SAR imaging method and system based on sparse and generalized total variation joint regularization

Abstract

The present invention belongs to the field of radar imaging technology, and discloses a SAR imaging method and system based on sparse and generalized total variation joint regularization. The SAR imaging method based on sparse and generalized total variation joint regularization includes: according to the sampling rate of the ground scene and the echo data, relying on the geometric relationship between the observation platform and the imaging scene, establishing an echo generation model; according to the echo generation model, relying on the mapping relationship between the echo data, the imaging observation matrix and the ground scene, constructing an imaging observation matrix; according to the imaging observation matrix, establishing a multi-platform constellation SAR imaging model with sparse and total variation joint regularization to obtain the final SAR imaging result. The present invention provides a SAR imaging method based on sparse and generalized total variation joint regularization, which effectively improves the imaging quality by keeping the edge sharp and suppressing the coherent speckle.

Description

Technical Field

[0001] The present invention belongs to the field of radar imaging technology, and in particular relates to a SAR imaging method and system based on sparse and generalized total variation joint regularization. Background Art

[0002] At present, imaging is a hot issue in microwave remote sensing research in modern remote sensing technology. When radar waves encounter rough surfaces, the different phases between the basic scatterers in the return signal cause interference between the echoes, so the SAR image contains coherent speckle noise, which affects the image quality and interpretation. Commonly used sparse SAR imaging algorithms are generally based on the L1 norm threshold algorithm, but this algorithm cannot suppress coherent speckle noise and the imaging effect is not ideal.

[0003] Through the above analysis, the problems and defects of the existing technology are as follows: the traditional sparse SAR imaging algorithm will produce coherent speckle noise during the imaging process, which will reduce the SAR imaging quality and affect the effect of subsequent radar signal processing algorithm.

[0004] The difficulty of solving the above problems and defects is: effectively suppressing coherent speckle noise while sparse imaging to achieve better imaging effects.

[0005] The significance of solving the above problems and defects is: effectively suppressing coherent speckle noise, improving SAR imaging quality, and providing a guarantee for subsequent radar signal processing algorithms. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention provides a SAR imaging method and system based on sparse and generalized total variation joint regularization.

[0007] The present invention is implemented as follows: a SAR imaging method based on sparse and generalized total variation joint regularization, comprising:

[0008] According to the sampling rate of the ground scene and echo data, the echo generation model is established relying on the geometric relationship between the observation platform and the imaging scene. According to the echo generation model, the imaging observation matrix is ​​constructed relying on the mapping relationship among the echo data, the imaging observation matrix and the ground scene. According to the imaging observation matrix, a multi-platform constellation SAR imaging model with sparse and total variation joint regularization is established to obtain the final SAR imaging result.

[0009] Furthermore, the SAR imaging method based on sparse and generalized total variation joint regularization includes the following steps:

[0010] Step 1: According to the radar imaging geometry and radar system settings, a sparse and total variation joint regularized SAR imaging model is established;

[0011] Step 2: Perform iterative imaging based on the SAR imaging model.

[0012] Further, in step 1, establishing a sparse and total variation joint regularized SAR imaging model according to radar imaging geometry and radar system settings includes:

[0013] Acquire echo data and express them in matrix form by discretely expressing range time, azimuth time and target two-dimensional coordinates; establish a SAR imaging model based on sparse and generalized total variation joint regularization and optimize it.

[0014] Further, the echo data is represented as follows:

[0015]

[0016] Where x, y represent the time in range and time in azimuth respectively; T(τ) represents the emission waveform, σ(m,n) represents the ground backscatter coefficient, m, n represent the two-dimensional coordinates of the target, R(m,n,y) represents the slant range, f0 represents the carrier, c represents the speed of light; and T represents the observation scene.

[0017] Further, the step of expressing the echo data in a matrix form by discretely expressing the range time, the azimuth time and the target two-dimensional coordinates includes:

[0018] RC = ΦX + Δ;

[0019] Among them, X represents the backscattering coefficient of the target, RC represents the collected echo data; Φ represents the radar observation matrix; Δ represents the system noise.

[0020] Further, the optimization includes:

[0021]

[0022] Among them, λ,γ are regularization parameters, represents the L2 norm of the vector, ||·||1 represents the L1 norm of the vector, ||·|| TGV represents the generalized total variation norm, and abs(·) represents the magnitude of the vector.

[0023] Further, the performing iterative imaging includes:

[0024] Introduce iterative intermediate variables, k represents the number of iterations, when the iteration meets the convergence condition, then:

[0025] z k =s k-1 -Φ H (Φs k-1 -RC);

[0026] bk =max(0,abs(z k )-λ);

[0027] x k =f(b k );

[0028]

[0029] Where f() represents the TGV regularization threshold. The expression of k-order TGV regularization is:

[0030]

[0031] ||div l v|| ∞ ≤α l ,l=0,···,k-1};

[0032] Among them, Sym k () indicates that k-order symmetric tensor space of parameters, α l is a fixed positive number.

[0033] Another object of the present invention is to provide a SAR imaging system based on sparse and generalized total variation joint regularization for implementing the SAR imaging method based on sparse and generalized total variation joint regularization, wherein the SAR imaging system based on sparse and generalized total variation joint regularization comprises:

[0034] An imaging model building module is used to build a SAR imaging model with sparse and total variation joint regularization according to the radar imaging geometry and radar system settings;

[0035] The imaging module is used to perform iterative imaging according to the SAR imaging model.

[0036] Another object of the present invention is to provide a storage medium for receiving a program input by a user, wherein the stored computer program enables an electronic device to execute the SAR imaging method based on sparse and generalized total variation joint regularization, which includes the following steps:

[0037] Step 1: According to the radar imaging geometry and radar system settings, a sparse and total variation joint regularized SAR imaging model is established;

[0038] Step 2: Perform iterative imaging based on the SAR imaging model.

[0039] Another object of the present invention is to provide a computer program product stored on a computer-readable medium, comprising a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the SAR imaging method based on sparse and generalized total variation joint regularization.

[0040] Combining all the above technical solutions, the advantages and positive effects of the present invention are as follows:

[0041] The present invention provides a SAR imaging method based on sparse and generalized total variation joint regularization, which effectively improves the sparse imaging quality by maintaining sharp edges and suppressing coherent speckles.

[0042] The present invention utilizes the sparse and generalized total variation joint regularization method to effectively improve the image quality, maintain the sharp edge of the image, and suppress the coherent speckle noise, which is of great significance for the development of subsequent SAR data applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of a SAR imaging method based on sparse and generalized total variation joint regularization provided by an embodiment of the present invention.

[0044] Figure 2 It is a flow chart of a SAR imaging method based on sparse and generalized total variation joint regularization provided by an embodiment of the present invention.

[0045] Figure 3 1 is a schematic diagram of the structure of a SAR imaging system based on sparse and generalized total variation joint regularization provided by an embodiment of the present invention;

[0046] In the figure: 1. Imaging model building module; 2. Imaging module. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] In view of the problems existing in the prior art, the present invention provides a SAR imaging method based on sparse and generalized total variation joint regularization. The present invention is described in detail below with reference to the accompanying drawings.

[0049] like Figure 1 As shown, the SAR imaging method based on sparse and generalized total variation joint regularization provided by the embodiment of the present invention includes:

[0050] According to the sampling rate of the ground scene and echo data, the echo generation model is established relying on the geometric relationship between the observation platform and the imaging scene. According to the echo generation model, the imaging observation matrix is ​​constructed relying on the mapping relationship among the echo data, the imaging observation matrix and the ground scene. According to the imaging observation matrix, a multi-platform constellation SAR imaging model with sparse and total variation joint regularization is established to obtain the final SAR imaging result.

[0051] like Figure 2 As shown, the SAR imaging method based on sparse and generalized total variation joint regularization provided by the embodiment of the present invention includes:

[0052] S101, establish a SAR imaging model with sparse and total variation joint regularization according to radar imaging geometry and radar system settings;

[0053] S102, performing iterative imaging according to the SAR imaging model.

[0054] The embodiment of the present invention provides a method for establishing a SAR imaging model with sparse and total variation joint regularization according to radar imaging geometry and radar system settings, including:

[0055] Acquire echo data and express them in matrix form by discretely expressing range time, azimuth time and target two-dimensional coordinates; establish a SAR imaging model based on sparse and generalized total variation joint regularization and optimize it.

[0056] The echo data provided by the embodiment of the present invention is expressed as follows:

[0057]

[0058] Where x, y represent the time in range and time in azimuth respectively; T(τ) represents the emission waveform, σ(m,n) represents the ground backscatter coefficient, m, n represent the two-dimensional coordinates of the target, R(m,n,y) represents the slant range, f0 represents the carrier, c represents the speed of light; and T represents the observation scene.

[0059] The embodiment of the present invention provides a method for expressing echo data in a matrix form by discretely expressing the range time, the azimuth time and the two-dimensional coordinates of the target, including:

[0060] RC = ΦX + Δ;

[0061] Among them, X represents the backscattering coefficient of the target, RC represents the collected echo data; Φ represents the radar observation matrix; Δ represents the system noise.

[0062] The optimization provided by the embodiment of the present invention includes:

[0063]

[0064] Among them, λ,γ are regularization parameters, represents the L2 norm of the vector, ||·||1 represents the L1 norm of the vector, ||·|| TGV represents the generalized total variation norm, and abs(·) represents the magnitude of the vector.

[0065] The iterative imaging provided by the embodiment of the present invention includes:

[0066] Introduce iterative intermediate variables, k represents the number of iterations, when the iteration meets the convergence condition, then:

[0067] z k =s k-1 -Φ H (Φs k-1 -RC);

[0068] b k =max(0,abs(z k )-λ);

[0069] x k =f(b k );

[0070]

[0071] Where f() represents the TGV regularization threshold. The expression of k-order TGV regularization is:

[0072]

[0073] ||div l v|| ∞ ≤α l ,l=0,···,k-1};

[0074] Among them, Sym k () indicates that k-order symmetric tensor space of parameters, α l is a fixed positive number.

[0075] like Figure 3 As shown, the SAR imaging system based on sparse and generalized total variation joint regularization provided by the embodiment of the present invention includes:

[0076] Imaging model building module 1, used to build a SAR imaging model with sparse and total variation joint regularization according to radar imaging geometry and radar system settings;

[0077] The imaging module 2 is used to perform iterative imaging according to the SAR imaging model.

[0078] The technical solution of the present invention is further described below in conjunction with specific embodiments.

[0079] Example:

[0080] A SAR imaging method based on sparse and generalized total variation joint regularization includes: establishing a SAR imaging model based on sparse and generalized total variation joint regularization according to radar imaging geometry and radar system settings; and then providing a specific iterative imaging method according to the SAR imaging model. The present invention provides a SAR imaging method based on sparse and generalized total variation joint regularization, which can keep edges sharp, suppress coherent speckles, and effectively improve imaging quality.

[0081] The SAR imaging method based on sparse and generalized total variation joint regularization is characterized by comprising the following steps:

[0082] S1: According to the radar imaging geometry and radar system settings, a SAR imaging model with sparse and total variation joint regularization is established;

[0083] S2: Based on the SAR imaging model, a specific iterative imaging method is given.

[0084] The step S1 specifically includes the following steps:

[0085] S11: For the observation scene T, the received echo data is expressed as:

[0086]

[0087] Where x, y are the range time and azimuth time respectively; T(τ) is the transmitted waveform, σ(m,n) represents the ground backscatter coefficient, m,n represent the two-dimensional coordinates of the target, R(m,n,y) represents the slant range, f0 represents the carrier, and c represents the speed of light;

[0088] S12: By discretely representing the range time, azimuth time and target two-dimensional coordinates, the echo data is represented in matrix form:

[0089] RC = ΦX + Δ;

[0090] Where X represents the backscatter coefficient of the target, RC represents the echo collected by the platform; Φ represents the radar observation matrix (determined by the echo data); Δ represents the system noise;

[0091] S13: Establish a SAR imaging model based on sparse and generalized total variation joint regularization. The specific optimization method is:

[0092]

[0093] Among them, λ,γ are regularization parameters, represents the L2 norm of the vector, ||·||1 represents the L1 norm of the vector, ||·|| TGV represents the generalized total variation norm, and abs(·) represents the magnitude of the vector.

[0094] The step S2 is specifically as follows:

[0095] S21: Introduce iterative intermediate variables, k represents the number of iterations. When the iteration meets the convergence condition, then:

[0096] A)z k =s k-1 -Φ H (Φs k-1 -RC)

[0097] B)b k =max(0,abs(z k )-λ)

[0098] C)x k =f(b k ), f() represents the TGV regularization threshold

[0099] D)

[0100] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0101] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A SAR imaging method based on sparse and generalized total variation joint regularization, characterized in that: The SAR imaging method based on sparse and generalized total variation joint regularization includes: According to the sampling rate of the ground scene and the echo data, the echo generation model is established by relying on the geometric relationship between the observation platform and the imaging scene; according to the echo generation model, the imaging observation matrix is ​​constructed by relying on the mapping relationship between the echo data, the imaging observation matrix and the ground scene; according to the imaging observation matrix, a multi-platform constellation SAR imaging model with sparse and total variation joint regularization is established to obtain the final SAR imaging result; The SAR imaging method based on sparse and generalized total variation joint regularization comprises the following steps: Step 1: According to the radar imaging geometry and radar system settings, a sparse and total variation joint regularized SAR imaging model is established; Step 2: Perform iterative imaging according to the SAR imaging model; In step 1, the establishment of a sparse and total variation joint regularized SAR imaging model according to radar imaging geometry and radar system settings includes: Acquire echo data and express them in matrix form by discretely expressing range time, azimuth time and target two-dimensional coordinates; establish and optimize a SAR imaging model based on sparse and generalized total variation joint regularization; The iterative imaging comprises: Introduce iterative intermediate variables, k represents the number of iterations, when the iteration meets the convergence condition, then: z k =s k-1 -ΦH(Φs k-1 -RC); b k =max(0,abs(z k )-λ); x k =f(b k ); Among them, f() represents the TGV regularization threshold, and the expression of k-order TGV regularization is: div l v ∞ ≤α l ,l=0,···,k-1}; Among them, Sym k () indicates that k-order symmetric tensor space of parameters, α l is a fixed positive number.

2. The SAR imaging method based on sparse and generalized total variation joint regularization as claimed in claim 1, characterized in that: The optimization comprises: Among them, λ,γ are regularization parameters, represents the L2 norm of the vector, ||·||1 represents the L1 norm of the vector, ||·|| TGV represents the generalized total variation norm, and abs(·) represents the magnitude of the vector.

3. A SAR imaging system based on sparse and generalized total variation joint regularization that implements the SAR imaging method based on sparse and generalized total variation joint regularization as claimed in any one of claims 1 to 2, characterized in that: The SAR imaging system based on sparse and generalized total variation joint regularization includes: An imaging model building module is used to build a SAR imaging model with sparse and total variation joint regularization according to the radar imaging geometry and radar system settings; The imaging module is used to perform iterative imaging according to the SAR imaging model.

4. A storage medium for receiving user input programs, wherein the stored computer program enables an electronic device to execute the steps of the SAR imaging method based on sparse and generalized total variation joint regularization as described in any one of claims 1 to 2.

5. A computer program product stored on a computer-readable medium, comprising a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the SAR imaging method based on sparse and generalized total variation joint regularization as described in any one of claims 1 to 2.

Images