CN115508835B - Chromatographic SAR three-dimensional imaging method based on blind compressed sensing - Google Patents
Chromatographic SAR three-dimensional imaging method based on blind compressed sensing Download PDFInfo
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Abstract
The invention discloses a chromatographic SAR three-dimensional imaging method based on blind compressed sensing, which relates to the technical field of chromatographic SAR imaging and comprises the following steps: s1: preprocessing an SAR sequence data set acquired by an observation object region, and sequentially constructing high-latitude signals by pixels in each image and arranging the high-latitude signals into a matrix form; s2: reconstructing image information by using a blind compressed sensing frame by utilizing the correlation of elevation directions of adjacent azimuth-distance units, and modeling an image signal into a product of a sparse matrix and a dictionary matrix; establishing a chromatographic SAR imaging model based on blind compressed sensing; s3: continuously transforming and solving the imaging model; and solving a target optimization problem by using an alternate multiplier method, and solving a sub-problem through variable alternate circulation, wherein the circulation is used for carrying out optimization solution on the sub-problem to obtain a high-resolution chromatographic SAR imaging result. Aiming at a sparse target point scene, the imaging quality can be still ensured when the number of navigation passes is small, and the complexity of the process is reduced.
Description
Technical Field
The invention relates to the technical field of chromatographic SAR imaging, in particular to a chromatographic SAR three-dimensional imaging method based on blind compressed sensing.
Background
The tomosynthesis aperture radar imaging (tomography synthetic aperture radar, tomoSAR) is to apply the synthetic aperture principle to the elevation direction, and perform aperture synthesis in the elevation direction from different incident angles by using a plurality of two-dimensional SAR images of the same scene, so as to obtain the resolution in the elevation direction. The three-dimensional imaging method can reconstruct three-dimensional information of a scatterer and invert an elevation direction section, and can effectively solve the problem of a covering effect when the skew between a target scattering point in the same scattering unit and a radar in two-dimensional SAR imaging is equal, so that three-dimensional imaging is realized. Compared with an Interferometric synthetic aperture radar (InSAR), the SAR imaging technology can obtain the elevation information of a target scatterer, can obtain the distribution of the scatterer in the elevation direction, and can completely recover a real three-dimensional scene.
Blind compressed sensing (Blind Compressed Sensing, BCS) is a sparse linear combination of Gao Chengji functions modeling signals as a large dictionary that incorporates dictionary construction theory, which refers to constructing optimal sparse basis under sparse representation that requires satisfaction of coefficient uniqueness conditions and optimization of the solution to get more accurate results. The traditional compressed sensing technology utilizes fixed analysis sparse conversion to reconstruct an image, signals are often unknown and complex, a potential sparse model in blind compressed sensing is unknown a priori, the theory of the technology is not to assume a fixed dictionary, dictionary learning is not carried out independently according to priori information, but an adaptive compressed sensing model based on dictionary replacement learning is built, the need of a dictionary in the sampling and recovering processes is avoided, and the sparse basis in the dictionary corresponds to a specific scene target, so that the sparse basis in the dictionary is not constrained by orthogonality. Therefore, the blind compressed sensing algorithm can enable the sparse model to be adaptive to the considered fixed data, and the final dictionary of the model is suitable for all sparse images no matter what the image sparse basis is.
The prior art discloses a full polarization SAR super-resolution imaging method based on distributed compressed sensing, which comprises the following steps: s1: establishing a full-polarization synthetic aperture radar signal model according to a full-polarization channel arranged in the synthetic aperture radar; the fully polarized channels include HH polarized channels, HV polarized channels, and VV polarized channels; s2: receiving corresponding back scattering echo data by utilizing each polarization channel in the synthetic aperture radar full polarization channels; obtaining a synthetic aperture radar backscattering coefficient matrix according to the backscattering echo data received by each polarization channel; s3: aiming at the super-resolution imaging problem of the full-polarization synthetic aperture radar, a distributed compressed sensing algorithm is adopted to establish an optimization problem model of a synthetic aperture radar backscattering coefficient matrix; s4: solving the optimization problem of the synthetic aperture radar backscattering coefficient matrix to obtain the synthetic aperture radar backscattering coefficient matrix; s5: according to the synthetic aperture radar backscattering coefficient matrix, super-resolution imaging processing is carried out on each polarized channel, and a corresponding pseudo-color image is obtained; s6: and aiming at the pseudo-color images corresponding to each polarization channel, adopting a pseudo-color image fusion algorithm based on RGB space to perform pseudo-color fusion, and obtaining a pseudo-color fusion image. However, the target scene is a distributed target scene, the sparse point target scene cannot be processed, the SAR imaging effect cannot be ensured when the number of passes is small, and the complexity of the process is high.
Disclosure of Invention
The invention provides a chromatographic SAR three-dimensional imaging method based on blind compressed sensing, which aims to solve the problems that a target scene which is aimed at now is a distributed target scene, a sparse point target scene cannot be processed, the SAR imaging effect cannot be guaranteed when the number of passes is small, and the complexity of a process is high.
In order to solve the technical problems, the invention adopts the following technical scheme:
a chromatography SAR three-dimensional imaging method based on blind compressed sensing comprises the following steps:
s1: : preprocessing a plurality of navigation SAR sequence data sets of the same imaging region in the obtained observation object, and sequentially constructing high-latitude signals one by pixels in each SAR image and arranging the signals into a matrix form; the aviation refers to repeated flying of airborne SAR at different height positions, wherein the flying tracks are all straight lines, and repeated observation is carried out on the same scene for a plurality of times;
s2: reconstructing image information by using a blind compressed sensing frame by utilizing the correlation of the elevation direction of the adjacent azimuth-distance units of the observed object, and modeling the image signal into the product of a sparse matrix and a dictionary matrix; establishing a chromatographic SAR imaging model based on blind compressed sensing; the image signals are the image signals in the preprocessed data set;
s3: continuously transforming and solving the chromatographic SAR imaging model based on the blind compressed sensing; and solving a target optimization problem by using an alternate multiplier method, and solving a sub-problem through variable alternate circulation, wherein the circulation is used for carrying out optimization solution on the sub-problem to obtain a high-resolution chromatographic SAR imaging result.
The working principle of the invention is as follows:
the invention combines a blind compressed sensing algorithm to establish a chromatographic SAR imaging model combined with blind compressed sensing, utilizes the structural characteristic of target sparseness, introduces the blind compressed sensing algorithm to process the elevation direction of an azimuth-distance unit of the target, combines the sparse characteristic of the target with the structural characteristic of the target, models the image into the product of a sparse matrix and a dictionary matrix by the blind compressed sensing scheme, adopts an alternate multiplier optimization algorithm to decompose a plurality of complex problems into simple sub-problems, solves the sub-problems through variable alternate circulation, and optimizes the sub-problems through circulation so as to obtain a high-resolution chromatographic SAR imaging result.
Preferably, the pretreatment includes: single vision complex image sequence registration, eliminating evil, phase compensation and baseline estimation.
Preferably, the expression of the chromatographic SAR imaging model based on blind compressed sensing is as follows:
in the formula (1), U is a data fidelity term and a sparse coefficient, V is a dictionary matrix, A represents a chromatographic operator of a chromatographic SAR, y is echo data, lambda is a regularization parameter, and s.t. is a constraint condition; the dictionary matrix V is added with the F-norm of one unit;
in the formula (1)For ensuring consistency of data; />By using non-convex terms on U P (p<1) To promote sparsity of spatial coefficients; u and V are decoupled by introducing the constraint s=uv, where S is an auxiliary variable of UV.
Further, the solving step of the chromatographic SAR imaging model based on the blind compressed sensing is as follows:
s301: the convergence of the decoupling acceleration equation is performed by variable decomposition, introducing the constraint z=s, where Z is an auxiliary variable, and the constraint optimization problem is written as:
approximating the p-penalty of U in equation (2) asWhere L is an auxiliary variable and β is a regularization parameter;
s302: the constraint in S301 is enforced using the enhanced lagrangian framework, and the expression of the AL function is:
where Q is an auxiliary variable of V, Λ S 、Λ V 、Λ Z Is an augmented Lagrangian multiplier, Λ' S 、Λ′ V 、Λ′ Z Is the inverse of the Lagrangian multiplier, beta S 、β V 、β U 、β z Is a penalty parameter, solving for the variables U, V, Q, L, S, Z using an alternating learning minimization strategy;
s303: decomposing into six sub-problems, resolving and solving all the sub-problems through a minimized formula (3), resolving one variable at a time, and keeping other variables unchanged; the six sub-problems include: l sub-problem, U sub-problem, Q sub-problem, V sub-problem, S sub-problem, Z sub-problem.
Further, the method for resolving the L variable is as follows:
l sub-problem: ignoring all terms that are independent of the L variable, equation (3) is written as:
solving using the contraction rule:
in the formula, "+" is defined as (τ) + Convergence operator of =max {0, τ }.
Further, the method for resolving the U variable is as follows:
u sub-problem: minimizing equation (3) yields a quadratic sub-problem with respect to the U variables:
the quadratic sub-problem solution is as follows:
wherein S is n 、V n 、V′ n To solve for the process variable of U, I is the identity matrix,is a penalty parameter.
The method of resolving the Q variable is as follows:
q sub-problem: obtained by minimizing formula (3) with respect to Q variable:
equation (8) is solved using the projection method specified in the following equation;
the projection equation is as follows:
if the F-norm of the Q variable is less than 1, q=v; otherwise, the variable V is a unit F-norm; due to Q n Is through V n Scaling results in that the F-norms are uniform.
Further, the method for resolving V variables is as follows:
v sub-problem: obtaining a quadratic sub-problem with respect to the V variable according to equation (3):
minimizing formula (10) with respect to V yields the following solution in closed form:
V n+1 ＝(β S U′ n+1 U n+1 +β V I) -1 (β S U′ n+1 S n +U′ n+1 Λ x +β V Q n+1 -Λ V ) (11)
further, the method for resolving the S variable is as follows:
s sub-problem: removing terms that are independent of the S variable according to equation (3) yields:
minimizing equation (12) with respect to S yields the following solution in closed form:
S n+1 ＝(β S I+β Z ) -1 (β S U n+1 V n+1 -Λ s +β Z Z n +Λ Z ) (13) further, the method of resolving the Z variable is as follows:
z sub-problem: neglecting constants independent of the Z variable according to equation (3) yields:
equation (14) is a fourier domain replacement problem, and is resolved as follows:
in the formula (15), A' is the inverse operation of A, and y is echo data;
and (3) performing calculation through loop iteration of the six sub-problems to obtain a solution of the formula (1).
Compared with the prior art, the invention has the beneficial effects that:
1. an alternative multiplier method is introduced to decompose the optimization algorithm into simple sub-problems for a plurality of complex problems, and the sub-problems are circularly and optimally solved, so that a high-resolution chromatographic SAR imaging result is obtained;
2. aiming at a sparse target point scene, the reconstruction result of a target area can still be ensured when the number of navigation passes is small, and the imaging of the ground and the canopy area can be realized more comprehensively;
3. the imaging quality is ensured, and the complexity of the process is reduced.
Drawings
Fig. 1 is a flowchart of a chromatographic SAR three-dimensional imaging method based on blind compressed sensing.
Fig. 2 is an L-band radar system parameter diagram in an embodiment.
Detailed Description
The invention is described in detail below with reference to the drawings and the detailed description.
Example 1
In this embodiment, as shown in fig. 1, a chromatographic SAR three-dimensional imaging method based on blind compressed sensing includes the following steps:
s1: : preprocessing a plurality of navigation SAR sequence data sets of the same imaging region in the obtained observation object, and sequentially constructing high-latitude signals one by pixels in each SAR image and arranging the signals into a matrix form; the aviation refers to repeated flying of airborne SAR at different height positions, wherein the flying tracks are all straight lines, and repeated observation is carried out on the same scene for a plurality of times;
s2: reconstructing image information by using a blind compressed sensing frame by utilizing the correlation of the elevation direction of the adjacent azimuth-distance units of the observed object, and modeling the image signal into the product of a sparse matrix and a dictionary matrix; establishing a chromatographic SAR imaging model based on blind compressed sensing; the image signals are the image signals in the preprocessed data set;
s3: continuously transforming and solving the chromatographic SAR imaging model based on the blind compressed sensing; and solving a target optimization problem by using an alternate multiplier method, and solving a sub-problem through variable alternate circulation, wherein the circulation is used for carrying out optimization solution on the sub-problem to obtain a high-resolution chromatographic SAR imaging result.
The working principle of the invention is as follows:
in the chromatographic SAR imaging process, the excessive number of navigation increases the calculated amount and the complexity, and the quality of imaging is affected by the insufficient number of navigation;
the invention combines a blind compressed sensing algorithm to establish a chromatographic SAR imaging model combined with blind compressed sensing, utilizes the structural characteristic of target sparseness, introduces the blind compressed sensing algorithm to process the elevation direction of an azimuth-distance unit of the target, combines the sparse characteristic of the target with the structural characteristic of the target, models the image into the product of a sparse matrix and a dictionary matrix by the blind compressed sensing scheme, adopts an alternate multiplier optimization algorithm to decompose a plurality of complex problems into simple sub-problems, solves the sub-problems through variable alternate circulation, and optimizes the sub-problems through circulation so as to obtain a high-resolution chromatographic SAR imaging result.
In this embodiment, the preprocessing includes: single vision complex image sequence registration, eliminating evil, phase compensation and baseline estimation.
Specifically, the expression of the chromatographic SAR imaging model based on the blind compressed sensing is as follows:
in the formula (1), U is a data fidelity term and a sparse coefficient, V is a dictionary matrix, A represents a chromatographic operator of a chromatographic SAR, y is echo data, lambda is a regularization parameter, and s.t. is a constraint condition; the dictionary matrix V is added with the F-norm of one unit;
in the formula (1)For ensuring consistency of data; />By using non-convex terms on U P (p<1) To promote sparsity of spatial coefficients; u and V are decoupled by introducing the constraint s=uv, where S is an auxiliary variable of UV.
More specifically, the solving step of the chromatographic SAR imaging model based on blind compressed sensing described in S3 is as follows:
s301: the convergence of the decoupling acceleration equation is performed by variable decomposition, introducing the constraint z=s, where Z is an auxiliary variable, and the constraint optimization problem is written as:
approximating the p-penalty of U in equation (2) asWhere L is an auxiliary variable and β is a regularization parameter;
s302: the constraint in S301 is enforced using the enhanced lagrangian framework, and the expression of the AL function is:
where Q is an auxiliary variable of V, Λ S 、Λ V 、A z Is an augmented Lagrangian multiplier, Λ' S 、Λ′ V 、Λ′ z Is the inverse of the Lagrangian multiplier, beta S 、β V 、β U 、β Z Is a penalty parameter, solving for the variables U, V, Q, L, S, Z using an alternating learning minimization strategy;
s303: decomposing into six sub-problems, resolving and solving all the sub-problems through a minimized formula (3), resolving one variable at a time, and keeping other variables unchanged; the six sub-problems include: l sub-problem, U sub-problem, Q sub-problem, V sub-problem, S sub-problem, Z sub-problem.
Example 2
Based on the chromatographic SAR three-dimensional imaging method based on blind compressed sensing described in embodiment 1, in this embodiment, six sub-problems and the analysis modes thereof are as follows:
the method for resolving the L variable is as follows:
l sub-problem: ignoring all terms that are independent of the L variable, equation (3) is written as:
solving using the contraction rule:
in the formula, "+" is defined as (τ) + Convergence operator of =max {0, τ }.
The method for analyzing the U variable is as follows:
u sub-problem: minimizing equation (3) yields a quadratic sub-problem with respect to the U variables:
the quadratic sub-problem solution is as follows:
wherein S is n 、V n 、V′ n To solve for the process variable of U, I is the identity matrix,is a penalty parameter.
The method of resolving the Q variable is as follows:
q sub-problem: obtained by minimizing formula (3) with respect to Q variable:
equation (8) is solved using the projection method specified in the following equation;
the projection equation is as follows:
if the F-norm of the Q variable is less than 1, q=v; otherwise, the variable V is a unit F-norm; due to Q n Is through V n Scaling results in that the F-norms are uniform.
The method for resolving the V variable is as follows:
v sub-problem: obtaining a quadratic sub-problem with respect to the V variable according to equation (3):
minimizing formula (10) with respect to V yields the following solution in closed form:
V n+1 ＝(β S U′ n+1 U n+1 +β V I) -1 (β S U′ n+1 S n +U′ n+1 Λ x +β V Q n+1 -Λ V ) (11)
the method for analyzing the S variable is as follows:
s sub-problem: removing terms that are independent of the S variable according to equation (3) yields:
minimizing equation (12) with respect to S yields the following solution in closed form:
S n+1 ＝(β S I+β Z ) -1 (β S U n+1 V n+1 -Λ s +β Z Z n +Λ Z ) (13)
the method for resolving the Z variable is as follows:
z sub-problem: neglecting constants independent of the Z variable according to equation (3) yields:
equation (14) is a fourier domain replacement problem, and is resolved as follows:
in the formula (15), A' is the inverse operation of A, and y is echo data;
and (3) performing calculation through loop iteration of the six sub-problems to obtain a solution of the formula (1). Solving the chromatographic SAR imaging model based on blind compressed sensing to obtain a high-resolution chromatographic SAR imaging result; the imaging quality is guaranteed, meanwhile, the complexity of the flow is reduced, and the computation complexity is reduced.
Example 3
Based on the chromatographic SAR three-dimensional imaging method based on blind compressed sensing described in the embodiment 1 and the embodiment 2, constructing a related experiment;
as shown in fig. 2, the experimental data is L-band data of HV polarized channel provided by an onboard F-SAR system of the german aerospace agency (Deutsches zentrum F u r Luftund Raumfahrt, DLR), and the acquisition area is a partial area of distance bits of the german truxenborne; the area is mainly composed of forests and lands.
In this example, experiments were performed using the parameters therein:
the radar center frequency of the experimental parameter is 1.325GHz, the azimuth resolution is 0.4m, the distance resolution is 1.5m, different types of methods are used for reconstructing data, and the methods are a classical spectrum estimation algorithm CAPON compressed sensing method and a chromatographic SAR imaging method based on a blind compressed sensing method respectively;
in the embodiment, the effectiveness of the imaging method is illustrated by mean square error (Mean Squared Error, MSE), when the signal-to-noise ratio is 5dB, the mean square error value of the imaging method is 0.0610, the mean square error value of the CAPN is 0.0932, and the mean square error value of the CS is 0.0854; when the signal-to-noise ratio is 25dB, the mean square error value of the imaging method is 0.0132, the mean square error value of the CAPON is 0.0297, and the mean square error value of the CS is 0.0215; compared with other methods, the CAPON has larger error and is not stable enough, CS and the imaging method have the trend of gradually reducing along with the change of the signal to noise ratio, but the imaging method has lower mean square error, and the imaging result is more stable. Therefore, compared with the traditional method, the imaging method can more accurately detect the ground and crown areas in the data, and the reconstruction accuracy of the target area is higher.
It is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (8)
1. A chromatographic SAR three-dimensional imaging method based on blind compressed sensing is characterized by comprising the following steps:
s1: preprocessing a plurality of navigation SAR sequence data sets of the same imaging region in the obtained observation object, and sequentially constructing high-latitude signals one by pixels in each SAR image and arranging the signals into a matrix form;
s2: reconstructing image information by using a blind compressed sensing frame by utilizing the correlation of the elevation direction of the adjacent azimuth-distance units of the observed object, and modeling the image signal into the product of a sparse matrix and a dictionary matrix; establishing a chromatographic SAR imaging model based on blind compressed sensing; the image signals are the image signals in the preprocessed data set;
s3: continuously transforming and solving the chromatographic SAR imaging model based on the blind compressed sensing; solving a target optimization problem by using an alternate multiplier method, and solving a sub-problem through variable alternate circulation, wherein the circulation is used for carrying out optimization solution on the sub-problem to obtain a high-resolution chromatographic SAR imaging result;
the expression of the chromatographic SAR imaging model based on the blind compressed sensing is as follows:
in the formula (1), U is a data fidelity term and a sparse coefficient, V is a dictionary matrix, A represents a chromatographic operator of a chromatographic SAR, y is echo data, lambda is a regularization parameter, and s.t. is a constraint condition; the dictionary matrix V is added with the F-norm of one unit;
in the formula (1)For ensuring consistency of data; lambdaiiUiid lP By using non-convex terms on U P ，p<A half-norm of 1 to promote sparsity of spatial coefficients; decoupling U and V by introducing the constraint s=uv, where S is an auxiliary variable of UV;
the solving step of the chromatographic SAR imaging model based on the blind compressed sensing is as follows:
s301: the convergence of the decoupling acceleration equation is performed by variable decomposition, introducing the constraint z=s, where Z is an auxiliary variable, and the constraint optimization problem is written as:
approximating the p-penalty of U in equation (2) asWhere L is an auxiliary variable and β is a regularization parameter;
s302: the constraint in S301 is enforced using the enhanced lagrangian framework, and the expression of the AL function is:
where Q is an auxiliary variable of V, Λ S 、Λ V 、Λ Z Is an augmented Lagrangian multiplier, Λ' S 、Λ′ V 、Λ′ Z Is the inverse of the Lagrangian multiplier, beta S 、β V 、β U 、β Z Is a penalty parameter, solving for the variables U, V, Q, L, S, Z using an alternating learning minimization strategy;
s303: decomposing into six sub-problems, resolving and solving all the sub-problems through a minimized formula (3), resolving one variable at a time, and keeping other variables unchanged; the six sub-problems include: l sub-problem, U sub-problem, Q sub-problem, V sub-problem, S sub-problem, Z sub-problem.
2. The method for chromatographic SAR three-dimensional imaging based on blind compressed sensing according to claim 1, wherein said preprocessing comprises: single vision complex image sequence registration, eliminating evil, phase compensation and baseline estimation.
3. The chromatographic SAR three-dimensional imaging method based on blind compressed sensing of claim 1, wherein the method for resolving the L-variable is as follows:
l sub-problem: ignoring all terms that are independent of the L variable, equation (3) is written as:
solving using the contraction rule:
in the formula, "+" is defined as (τ) + Convergence operator of =max {0, τ }.
4. A method of tomosynthesis SAR three-dimensional imaging based on blind compressed sensing according to claim 3, wherein the method of resolving the U-variable is as follows:
u sub-problem: minimizing equation (3) yields a quadratic sub-problem with respect to the U variables:
the quadratic sub-problem solution is as follows:
wherein S is n 、V n 、V′ n To solve for the process variable of U, I is the identity matrix,is a penalty parameter.
5. The method for three-dimensional imaging of chromatographic SAR based on blind compressed sensing as set forth in claim 4, wherein the method for resolving the Q variable is as follows:
q sub-problem: obtained by minimizing formula (3) with respect to Q variable:
equation (8) is solved using the projection method specified in the following equation;
the projection equation is as follows:
if the F-norm of the Q variable is less than 1, q=v; otherwise, the variable V is a unit F-norm; due to Q n Is through V n Scaling results in that the F-norms are uniform.
6. The method for three-dimensional imaging of chromatographic SAR based on blind compressed sensing as set forth in claim 5, wherein the method for resolving the V variable is as follows:
v sub-problem: obtaining a quadratic sub-problem with respect to the V variable according to equation (3):
minimizing formula (10) with respect to V yields the following solution in closed form:
V n+1 ＝(β S U′ n+1 U n+1 +β V I) -1 (β S U′ n+1 S n +U′ n+1 Λ x +β V Q n+1 -Λ V ) (11)。
7. the method for three-dimensional imaging of a chromatographic SAR based on blind compressed sensing as set forth in claim 6, wherein the method for resolving S-variables is as follows:
s sub-problem: removing terms that are independent of the S variable according to equation (3) yields:
minimizing equation (12) with respect to S yields the following solution in closed form:
S n+1 ＝(β s I+β Z ) -1 (β s U n+1 V n+1 -Λ s +β Z Z n +Λ Z ) (13)。
8. the method for three-dimensional imaging of a tomographic SAR based on blind compressed sensing as set forth in claim 7, wherein the method for resolving the Z-variable is as follows:
z sub-problem: neglecting constants independent of the Z variable according to equation (3) yields:
equation (14) is a fourier domain replacement problem, and is resolved as follows:
in the formula (15), A' is the inverse operation of A, and y is echo data;
and (3) performing calculation through loop iteration of the six sub-problems to obtain a solution of the formula (1).
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103076608A (en) * | 2013-01-27 | 2013-05-01 | 西安电子科技大学 | Contour-enhanced beaming-type synthetic aperture radar imaging method |
| CN103675817A (en) * | 2013-11-21 | 2014-03-26 | 中国科学院电子学研究所 | Synthetic aperture radar side-view three-dimensional imaging method based on transformation domain sparseness |
| CN105388476A (en) * | 2015-12-28 | 2016-03-09 | 河南工业大学 | SAR imaging method based on joint sparsity model |
| CN107133930A (en) * | 2017-04-30 | 2017-09-05 | 天津大学 | Ranks missing image fill method with rarefaction representation is rebuild based on low-rank matrix |
| CN110702026A (en) * | 2019-08-27 | 2020-01-17 | 广东工业大学 | Flatness three-dimensional shape detection device based on complex beam angle adaptive optics and processing method thereof |
| CN113777607A (en) * | 2021-09-14 | 2021-12-10 | 电子科技大学 | Video SAR imaging method |
| CN114966687A (en) * | 2022-05-27 | 2022-08-30 | 河南工业大学 | Sparse ISAR imaging method and system based on low rank and non-local self-similarity |
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103076608A (en) * | 2013-01-27 | 2013-05-01 | 西安电子科技大学 | Contour-enhanced beaming-type synthetic aperture radar imaging method |
| CN103675817A (en) * | 2013-11-21 | 2014-03-26 | 中国科学院电子学研究所 | Synthetic aperture radar side-view three-dimensional imaging method based on transformation domain sparseness |
| CN105388476A (en) * | 2015-12-28 | 2016-03-09 | 河南工业大学 | SAR imaging method based on joint sparsity model |
| CN107133930A (en) * | 2017-04-30 | 2017-09-05 | 天津大学 | Ranks missing image fill method with rarefaction representation is rebuild based on low-rank matrix |
| CN110702026A (en) * | 2019-08-27 | 2020-01-17 | 广东工业大学 | Flatness three-dimensional shape detection device based on complex beam angle adaptive optics and processing method thereof |
| CN113777607A (en) * | 2021-09-14 | 2021-12-10 | 电子科技大学 | Video SAR imaging method |
| CN114966687A (en) * | 2022-05-27 | 2022-08-30 | 河南工业大学 | Sparse ISAR imaging method and system based on low rank and non-local self-similarity |
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