​determined based on specific image data and can be fine-tuned in actual processing.

[0055] In this application, the preset primary image and preset secondary image refer to two SAR images acquired separately by a dual-channel antenna in an interferometric SAR system. The preset primary image is generated from radar echo data acquired by the primary channel antenna, and the preset secondary image is generated from radar echo data acquired by the secondary channel antenna. These two images are spatially correlated and are used for interferometric processing.

[0056] The optimized image phase error compensation method is mainly applied to the following scenarios:

[0057] First, in interferometric SAR system scenarios with large motion errors: for example, in UAV platforms or airborne SAR systems with complex flight trajectories and frequent attitude changes, motion errors can cause phase mismatch and image decoherence.

[0058] Second, scenarios requiring high interferometric phase quality: such as high-precision topographic mapping and surface deformation monitoring, where the stability and quality of the interferometric phase are critical. Traditional PGA algorithms may lead to severe interferometric fringe distortion, while optimized algorithms can improve fringe clarity and phase consistency.

[0059] Step S2: Select each strong scattering point according to preset constraints to obtain the target scattering point; wherein, the target scattering point includes the target scattering point of the main image and the target scattering point of the auxiliary image.

[0060] Specifically, in some embodiments, target scattering points are obtained by filtering each strong scattering point according to preset constraints, including:

[0061] Step S21: Check if the candidate point is a maximum value within the preset window range; if not, delete the candidate point.

[0062] Step S22: Check whether the main lobe widths of the candidate points in the distance and azimuth directions meet the preset limits; if not, delete the candidate points.

[0063] Step S23: Check whether the main lobe-to-side lobe ratio of the candidate point reaches the preset value; if not, delete the candidate point.

[0064] Step S24: Take the remaining strong scattering points as target scattering points.

[0065] The term "local" is defined by a preset window range. The value of this window range typically corresponds to the defocus length of each candidate point in the range and azimuth directions, reflecting the spatial radiation range of the main energy of each candidate point in these two directions. If the normalized value of a candidate point is not the maximum value within this window range, it is discarded.

[0066] Candidate points with excessively large main lobe widths do not meet the requirements. The main lobe width is determined by the energy distribution range of the candidate point's peak power relative to the attenuation threshold. If the main lobe width in the range or azimuth direction exceeds the limit, it indicates that the point is too defocused or is not an isolated scattering point, meaning that the point is not a high-quality candidate point and is therefore eliminated.

[0067] Points of poor quality did not meet the preset value. For example, candidate points with a main lobe-to-side lobe ratio less than the threshold were all eliminated. This indicates that the energy difference between the main lobe and the side lobe is not significant, and also indicates that the candidate point is severely defocused and is not a high-quality, isolated, strong scattering point. This avoids misselection due to energy diffusion or defocus.

[0068] Step S3: Calculate candidate scattering points that meet the preset error range among the target scattering points in the main image and the target scattering points in the auxiliary image, based on the preset distance range and preset orientation range.

[0069] The preset distance range and preset orientation range are determined by the geometric offsets of the distance and orientation dimensions, respectively. The specific values ​​are dynamically set based on the geometric relationship between the primary and secondary images (such as resolution and imaging parameters). The preset error range is typically between a few pixels and tens of pixels, for example, 20 pixels.

[0070] In practical processing, the following issues need to be considered: First, due to the geometric offset between the preset main image and the preset auxiliary image, the image point position of the same ground target in the image may deviate; second, due to differences in imaging conditions, the feature response of the same scattering target in the preset main image and the preset auxiliary image may be inconsistent. Therefore, when selecting target scattering points in the main image and the auxiliary image, it is necessary to select and match points under certain constraints.

[0071] Step S4: Filter candidate scattering point indices based on the difference between preset threshold and preset window length to obtain the filtered result points.

[0072] The preset window length difference is determined by imaging conditions and target characteristics. The threshold typically sets to reflect the degree of defocusing and the difference in feature response between candidate scattering points in the preset main image and preset auxiliary image; the specific value depends on the actual application scenario. An excessively large difference indicates inconsistent responses between candidate scattering points in the preset main image and preset auxiliary image, requiring their removal. The preset threshold is usually between a few pixels and tens of pixels, for example, 50 pixels.

[0073] Based on the matching of preset distance range and preset azimuth range, a preset window length difference constraint is further introduced to filter the candidate scattering point index and remove the candidate scattering point index with an excessively large window length difference: In the preset distance range and preset azimuth range, the preset distance range, preset azimuth range and preset window length difference of the current candidate scattering point and the candidate scattering point index are compared respectively. If the preset window length difference exceeds the preset threshold, the current candidate scattering point is removed.

[0074] Step S5: Process the filtered result points one by one to obtain valid result points.

[0075] The point-by-point processing includes sequentially performing windowing, circular displacement, phase decompression, phase gradient calculation, smoothing and noise reduction, and weight allocation operations.

[0076] Specifically, the windowing operation includes: for each filtered result point, firstly, taking that filtered result point as the center, extracting a window of data in the azimuth direction, and setting the data outside this window range to zero. An adaptive window selection strategy based on energy decay is adopted, defining the window boundary as the critical point where energy decays to a preset threshold.

[0077] The circular displacement operation includes: calculating the energy centroid of the window data and performing a circular displacement on the data to move the energy centroid to the beginning of the data.

[0078] Phase decompression includes: restoring the signal to its state before azimuth compression; performing a Fourier transform (FFT) on the window data; multiplying by the decompression function; and then performing an inverse Fourier transform (IFFT) to restore the time-domain signal. The expression for the decompression function is:

[0079]

[0080] In the formula, H unpress (k) is the decompression function, R0 is the slant range when the radar is closest to the target, and V r f is the flight speed. f0 is the carrier frequency of the signal. D represents the migration parameter in the range-Doppler domain.a Let f be the frequency coordinates of the azimuth spectrum, satisfying f a =(-N a / 2:1:N a / 2-1)·PRF / N a +f dc , where f dc N is the Doppler center frequency. a The number of sampling points is azimuth, and PRF is the pulse repetition frequency.

[0081] The phase gradient calculation operation includes: selecting a signal interval within the synthetic aperture time frame centered on the centroid of the target signal, calculating the phase gradient, and accumulating these gradients to obtain the phase error curve. The signal within this interval is denoted as I. s The formula for calculating the phase gradient Δφ(k) is as follows:

[0082] Δφ(k)=∠(conj(I s (k))·I s (k+1)), k=1,2,...,L-1, (2)

[0083] Among them, I s (k) represents the complex value of the kth sampling point, conj represents the complex conjugate operation, ∠ represents taking the phase (angle), and L represents the total length of the signal in this interval.

[0084] The smoothing and noise reduction operation includes: smoothing the phase error curve to eliminate the influence of noise and preserve local variation characteristics. Then, the phase gradient is recalculated on the smoothed phase error curve.

[0085] The weight allocation operation includes: calculating the contribution intensity of each selected result point based on the intensity distribution of the interval signal and the window size, combined with the intensity characteristics of the strong scattering points, allocating weights to obtain effective result points, which are then used for subsequent weighted least squares estimation.

[0086] To facilitate phase-weighted least squares estimation, during the process of traversing all selected result points and calculating the phase gradient, the calculated phase gradient and its corresponding weights need to be stored in N. a ×N r In the matrix (N) a N r These are the number of azimuth points and the number of range points, respectively, and are denoted by Φ(a,r) and C(a,r). Their placement is determined based on the range gate of the selected point and the azimuth gate covered by its synthetic aperture. Regions in the matrix not covered by any selected point are uniformly set to null values ​​and considered invalid data.

[0087] Step S6: Process the valid result points to obtain valid parameters.

[0088] Before estimation, it is necessary to establish the relationship between motion parameters, attitude parameters, and the phase errors of preset main and secondary images based on the geometric model. Here, taking the interferometric mode as an example, the mathematical relationship between phase error and motion parameters and attitude parameters is derived.

[0089] When describing aircraft attitude, the pitch angle θ is often used. p Roll angle θ r and yaw angle θ y These attitude angles define the aircraft's three-dimensional rotation. Using these attitude angles, a rotation matrix can be constructed from the body coordinate system to the global inertial coordinate system. The attitude angles are defined as follows:

[0090] Yaw angle θ y : Describes the rotation of the aircraft around the Z-axis of the fuselage, which affects the aircraft's direction in the horizontal direction.

[0091] Pitch angle θ p : Describes the rotation of the aircraft around the Y-axis of the fuselage, which affects the aircraft's orientation in the vertical direction.

[0092] Roll angle θ r : Describes the rotation of the aircraft around the X-axis of the fuselage, which affects the degree of tilt on both sides of the aircraft.

[0093] Attitude rotation is represented by three basic rotation matrices, θ y ,θ p ,θ r The corresponding rotation matrix is ​​represented as follows:

[0094]

[0095] Rotating in the order of yaw-pitch-roll, the total rotation matrix R can be obtained as follows:

[0096]

[0097] A schematic diagram of the cross-track interference geometry when attitude error exists is shown below. Figure 2 As shown. Using the XYZ coordinate system, the platform moves along the X direction, Y is the side view direction, and Z is the direction the fuselage points upwards. Assume the POS is installed at the center of the platform, as... Figure 2 In the diagram, point O, A1, and A2 represent the phase centers of the two antennas, R1 and R2 represent the distances from the ground target point to the ideal positions of antennas 1 and 2, R′1 and R′2 represent the distances from the ground target point to the actual positions of antennas 1 and 2, H is the height of the flight platform relative to the reference plane, and P is the ground target point.

[0098] Let the distance between the two cross-track antennas be the cross-track baseline B. XThe baseline tilt angle is α. Taking point O, where POS is located, as the dividing point, the baseline components corresponding to the two antennas are B and B, respectively. X1 B X2 Assuming the initial coordinates of POS are [0,0,0], then the initial positions of antennas 1 and 2 are S and S, respectively. X1 =[0,B X1 cosα,B X1 sinα] and S X2 =[0,-B X2 cosα,-B X2 [sinα]. Due to the introduction of the attitude angle, the positions of the cross-track antennas 1 and 2 will change dynamically, and the changed position S′ X1 ,S′ X2 The expression is as follows:

[0099]

[0100] When the carrier platform begins to move, its own motion error and the offset introduced by the attitude angle are the two main factors affecting the position change of the interferometric antenna. Let the ideal position of POS be T(η) = [x(η), y(η), z(η)]. T The actual position is T. r (η)=[x r (η),y r (η),z r (η)] T =[x(n)+Δx,y(n)+Δy,z(n)+Δz] T Where Δx, Δy, and Δz represent the platform's own motion error, which varies with azimuth and time η. The ideal positions of antennas 1 and 2 are as follows:

[0101] S X1 (η)=[x(η), y(η)+B X1 cosα, z(η)+B X1 sinα] T (9)

[0102] S X2 (η)=[x(η), y(η)-B X2 cosα, z(η)-B X2 sinα] T (10)

[0103] After superimposing the attitude error, the actual positions of antennas 1 and 2 can be represented as:

[0104]

[0105] The slant range error is equivalent to the projection of the position vector between the actual and ideal tracks onto the radar line of sight, which can be expressed as:

[0106] ΔR X1 =-sinθ sq (Δx+B X1 cosα(cosθ p sinθ y +sinθ p sinθ r cosθ y )-B X1 sinαsinθ p cosθ r )

[0107] -sin(β)cosθ sq (Δy+B X1 cosαcosθ r cosθ y +B X1 sinαsinθ r -B X1 cosα) +cos(β)cosθ sq (Δz+B X1 cosα(sinθ p sinθ y -cosθ p sinθ r cosθ y )+B X1 sinαcosθ p cosθ r -B X1 sinα) (13)

[0108] ΔR X2 =-sinθ sq (Δx-B X2 cosα(cosθ p sinθ y +sinθ p sinθ r cosθ y )+B X2 sinαsinθ p cosθ r )

[0109] -sin(β)cosθ sq (Δy-B X2 cosαcosθ r cosθ y -B X1sinαsinθ r +B X2 cosα)

[0110] +cos(β)cosθ sq (Δz-B X2 cosα(sinθ p sinθ y -cosθ p sinθ r cosθ y )-B X2 sinαcosθ p cosθ r +B X1 sinα)(14)

[0111] The oblique angle on the radar line-of-sight plane is θ. sq The downward viewpoint is β. Motion parameters Δx, Δy, Δz and attitude parameter θ. p ,θ y ,θ r It varies with azimuth and time η.

[0112] Phase error can be obtained from slant range error, and its expression satisfies:

[0113]

[0114] The corresponding images acquired by the two antenna channels are referred to as the main image and the auxiliary image, respectively. After compensation using inertial navigation data, the main motion error components in the images have been effectively corrected, and the magnitude of the residual motion error has been significantly reduced, satisfying the small error approximation condition. Based on this, equation (13-15) can be linearized (first-order Taylor expansion) to transform it into a function relating Δx, Δy, Δz, and θ. p ,θ y ,θ r The linear relationship is shown below. The simplified expression is as follows:

[0115]

[0116] The above equation is expressed in terms of motion parameters Δx, Δy, Δz and attitude parameter θ. p ,θ y ,θ r Organize it, and the θ in it p ·θ r and θ p ·θ y Approximately 0, it can be further simplified to:

[0117]

[0118] In this equation, the first term represents the phase error caused by the platform's own offset errors Δx, Δy, and Δz; the second term reflects the platform's attitude angle θ. p ,θ y ,θ r The impact of the difference in phase error between the preset main image and the preset auxiliary image.

[0119] Taking the derivative of equation (18-19) with respect to time, we can obtain the expression for the phase gradient as follows:

[0120]

[0121] Based on this, construct a function for Δx, Δy, Δz, θ p ,θ y ,θ r Weighted least squares estimation:

[0122] B = (A T WA) -1 A T WΦ, (22)

[0123] Where B is the first parameter, It is the gradient estimate of the motion error Δx, Δy, Δz at a certain position and time. The attitude error θ at a certain azimuth and time y ,θ p ,θ r Gradient estimation. W = diag[w1,…,w N [ ] is the diagonal matrix of weighting factors, w n Let A be the weight of the nth valid result point. A is the dual-channel joint phase error coefficient matrix constructed based on the lower viewpoint of the valid result points. Let A1 and A2 represent the dual-channel joint phase error coefficient matrix, where A1 and A2 correspond to the phase error coefficient matrices of the main and auxiliary images, respectively. The expressions are as follows:

[0124]

[0125] Where, β n This represents the downward viewpoint corresponding to the nth sample point.

[0126] Let represent the joint phase gradient estimation matrix for the two channels, where Φ1 and Φ2 are the phase gradient estimation matrices of the main and secondary images, respectively, and their expressions are as follows:

[0127]

[0128] After completing the above derivation, determine whether the number of valid result points has reached the preset number of valid points.

[0129] If so, then a weighted least squares estimation is performed on a preset number of valid result points to obtain the first parameter. Specifically, the valid result points are substituted into a preset weighted least squares estimation formula to obtain the first parameter.

[0130] Specifically, the data is processed line by line in the azimuth direction. For each azimuth gate, valid result points are selected, which are the non-empty data points in the matrices Φ(a,r) and C(a,r) mentioned in the phase gradient estimation. The number of valid data points is denoted as N. valid Since there are six parameters to be estimated, at least six samples are needed to complete the fitting. If the N corresponding to this orientation gate... valid A value less than 6 indicates that the number of valid result points covering this azimuth gate is too small to perform effective fitting, and this azimuth gate should be skipped. For N... valid For azimuth gates ≥6, weighted least squares estimation is performed according to equation (22) to obtain the first parameter. The number of sample points in matrices A, W, Φ is N. valid ,β n This represents the downward viewpoint corresponding to the distance gate where the nth valid result point is located, along with the phase gradient φ(n) and weight w. n Read from the corresponding positions of matrices Φ(a,r) and C(a,r) respectively, and w n They need to be arranged in order to form a diagonal matrix W.

[0131] Step S7: Calculate the phase error corresponding to the preset main image and the preset auxiliary image based on the effective parameters, compensate the phase error into the corresponding preset main image and the preset auxiliary image and perform azimuth compression to obtain the compensated main image and the compensated auxiliary image.

[0132] This includes: substituting the effective parameters into a preset formula and a preset distance range and calculating to obtain the phase error corresponding to the preset main image and the preset auxiliary image; and integrating the first parameter to obtain the effective parameters.

[0133] Weighted least squares estimation is performed on each first parameter to obtain Integrating these parameters further yields the effective parameters Δx, Δy, Δz, and θ. y ,θ p ,θ r Then, substitute it into equation (18-19) and combine it with the corresponding distance unit to calculate the phase error of the preset main image and the preset auxiliary image respectively. Compensate the phase error into the preset main image and the preset auxiliary image data without azimuth compression respectively. Then perform azimuth compression to obtain the compensated main image and compensated auxiliary image after error compensation.

[0134] Step S8 repeats steps S1-S7 until the preset number of iterations is reached, then outputs the compensated main image and the compensated auxiliary image.

[0135] In the process of loop execution, the compensated main image obtained in step S7 is used as the preset main image, and the compensated auxiliary image is used as the preset auxiliary image. Then, the process returns to step S1 to execute the next iteration.

[0136] The image phase error compensation method provided in the above embodiments establishes the relationship between multiple preset conditions and the phase errors of the main image and auxiliary image under the interference mode, and uses the phase errors obtained from the two images to compensate for the main image and auxiliary image; in scenarios with large motion errors, it can not only improve the focusing performance of the main image and auxiliary image, but also effectively maintain the stability of the interference phase, thereby improving the quality of the output main image and auxiliary image.

[0137] Please see Figure 3 Another embodiment of this application provides an image phase error compensation device, which includes:

[0138] Select module 101 selects multiple strong scattering points from a preset main image and a preset auxiliary image based on energy and contrast.

[0139] The filtering module 102 filters each strong scattering point according to preset constraints to obtain target scattering points; wherein, the target scattering points include target scattering points in the main image and target scattering points in the auxiliary image;

[0140] The calculation module 103 calculates candidate scattering points that meet the preset error range among the target scattering points in the main image and the target scattering points in the auxiliary image, based on the preset distance range and the preset orientation range.

[0141] The secondary filtering module 104 filters candidate scattering point indices based on a preset threshold and a preset window length difference to obtain the filtered result points;

[0142] The point-by-point processing module 105 processes and filters the result points one by one to obtain valid result points;

[0143] Secondary processing module 106 processes valid result points to obtain valid parameters;

[0144] The orientation compression module 107 calculates the phase error corresponding to the preset main image and the preset auxiliary image based on the effective parameters, compensates the phase error into the preset main image and the preset auxiliary image and performs orientation compression to obtain the compensated main image and the compensated auxiliary image.

[0145] The loop module 108 executes the selection module to the orientation compression module in a loop. After reaching the preset number of iterations, it outputs the compensated main image and the compensated auxiliary image.

[0146] The specific limitations of the image phase error compensation device provided in this embodiment can be found in the embodiments of the image phase error compensation method described above, and will not be repeated here. Each module in the above-described image phase error compensation device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0147] This application provides a computer device that may include a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it causes the processor to perform the steps of the image phase error compensation method as described in any of the above embodiments.

[0148] The working process, working details, and technical effects of the computer device provided in this embodiment can be found in the above embodiment regarding the image phase error compensation method, and will not be repeated here.

[0149] This application provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps of the image phase error compensation method as described in any of the above embodiments. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0150] The working process, working details, and technical effects of the computer-readable storage medium provided in this embodiment can be found in the above embodiment regarding the image phase error compensation method, and will not be repeated here.

[0151] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0152] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An image phase error compensation method, characterized in that, The method includes: Step S1: Select multiple strong scattering points from the preset main image and preset auxiliary image based on energy and contrast; Step S2: Filter each of the strong scattering points according to preset constraints to obtain target scattering points; wherein, the target scattering points include target scattering points in the main image and target scattering points in the auxiliary image; Step S3: Calculate candidate scattering points that meet the preset error range among the target scattering points in the main image and the target scattering points in the auxiliary image, based on the preset distance range and preset orientation range; Step S4: Filter candidate scattering point indices based on the difference between a preset threshold and a preset window length to obtain the filtered result points; Step S5: Process the filtered result points one by one to obtain valid result points; Step S6: Process the valid result points to obtain valid parameters; Step S7: Calculate the phase error corresponding to the preset main image and the preset auxiliary image based on the effective parameters, compensate the phase error into the corresponding preset main image and the preset auxiliary image and perform azimuth compression to obtain the compensated main image and the compensated auxiliary image. Step S8 repeats steps S1-S7 until the preset number of iterations is reached, then outputs the compensated main image and the compensated auxiliary image.

2. The image phase error compensation method according to claim 1, characterized in that, The step of filtering each of the strong scattering points according to preset constraints to obtain the target scattering point includes: Check if the candidate point is a maximum value within a preset window range; if not, delete the candidate point. Check whether the main lobe widths of the candidate points in the distance and azimuth directions meet the preset limits; if not, delete the candidate points. Check whether the main lobe-to-side lobe ratio of the candidate point reaches the preset value; if not, delete the candidate point. The remaining strong scattering points are taken as the target scattering points.

3. The image phase error compensation method according to claim 1, characterized in that, The point-by-point processing includes: The windowing, circular displacement, phase decompression, phase gradient calculation, smoothing and noise reduction, and weight assignment operations are performed sequentially.

4. The image phase error compensation method according to claim 1, characterized in that, The process of obtaining valid parameters from the valid result points includes: Determine whether the number of valid result points has reached the preset number of valid results; If so, then a weighted least squares estimation is performed on the preset number of valid result points to obtain the first parameter; Integrating the first parameter yields the effective parameter.

5. The image phase error compensation method according to claim 4, characterized in that, The step of performing weighted least squares estimation on a preset number of valid result points to obtain the first parameter includes: Substituting the effective result points into the preset weighted least squares estimation formula, the first parameter is obtained; The preset weighted least squares estimation formula is as follows: B6(A T FALL) -1 AM T WΦ Where B is the first parameter; W = diag[w1,…,w N [ ] is the diagonal matrix of weighting factors, w n is the weight of the nth valid result point; A is the dual-channel joint phase error coefficient matrix constructed based on the lower viewpoint of the valid result point.

6. The image phase error compensation method according to claim 4, characterized in that, The step of calculating the phase error corresponding to the preset main image and the preset auxiliary image based on the effective parameters includes: Substitute the effective parameters into the preset formula and preset distance range and calculate to obtain the phase error corresponding to the preset main image and preset auxiliary image.

7. The image phase error compensation method according to claim 1, characterized in that, The preset distance range and preset orientation range are determined based on the geometric relationship between the preset main image and the preset auxiliary image; The geometric relationship includes resolution and imaging parameters.

8. An image phase error compensation device, characterized in that, The device includes: The selection module allows you to choose multiple strong scattering points from a preset main image and a preset secondary image based on energy and contrast. The filtering module filters each of the strong scattering points according to preset constraints to obtain target scattering points; wherein, the target scattering points include target scattering points in the main image and target scattering points in the auxiliary image; The calculation module calculates candidate scattering points that meet the preset error range among the target scattering points in the main image and the target scattering points in the auxiliary image, based on the preset distance range and the preset orientation range. The secondary filtering module filters the candidate scattering point indexes based on a preset threshold and a preset window length difference to obtain the filtered result points; The point-by-point processing module processes the filtered result points one by one to obtain valid result points; The secondary processing module processes the valid result points to obtain valid parameters; The orientation compression module calculates the phase error corresponding to the preset main image and the preset auxiliary image based on the effective parameters, compensates the phase error into the preset main image and the preset auxiliary image and performs orientation compression to obtain the compensated main image and the compensated auxiliary image. The loop module executes the selection module and the orientation compression module in a loop. After reaching a preset number of iterations, it outputs the compensated main image and the compensated auxiliary image.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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