A single image sea surface target feature enhancement and anti-interference method

Abstract

The present invention discloses a single-image sea surface target feature enhancement and anti-interference method, comprising: slicing a SLC image whose background is a sea-land boundary or a pure sea surface scene and a target exists on the sea surface; performing Fourier transform on the sliced ​​image matrix along the distance dimension to obtain a range-frequency-azimuth-time domain representation matrix, predicting a row interference mask, determining the interference spectrum in the representation matrix, and obtaining a pure interference matrix; when the pure interference matrix satisfies the low-rank property of the pure interference matrix, using it as an initialization low-rank matrix; otherwise, using the image matrix as the initialization low-rank matrix; modeling the image matrix as a superposition of an unknown target signal matrix and an unknown interference matrix, performing low-rank sparse decomposition on the image matrix, and constructing an optimization model; solving the optimization model to obtain a result of removing interference from the image matrix; and performing a splicing operation on the image matrices of all slices after removing interference to obtain an SLC image after removing interference.

Description

Technical Field

[0001] The present invention belongs to the field of radar signal processing, and in particular relates to a method for enhancing the features of a single-image sea surface target in an anti-interference scenario, which is suitable for suppressing narrowband and complex modulation broadband interference on the sea surface in a synthetic aperture radar single look complex (SLC) image. Background Art

[0002] Synthetic Aperture Radar (SAR), with its all-weather, high-resolution capabilities, plays an indispensable role in military imaging and reconnaissance scenarios. On the one hand, with the widespread application of modern radio technology, electromagnetic spectrum resources are becoming increasingly limited, which may lead to an increased probability of radio frequency interference (RFI). RF interference primarily originates from ground-based radars, meteorological detection systems, and communications equipment operating in the same frequency band. On the other hand, in battlefield environments, interference generated by electronic countermeasures (ECM) processes is characterized by high energy, a certain degree of coherence with the signal, and a high degree of interweaving with the target signal in the time-frequency and spatial domains. Once a SAR system is contaminated by interference, it will affect the signal's impulse response, thereby reducing the quality of SAR images and, in turn, affecting the SAR reconnaissance satellite's ability to detect surface ship targets.

[0003] Currently, the main interference suppression methods are mainly divided into parametric, semi-parametric, and non-parametric methods. Parametric methods estimate interference parameters by establishing a parameterized interference model, thereby suppressing interference in echo data. However, the mathematical modeling of interference signals in adversarial scenarios is relatively complex, and errors in model parameter estimation can lead to inaccurate reconstruction of broadband interference. Non-parametric methods effectively suppress interference in the transform domain by leveraging the difference in amplitude characteristics between interference and true echoes, with relatively low computational complexity. However, in adversarial scenarios, it is difficult to obtain a suitable mapping transform that can effectively distinguish interference from echo signals in SAR images affected by large-bandwidth interference. Semi-parametric methods transform the interference suppression problem into a hyperparametric optimization problem. For example, they utilize low-rank sparse features to establish an optimization model for interference suppression. Compared with parametric methods, semi-parametric methods have the advantage of not requiring accurate prior knowledge of the interference signal and achieving good interference suppression performance with relatively simple models. However, the performance of semi-parametric methods is highly dependent on strict assumptions, often requiring the use of multiple images to form these assumptions. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for enhancing the features of sea surface targets in a single image under interference-resistant scenarios, so as to overcome the problems existing in the prior art.

[0005] In order to achieve the above tasks, the present invention adopts the following technical solutions:

[0006] A single-image sea surface target feature enhancement and anti-interference method, comprising:

[0007] Slice the synthetic aperture radar SLC image with the background of the land-sea boundary or the pure sea surface scene and the target on the sea surface;

[0008] Perform Fourier transform on the slice image matrix along the range dimension to obtain the range frequency domain-azimuth time domain representation matrix; calculate the statistics of the representation matrix to predict the interference mask of the representation matrix;

[0009] Using the interference mask, determining the interference spectrum in the characterization matrix, and performing inverse Fourier transform on the interference spectrum to obtain a pure interference matrix; when the pure interference matrix satisfies the low rank property of the pure interference matrix, using it as the initialization low rank matrix; otherwise, using the image matrix as the initialization low rank matrix;

[0010] The image matrix is ​​modeled as a superposition of an unknown target signal matrix and an unknown interference matrix, and the image matrix is ​​subjected to low-rank sparse decomposition to construct an optimization model; wherein the initialization low-rank matrix is ​​an initialization value of the unknown interference matrix;

[0011] The optimization model is solved to obtain the result of the image matrix after the interference is removed; the image matrices of all slices are combined after the interference is removed to obtain the SLC image after the interference is removed.

[0012] Furthermore, the statistics of the calculation characterization matrix are expressed as:

[0013]

[0014] where X fr (i,j) is X fr The i-th row and j-th column element in Z score (i, j) is the corresponding statistic; mean(·) is to find the mean of the two-dimensional matrix, std(·) is to calculate the standard deviation of the two-dimensional matrix; X fr It is the distance frequency domain-azimuth time domain representation matrix obtained by Fourier transforming the image matrix.

[0015] Furthermore, the predicting of the interference mask for the characterization matrix includes:

[0016] Interference Mask m The elements in can be represented as:

[0017]

[0018] Where normcdf(·) is the cumulative standard normal distribution, |·| is the absolute value operation, and α is the confidence level.

[0019] Furthermore, the interference mask is used to determine the interference spectrum in the characterization matrix, and the interference spectrum is inverse Fourier transformed to obtain a pure interference matrix, including:

[0020] Representation matrix X fr Corresponding Mask m All elements that are 0 are set to 0, and the matrix X fr The remaining part is the interference spectrum, and the inverse Fourier transform is performed on it to obtain the pure interference matrix I m .

[0021] Furthermore, the judgment criterion of low rank is:

[0022] When the rank percentage of the matrix is ​​less than 15%, it is considered to meet the low rank property; when the rank percentage is greater than 15%, it is considered not to meet the low rank property; the rank percentage calculation formula is:

[0023] r=rank(I m ) / Nr

[0024] Where rank(·) is the rank of the matrix, and Nr represents the total number of fast-time discrete sampling points of the image matrix.

[0025] Furthermore, the image matrix is ​​modeled as a superposition of an unknown target signal matrix and an unknown interference matrix, and a low-rank sparse decomposition is performed on the image matrix to construct an optimization model, which is expressed as:

[0026]

[0027] Among them, X represents the image matrix, S represents the unknown target signal matrix, L represents the unknown interference matrix, and ||·|| * is the nuclear norm, ||·||1 is the 1 norm, λ is the hyperparameter, L0 is the initialization low rank matrix, that is, the initialization value of L; the initialization value of S is set to 0, I m is a pure interference matrix.

[0028] Furthermore, solving the optimization model to obtain the result of removing interference from the image matrix includes:

[0029] The optimization model is decomposed into the following sub-optimization models to obtain the k-th generation optimization results of S and L:

[0030] Step 4.1, use the following sub-optimization model to solve the current k-th generation optimal sparse matrix S k :

[0031]

[0032] where Y k The matrix is ​​the kth generation Lagrange multiplier, μ is the Lagrange penalty coefficient, ||·||F is the F norm, L k-1 is the low-rank matrix solved for the k-1th generation;

[0033] Step 4.2, use the following sub-optimization model to solve the current k-th generation optimal low-rank matrix L k :

[0034]

[0035] Step 4.3, update the Lagrange multiplier:

[0036] Y k =Y k-1 +(XS k-1 -L k-1 )

[0037] Step 4.4: Determine whether to stop the iteration or continue with step 4.1 based on whether the iteration termination condition is met. The termination condition is set to be that the reconstruction error is less than the set value err:

[0038] XS k -L k <err

[0039] If the iteration is stopped, the optimal solution of S and L can be obtained and is the result of removing interference from the sliced ​​image matrix, and is the final output image with restored quality. For slices with only interference.

[0040] Furthermore, the sub-optimization model is solved using a classical soft threshold operator method.

[0041] A terminal device comprises a processor, a memory and a computer program stored in the memory; when the processor is executed by a computer, the single-image sea surface target feature enhancement and anti-interference method is implemented.

[0042] A computer-readable storage medium stores a computer program; when the computer program is executed by a processor, the single-image sea surface target feature enhancement and anti-interference method is implemented.

[0043] Compared with the prior art, the present invention has the following technical features:

[0044] Based on the texture characteristics of interference in the image domain and the local sparseness of ship targets, this paper proposes a method for enhancing the features of single-image sea surface targets in interference-resistant scenarios. This method not only removes narrowband and broadband interference above the target, but also effectively suppresses defocusing of the ship target, improves the signal-to-noise ratio within the region, and enhances the feature distinction between the target and the background, thereby improving subsequent target detection performance. Furthermore, based on traditional sparse decomposition algorithms, the method can further improve the completeness of target information recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flow chart of an embodiment of the method of the present invention;

[0046] Figure 2 The image slices of the SLC containing the ship target are interfered with, where (a) is the measured data scene of scene A superimposed with ultra-wideband sinusoidal FM interference; (b) is the scene of scene B with actual narrowband interference;

[0047] Figure 3 is the scene range frequency domain-azimuth time domain matrix, where (a) is the range frequency domain-azimuth time domain matrix of scene A; (b) is the range frequency domain-azimuth time domain matrix of scene B;

[0048] Figure 4 To extract the pure interference matrix; (a) is the pure interference matrix extracted for scene A; (b) is the pure interference matrix extracted for scene B;

[0049] Figure 5 The processing results of scene A, where (a) is the low-rank matrix after decomposition; (b) is the sparse matrix after decomposition; (c) is the original unperturbed imaging result;

[0050] Figure 6 The processing results of scenario B, where (a) is the low-rank matrix after decomposition by the original robust principal component analysis method; (b) is the low-rank matrix after decomposition by the principal component analysis method of the present invention; (c) is the sparse matrix after decomposition by the present invention; and (d) is the frequency domain notch result. DETAILED DESCRIPTION

[0051] The present invention provides a single-image sea surface target feature enhancement and anti-interference method. First, a full-size SAR image is processed in blocks to construct a target sparse interference low-rank matrix to be decomposed; secondly, the interference roughly extracted by a frequency domain notch filter is used as the initial low-rank matrix value to guide its decomposition process, improve the model accuracy, and retain more target features; finally, a robust principal component analysis method is performed to decompose and reconstruct the sparse matrix as a target detection slice to remove interference, defocusing, and sidelobe tailing phenomena, and the low-rank matrix is ​​the separated interference matrix.

[0052] Referring to the accompanying drawings, a method for enhancing and anti-interference of sea surface target features in a single image includes the following steps:

[0053] Step 1: Slice the large-scale synthetic aperture radar SLC image with the background of the land-sea boundary or the pure sea scene and the target on the sea surface into small slices. The slice size can be set to 256×256; let the slice image matrix Represented as follows, where X(i,j) represents the i-th row and j-th column element in the image matrix X of the slice, where 1≤i≤Na and 1≤j≤Nr, i represents the slow time discrete sampling point, j represents the fast time sampling point, Na represents the total number of slow time discrete sampling points, Nr represents the total number of fast time discrete sampling points, the size of Na and Nr depends on the selected slice size and can be set to 256 respectively. Represents a complex set.

[0054] Step 2: Perform Fourier transform on X along the distance dimension, that is, transform each row element of X into the frequency domain to obtain the distance frequency domain-azimuth time domain representation matrix X fr , the dimension of the representation matrix is ​​also Na×Nr; calculate X fr The statistic Z score , to quantify the specificity of interference relative to target signal:

[0055]

[0056] where X fr (i,j) is X fr The i-th row and j-th column element in Z score (i, j) is the corresponding statistic, mean(·) is used to find the mean of a two-dimensional matrix, and std(·) is used to calculate the standard deviation of a two-dimensional matrix;

[0057] By calculating Z score (i, j) and set the confidence level to design the frequency domain notch filter, so as to fr Perform two-dimensional interference mask m The prediction of , get the interference mask Mask m The elements in can be represented as:

[0058]

[0059] Where normcdf(·) is the cumulative standard normal distribution, |·| is the absolute value operation, and α is the confidence level, which can be set according to experience.

[0060] Step 3, X fr Corresponding Mask m All elements that are 0 are set to 0, and the matrix X fr The remaining part is the interference spectrum, and the inverse Fourier transform is performed on it to obtain the pure interference matrix I m :

[0061]

[0062] The interference matrix extracted for broadband interference scenarios is not accurate. Its validity can be judged based on the rank percentage of the extracted interference matrix. When the rank percentage is less than 15%, it is considered to meet the low rank property. The extracted interference matrix I m Valid, it is used as the low-rank matrix initialized in step 4; when the rank percentage is greater than 15%, it is considered that the low-rank property is not satisfied, and the extracted interference matrix is ​​invalid. At this time, let I m =X.

[0063] The rank percentile is calculated as:

[0064] r=rank(I m ) / Nr (4)

[0065] Where rank(·) is the rank of the matrix.

[0066] Step 4: Model the image matrix X as the superposition of the unknown target signal matrix S and the unknown interference matrix L.

[0067] X=L+S (5)

[0068] The matrix X is subjected to low-rank sparse decomposition using an improved robust principal component analysis algorithm, and the target optimization variables S and L are obtained by iteratively solving the following optimization model:

[0069]

[0070] where ||·|| * is the nuclear norm used to constrain the low-rank condition, ||·||1 is the 1-norm used to constrain the sparse condition, and λ is the algorithm hyperparameter, which is generally taken as max(·) is the maximum value of the two values ​​in the brackets, L0 is the initialization value of the low-rank matrix, that is, the initialization value of L; the initialization value of S is set to 0.

[0071] After initializing the low-rank matrix, the process of solving the optimization model using the Lagrangian method in the original robust principal component analysis method needs to be adjusted. The optimization model (6) is decomposed into the following sub-optimization models. The specific steps for calculating the k-th generation optimization results S and L are as follows:

[0072] Step 4.1, use the following sub-optimization model to solve the current k-th generation optimal sparse matrix S k :

[0073]

[0074] where Y k The matrix is ​​the kth generation Lagrange multiplier, μ is the Lagrange penalty coefficient, which can be 10λ, ||·||F is the F norm, L k-1 is the low-rank matrix solved for the k-1th generation; the optimization model is solved using the classic soft threshold operator method, which has the same steps as the classic robust principal component analysis method and will not be described here.

[0075] Step 4.2, use the following sub-optimization model to solve the current optimal low-rank matrix L k :

[0076]

[0077] The optimization model is solved using the singular value decomposition algorithm and the classic soft threshold operator method. The steps are the same as the classic robust principal component analysis method and will not be repeated here.

[0078] Step 4.3, update the Lagrange multiplier:

[0079] Y k =Y k-1 +(XS k-1 -L k-1 ) (9)

[0080] In step 4.4, determine whether to stop the iteration or continue with step 4.1 based on whether the iteration termination condition is met. The termination condition can generally be set to that the reconstruction error is less than the set value err, which can be set to 1e-5. The specific value can be adjusted according to the actual value.

[0081] XS k -L k <err (10)

[0082] If the iteration is stopped, the optimal solution of S and L can be obtained and is the result of removing interference from the sliced ​​image matrix, and is the final output image with restored quality. For slices with only interference; first optimize S k Matrix post-optimization L k The matrix can be used to represent S containing the complete target and residual interference k The interference is gradually eliminated based on the matrix, which is opposite to the optimization process of the original method where the matrix is ​​initialized to 0 and the sparse pixels are gradually added. This ensures the integrity of the target. Steps 1 to 4 are performed on all slices. After stitching, the original SLC image with enhanced anti-interference features is obtained.

[0083] Example:

[0084] Step 1: Cut the large-scale synthetic aperture radar SLC image with the background of land-sea boundary or pure sea scene and the target on the sea surface into small slices, and set the slice size to 256×256. Let one of the slices be the matrix Where X(i,j) represents the i-th row and j-th column element in the slice matrix X, where 1≤i≤Na and 1≤j≤Nr, i represents the slow-time discrete sampling point, j represents the fast-time sampling point, Na represents the number of slice orientation points, and Nr represents the number of slice fast-time sampling points. The sizes of Na and Nr depend on the selected slice size and are set to 256 respectively. Here we use two scenarios as examples for implementation. Scenario A is the imaging result of measured echo data superimposed with simulated ultra-wideband sinusoidal modulation interference, and scenario B is the data affected by actual narrowband radio frequency interference, such as Figure 2 .

[0085] Step 2: The matrix X is modeled as the superposition of the unknown target signal matrix S and the unknown interference matrix L.

[0086] X=L+S (1)

[0087] Perform Fourier transform on each row element of X and transform X into the range frequency domain-azimuth time domain X fr like Figure 3 , we can see that complex modulated ultra-wideband interference forms dense bright lines in the frequency domain, while narrowband interference forms interference strips with relatively concentrated energy. fr Z score The statistic quantifies the specificity of the interference relative to the target signal:

[0088]

[0089] Where mean(·) is to find the mean of a two-dimensional matrix, and std(·) is to calculate the standard deviation of a two-dimensional matrix. score The confidence interval of X is set to set the confidence level to design the frequency domain notch filter fr Perform two-dimensional interference mask m The prediction is obtained. m Final detection mask Mask m :

[0090]

[0091] where normcdf(·) is the cumulative standard normal distribution, |·| is the absolute value operation, and α is the confidence level, which is set to 0.2.

[0092] Step 3, X fr Corresponding Mask m All the values ​​that are 0 are set to 0, and the remaining X frThe matrix is ​​the interference spectrum, and the pure interference matrix I is obtained by inverse Fourier transform. m :

[0093]

[0094] The interference matrix extracted for broadband interference scenarios is not accurate, such as Figure 4 (b), the extracted interference matrix rank percentage is 18.83%, which does not meet the low rank assumption, so the matrix is ​​not used as the initial low rank matrix. Let I m = X. The interference matrix of narrowband interference scenario is as follows Figure 4 (a), its rank percentage is 8.56%, which meets the low rank assumption, I m The matrix is ​​valid.

[0095] Step 4: Use the improved robust principal component analysis algorithm to perform low-rank sparse decomposition on the matrix X and solve the following optimization model:

[0096]

[0097] where ||·|| * is the nuclear norm used to constrain the low-rank condition, ||·||1 is the 1-norm used to constrain the sparse condition, and λ is the algorithm hyperparameter, which is the default in the sample. max(·) is the maximum value of the two values ​​in the brackets, and L0 is the initial low-rank matrix.

[0098] Finally, the two scenarios get the optimal results as follows Figure 5 , the defocus linearity of the ship target is reduced. The narrowband interference scenario is compared with the original robust principal component analysis method and frequency domain notch results. Figure 6 , it can be seen that the present invention suppresses the side lobes and reduces the noise compared with the frequency domain notch method, as shown in Figure 6 (c) and (d) are the comparison results. Compared with the traditional robust principal component analysis method, more target information is extracted, and there is no residual target pixel in the separated interference matrix, such as Figure 6 Comparison results of (a) and (b).

[0099] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for enhancing and anti-interference of sea surface target features in a single image, characterized in that: include: Slice the synthetic aperture radar SLC image with the background of the land-sea boundary or the pure sea surface scene and the target on the sea surface; Perform Fourier transform on the image matrix of the slice along the range dimension to obtain the range frequency domain-azimuth time domain representation matrix; By calculating the statistics of the characterization matrix, the interference mask of the characterization matrix is ​​predicted; Using the interference mask, determining the interference spectrum in the characterization matrix, and performing inverse Fourier transform on the interference spectrum to obtain a pure interference matrix; when the pure interference matrix satisfies the low rank property of the pure interference matrix, using it as the initialization low rank matrix; otherwise, using the image matrix as the initialization low rank matrix; The image matrix is ​​modeled as a superposition of an unknown target signal matrix and an unknown interference matrix, and the image matrix is ​​subjected to low-rank sparse decomposition to construct an optimization model; wherein the initialization low-rank matrix is ​​an initialization value of the unknown interference matrix; The optimization model is solved to obtain the result of the image matrix after the interference is removed; the image matrices of all slices are combined after the interference is removed to obtain the SLC image after the interference is removed.

2. The single image sea surface target feature enhancement and anti-interference method according to claim 1, characterized in that: The statistics of the computational characterization matrix are expressed as: where X fr (i,j) is X fr The i-th row and j-th column element in Z score (i, j) is the corresponding statistic; mean(·) is to find the mean of the two-dimensional matrix, std(·) is to calculate the standard deviation of the two-dimensional matrix; X fr It is the distance frequency domain-azimuth time domain representation matrix obtained by Fourier transforming the image matrix.

3. The method for enhancing and anti-interference of sea surface target features in a single image according to claim 1, characterized in that: The predicting of the interference mask for the characterization matrix includes: Interference Mask m The elements in can be represented as: Where normcdf(·) is the cumulative standard normal distribution, |·| is the absolute value operation, and α is the confidence level.

4. The method for enhancing and anti-interference of sea surface target features in a single image according to claim 1, characterized in that: The interference mask is used to determine the interference spectrum in the characterization matrix, and the interference spectrum is inverse Fourier transformed to obtain a pure interference matrix, including: Representation matrix X fr Corresponding Mask m All elements that are 0 are set to 0, and the matrix X fr The remaining part is the interference spectrum, and the inverse Fourier transform is performed on it to obtain the pure interference matrix I m .

5. The single image sea surface target feature enhancement and anti-interference method according to claim 1, characterized in that: The criterion for judging low rank is: When the rank percentage of the matrix is ​​less than 15%, it is considered to meet the low rank property; when the rank percentage is greater than 15%, it is considered not to meet the low rank property; the rank percentage calculation formula is: r=rank(I m ) / Nr Where rank(·) is the rank of the matrix, and Nr represents the total number of fast-time discrete sampling points of the image matrix.

6. The method for enhancing and anti-interference of single-image sea surface target features according to claim 1, characterized in that: The image matrix is ​​modeled as a superposition of an unknown target signal matrix and an unknown interference matrix, and a low-rank sparse decomposition is performed on the image matrix to construct an optimization model, which is expressed as: Among them, X represents the image matrix, S represents the unknown target signal matrix, L represents the unknown interference matrix, and ||·|| * is the nuclear norm, ||·||1 is the 1 norm, λ is the hyperparameter, L0 is the initialization low rank matrix, that is, the initialization value of L; the initialization value of S is set to 0, I m is a pure interference matrix.

7. The method for enhancing and anti-interference of sea surface target features in a single image according to claim 1, characterized in that: Solving the optimization model to obtain the result of removing interference from the image matrix includes: The optimization model is decomposed into the following sub-optimization models to obtain the k-th generation optimization results of S and L: Step 4.1, use the following sub-optimization model to solve the current k-th generation optimal sparse matrix S k : where Y k The matrix is ​​the kth generation Lagrange multiplier, μ is the Lagrange penalty coefficient, ||·|| F is the F norm, L k-1 is the low-rank matrix solved for the k-1th generation; Step 4.2, use the following sub-optimization model to solve the current k-th generation optimal low-rank matrix L k : Step 4.3, update the Lagrange multiplier: AND k =And k-1 +(XS k-1 -L k-1 ) Step 4.4: Determine whether to stop the iteration or continue with step 4.1 based on whether the iteration termination condition is met. The termination condition is set to be that the reconstruction error is less than the set value err: XS k -L k <err If the iteration is stopped, the optimal solution of S and L can be obtained and is the result of removing interference from the sliced ​​image matrix, and is the final output image with restored quality. For slices with only interference.

8. The method for enhancing and anti-interference of sea surface target features in a single image according to claim 7, characterized in that: The sub-optimization model is solved using the classical soft threshold operator method.

9. A terminal device comprising a processor, a memory, and a computer program stored in the memory; characterized in that: When the processor is executed by a computer, the single-image sea surface target feature enhancement and anti-interference method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program; wherein: When the computer program is executed by a processor, the single image sea surface target feature enhancement and anti-interference method according to any one of claims 1 to 8 is implemented.

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