Method, device and equipment for determining three-dimensional scattering information of observation target and medium

Abstract

The present application provides a kind of determination method, device, equipment and medium for observing target three-dimensional scattering information, which is realized by SAR tomography method based on morphological regularization, and the determination method comprises: using the sparse characteristics of strong scattering point less in single pixel point of observation target and the characteristics that target scattering coefficient has continuity in space structure, combining L1 regularization term and morphological regularization term, to construct SAR tomography model based on morphological regularization;SAR tomography model is solved based on the alternating direction multiplier algorithm of geometric constraint, to determine the three-dimensional scattering information of observation target.The determination method, device, equipment and medium can improve the positioning accuracy of reconstructed target, enhance the continuity of reconstructed target in structure, and obtain more accurate three-dimensional scattering information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar data imaging, and particularly relates to a method and device for determining three-dimensional scattering information of an observed target, equipment and a medium. BACKGROUND

[0002] Synthetic Aperture Radar (SAR) is an active microwave remote sensing method, and has strong signal penetration and all-weather and all-time observation capability compared with optical remote sensing, and is one of important means for earth observation. SAR Tomography imaging technology extends the synthetic aperture principle to the elevation direction, uses multiple registered two-dimensional SAR complex image data at different viewing angles obtained by observing the same scene to synthesize the aperture in the elevation direction to obtain the elevation information, and thus obtains the three-dimensional scattering information of the observed target.

[0003] With the continuous development of SAR Tomography imaging technology, the requirement for imaging accuracy is higher and higher. Traditional SAR Tomography imaging methods include a Beamforming (BF) algorithm and a spectrum estimation algorithm, and are affected by the number of baselines and the distribution state. When the baseline interval does not satisfy the Nyquist sampling theorem, the resolution in the tomography direction will be reduced, thereby causing tomography direction blurring imaging. Moreover, when the echo data is under-sampled, the imaging resolution of the BF algorithm is limited by the Rayleigh criterion. Therefore, in actual application, the traditional imaging algorithm may be greatly limited, and thus cannot meet the requirement for high-precision imaging. SUMMARY

[0004] In view of the above technical problems, the present application provides a method and device for determining three-dimensional scattering information of an observed target, and equipment and a medium, which are used to at least partially solve the above technical problems.

[0005] Based on this, the present application provides a method for determining three-dimensional scattering information of an observed target. The determination method is realized by using a SAR Tomography imaging method based on morphological regularization. The determination method includes the following steps: using the sparse characteristics of a single pixel point of the observed target that has few strong scattering points and the characteristic that the target scattering coefficient has continuity in the spatial structure, combining an L1 regularization term and a morphological regularization term, and constructing a SAR Tomography imaging model based on morphological regularization; solving the SAR Tomography imaging model based on a geometric constraint alternating direction multiplier algorithm, and determining the three-dimensional scattering information of the observed target.

[0006] According to an embodiment of the present invention, constructing a SAR tomography model based on morphological regularization includes: for the m-th observation channel of SAR tomography, integrating the backscattering coefficient of the observed target along the elevation direction to obtain the continuous echo signal of the m-th observation channel; discretizing the continuous echo signal of the m-th observation channel to obtain the discrete echo signal of the m-th observation channel; and constructing a SAR tomography model based on morphological regularization based on the discrete echo signal, the L1 regularization term, and the morphological regularization term.

[0007] According to an embodiment of the present invention, the discrete echo signal is:

[0008]

[0009] Among them, y m Let s be the discrete echo signal obtained from the m-th channel SAR tomography, where m = 1, 2, ..., M, and M is the number of observation channels in the SAR tomography. n Let γ(s) represent the elevation value of the nth discrete point within the elevation direction, where n = 1, 2, ..., N, and N is the number of discretizations along the elevation direction. n ) represents the elevation value s of the observed target. n The scattering coefficient, ξ m =-2b m / λr represents the elevation frequency, b m Let λ be the vertical distance between the baseline of the m-th channel and the reference baseline, λ be the wavelength, r be the slant range between the SAR radar and the observed target, and ε be the vertical distance between the baseline and the reference baseline. m This represents the noise interference term for the m-th channel.

[0010] According to an embodiment of the present invention, a SAR tomography model based on morphological regularization for:

[0011]

[0012]

[0013] γ=[γ(s1), γ(s2),..., γ(s N )] T

[0014] y = [y1, y2, ..., y m ] T

[0015]

[0016] Where y is the observation vector of the observed target, Φ is the sparse observation matrix, γ is the scattering coefficient vector of the observed target along the elevation direction, ε is the noise interference vector, g(γ) is the morphological regularization term, λ1 is the parameter of the L1 regularization term, λ2 is the parameter of the morphological regularization term, α is the weight parameter, and 1 is a column vector of all 1s. A three-dimensional sphere is selected as the structuring element. The size of the structural element. and These represent the morphological opening and closing operations, respectively.

[0017] According to an embodiment of the present invention, the SAR tomography model is solved using a geometrically constrained alternating direction multiplier algorithm to determine the three-dimensional scattering information of the observed target. This includes: defining and initializing a first intermediate variable, a second intermediate variable, and a Lagrange multiplier variable; initializing the scattering coefficient vector; setting the augmented Lagrange penalty parameter, the maximum iteration step size, the number of iteration steps, and the iteration termination condition; solving for the point density and height statistics of the three-dimensional point cloud in the ground distance coordinate system based on the SAR tomography model; determining the height range of the observed target based on the point density and height statistics; and determining the height range of the observed target in the ground distance coordinate system. The elevation range in the coordinate system is transformed into the elevation range in the slant range coordinate system, and the sparse observation matrix is ​​updated. The gradient descent method is used to solve for and update the scattering coefficient vector of the observed target along the elevation direction. Based on the updated sparse observation matrix and scattering coefficient vector, the threshold iteration algorithm and the Bregman iteration algorithm are used to solve for the first and second intermediate variables, respectively. The Lagrange multiplier variables are updated, and the residuals are calculated by combining the first and second intermediate variables until the residuals meet the iteration termination condition. The three-dimensional scattering coefficients of the residuals that meet the iteration termination condition are used as the three-dimensional scattering information of the observed target.

[0018] According to an embodiment of the present invention, the method for solving the point density and height statistics of a three-dimensional point cloud in the ground-range coordinate system based on the SAR tomographic imaging model includes: normalizing the scattering coefficient vector, transforming the normalized scattering coefficient vector from the slant range coordinate system to the ground-range coordinate system to obtain the scattering coefficient vectors in the azimuth, range, and height directions in the ground-range coordinate system; obtaining the three-dimensional point cloud by setting a threshold for the scattering coefficient; and projecting the three-dimensional point cloud onto a two-dimensional plane with the azimuth and range directions as references to obtain the point density and height statistics.

[0019] According to an embodiment of the present invention, determining the height range of an observed target based on a point density statistical map and a height statistical map includes: extracting the facade centerline of the observed target based on the point density statistical map; estimating the height of the facade of the observed target based on the height statistical map; determining the geometric information of the observed target using the facade centerline and height; and determining the height range of the observed target based on the geometric information using the signal-to-noise ratio of the two-dimensional SAR image.

[0020] A second aspect of the present invention provides a device for determining the three-dimensional scattering information of an observed target. The device is implemented using a morphologically regularized SAR tomography method. The device includes: a construction module, used to construct a morphologically regularized SAR tomography model by utilizing the sparse characteristic of having few strong scattering points in a single pixel of the observed target and the characteristic that the target scattering coefficient has continuity in spatial structure, combined with L1 regularization and morphological regularization terms; and a solution module, used to solve the SAR tomography model based on a geometrically constrained alternating direction multiplier algorithm to determine the three-dimensional scattering information of the observed target.

[0021] A third aspect of the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described method.

[0022] A fourth aspect of the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to implement the above-described method.

[0023] The method, apparatus, device, and medium for determining the three-dimensional scattering information of an observation target provided by embodiments of the present invention have at least the following beneficial effects:

[0024] A morphologically regularized SAR tomography model is constructed based on L1 regularization and morphological regularization. L1 regularization can effectively suppress the elevation sidelobe effect of traditional SAR tomography methods and enhance the features of the reconstructed target. On this basis, by introducing morphological regularization, the generation of outliers in the reconstruction results is suppressed, the positioning accuracy of the reconstructed target is improved, and the difference in the scattering coefficient of the target within the three-dimensional structural elements is reduced, enhancing the structural continuity of the reconstructed target and thus obtaining more accurate three-dimensional scattering information.

[0025] Furthermore, the Alternating Directions Method of Multipliers (ADMM) algorithm based on geometric constraints is used for iterative solution. During the iterative solution process, the geometric information of the target is used to accurately estimate the position of the centerline of the target facade and the elevation direction, thereby improving the performance of the target reconstruction results in terms of positioning accuracy and structural continuity. Attached Figure Description

[0026] The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0027] Figure 1 The flowchart illustrating the method for determining the three-dimensional scattering information of an observation target provided by an embodiment of the present invention is shown.

[0028] Figure 2 A flowchart illustrating operation S120 provided according to an embodiment of the present invention is shown schematically.

[0029] Figure 3 A block diagram of a device for determining three-dimensional scattering information of an observation target according to an embodiment of the present invention is shown schematically.

[0030] Figure 4 A block diagram of an electronic device suitable for implementing the methods described above, according to an embodiment of the present invention, is shown schematically. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0032] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0033] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0034] In the description of this invention, it should be understood that the terms "longitudinal", "length", "circumferential", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the subsystem or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0035] Throughout the accompanying drawings, identical elements are represented by the same or similar reference numerals. Conventional structures or configurations may be omitted where they might cause confusion in understanding the invention. Furthermore, the shapes, sizes, and positional relationships of the components in the drawings do not reflect actual size, scale, or actual positional relationships. Additionally, any reference numerals placed between parentheses in the claims should not be construed as limiting the claims.

[0036] Similarly, to simplify the invention and aid in understanding one or more of the various disclosed aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. The use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicates that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0038] In realizing this invention, the applicant discovered through research that applying an L1-regularized 3D sparse reconstruction algorithm to SAR tomography not only overcomes the limitations of the Nyquist sampling theorem but also enables signal recovery even under downsampling conditions. However, the sparse constraints of L1 regularization can generate outliers under the interference of additive noise and speckle, and can underestimate the scattering coefficient of the reconstructed target, resulting in low elevation accuracy and structural discontinuities in the target's 3D point cloud after thresholding. Furthermore, morphological post-processing can enhance the continuity of the target's 3D structure, suppress outliers, and obtain more accurate and continuous reconstruction results. With the further development of compressed sensing theory, SAR tomography based on sparse reconstruction is no longer limited to regularization models with a single penalty term, but rather to composite regularization models with multiple penalty terms, utilizing multi-constraint information to achieve high-precision SAR tomographic 3D imaging. Therefore, this invention constructs a SAR tomography model based on morphological regularization, using morphological regularization terms to enhance the accuracy and continuity of the reconstruction results.

[0039] Figure 1The flowchart illustrating the method for determining the three-dimensional scattering information of an observation target provided by an embodiment of the present invention is shown.

[0040] like Figure 1 As shown, the method for determining the three-dimensional scattering information of the observed target is implemented using a morphologically regularized SAR tomography method, which may include, for example, operations S110 to S120.

[0041] In operation S110, taking advantage of the sparse characteristics of fewer strong scattering points in a single pixel of the observed target and the continuity of the target scattering coefficient in spatial structure, a SAR tomography model based on morphological regularization is constructed by combining L1 regularization term and morphological regularization term.

[0042] In this embodiment of the invention, the process of constructing a SAR tomography model based on morphological regularization can, for example, operate S111 to S113.

[0043] In operation S111, for the m-th observation channel of SAR tomography, the backscattering coefficient of the observed target is integrated along the elevation direction to obtain the continuous echo signal of the m-th observation channel.

[0044] In operation S112, the continuous echo signal of the m-th observation channel is discretized to obtain the discrete echo signal of the m-th observation channel.

[0045] In operation S113, a morphologically regularized SAR tomography model is constructed based on the discrete echo signal, L1 regularization term, and morphological regularization term.

[0046] For example, for a certain azimuth-range resolvable cell, at aperture position b m Radar focusing complex data of the m-th channel at location m The backscattering coefficient is the integral along the elevation upwards, and can be expressed as:

[0047]

[0048] in, Let γ(s) be the continuous echo signal obtained from the m-th channel SAR tomography, m = 1, 2, ..., M, where M is the number of observation channels in the SAR tomography, γ(s) is the backscattering coefficient of the target along the elevation direction s, Δs is the elevation range, and ξ is the backscattering coefficient. m =-2b m / λr represents the elevation frequency, b m ε represents the vertical distance between the baseline of the m-th channel and the reference baseline, λ is the wavelength, and r is the slant range between the SAR radar and the observed target. m This represents the noise interference term for the m-th channel.

[0049] Discretize the above equation along the elevation direction s, with an elevation sampling interval of ρ. s =Δs / (N-1), then the discrete form of the echo data of the m-th channel is expressed as:

[0050]

[0051] Among them, y m Let s be the discrete echo signal obtained from the SAR tomography of the m-th channel. n Let γ(s) represent the elevation value of the nth discrete point within the elevation direction, where n = 1, 2, ..., N, and N is the number of discretizations along the elevation direction. n ) represents the elevation value s of the observed target. n The scattering coefficient. Then the discrete form of the echo data can be approximately expressed as:

[0052] y = Φγ + ε

[0053] in, The observation vector of the single target signal is the observation vector of the target. For sparse observation matrices, Let be the scattering coefficient vector of the observed target along the elevation direction. This is the noise interference vector.

[0054] The expression for the observation matrix Φ is:

[0055]

[0056] The observation vector of a single target signal can be represented as:

[0057] y = [y1, y2, ..., y m ] T

[0058] The scattering coefficient vector of the observed target along the elevation direction can be expressed as:

[0059] γ=[γ(s1), γ(s2),..., γ(s N )] T

[0060] Where T represents transpose.

[0061] Based on the sparse reconstruction criterion, a morphologically regularized SAR tomography model is constructed by jointly using morphological regularization terms. as follows:

[0062]

[0063] Where λ1 is the parameter of the L1 regularization term. g(γ) is the morphological regularization term, and its expression is:

[0064]

[0065] Where λ2 is the parameter of the morphological regularization term, α is the weight parameter, and 1 is a column vector of all 1s. The size of the structuring element (SE) A three-dimensional sphere is selected as SE. Indicates the size is SE. and Let represent the morphological opening and closing operations, respectively, and their expressions are:

[0066]

[0067]

[0068] in, and These represent the morphological erosion and dilation operations, respectively, and their expressions are as follows:

[0069]

[0070]

[0071] in, For x i Centered The included pixels, i = 1, 2, ..., Num, where Num is the total number of pixels in X, x r Indicates that it is located at The value at that location.

[0072] In operation S120, the alternating direction multiplier algorithm based on geometric constraints is used to solve the SAR tomography model and determine the three-dimensional scattering information of the observed target.

[0073] Figure 2 A flowchart illustrating operation S120 provided according to an embodiment of the present invention is shown schematically.

[0074] like Figure 2 As shown, operation S120 may include, for example, operations S121 to S127.

[0075] In operation S121, define and initialize the first intermediate variable, the second intermediate variable, and the Lagrange multiplier variable, initialize the scattering coefficient vector, and set the augmented Lagrange penalty parameter, the maximum iteration step size, the number of iterations, and the iteration termination condition.

[0076] For example, the iteration parameters are initialized as follows: the multi-channel SAR complex image is y, the sparse observation matrix is ​​Φ, and the scattering coefficient vector is initialized to γ. (0) =0, the first intermediate variable is initialized to The second intermediate variable is initialized to The Lagrange multiplier variables are The augmented Lagrange penalty parameter is μ. The maximum iteration step size is set to T. max Let the initial value of the iteration step be t = 0, and the iteration termination condition be ∈.

[0077] In operation S122, the point density and height statistics of the three-dimensional point cloud in the ground distance coordinate system are solved based on the SAR tomographic imaging model.

[0078] In this embodiment of the invention, the process of obtaining the point density map and height map can be as follows: The scattering coefficient vector is normalized, and the normalized scattering coefficient vector is transformed from the slant range coordinate system to the ground range coordinate system to obtain the scattering coefficient vectors in the azimuth, range, and height directions under the ground range coordinate system. A three-dimensional point cloud is obtained by setting a threshold for the scattering coefficient. The three-dimensional point cloud is projected onto a two-dimensional plane with the azimuth and range directions as references to obtain the point density map and height map.

[0079] For example, the normalized three-dimensional scattering coefficient vector γ (t) Transform from slant distance coordinate system (a, r, s) to ground distance coordinate system (g) x g y g z ), where a, r, and s are the azimuth, slant range, and elevation directions in the slant range coordinate system, respectively, and g x g y and g z These represent the azimuth, range, and altitude directions in the ground-distance coordinate system, respectively, and their expressions are:

[0080]

[0081] Where α is the baseline oblique angle and θ is the downward angle.

[0082] By setting a threshold γ th Obtain a 3D point cloud ∈ [0, 1], at a distance of from the ground from the 2D plane (g x g y Projecting the point cloud yields a point density statistical map P1(g) of the three-dimensional point cloud. x g y ) and height statistics H1(g x g y ), where P1(g x g y Pixel P1(g) in ) x g yH1(g) is defined as the number of projection points of a 3D point cloud onto a 2D plane at coordinates (x, y) above the ground. x g y Pixel H1(g) in ) x g y The location is defined as the distance between the three-dimensional point cloud and the two-dimensional plane coordinates (g). x g y The maximum height of all projected points at ().

[0083] In operation S123, the height range of the observed target is determined based on the point density statistics map and the height statistics map.

[0084] In this embodiment of the invention, the process of determining the height range of an observed target may include: extracting the facade centerline of the observed target based on a point density statistical map; estimating the height of the facade of the observed target based on a height statistical map; determining the geometric information of the observed target using the facade centerline and height; and determining the height range of the observed target based on the geometric information using the signal-to-noise ratio of a two-dimensional SAR image.

[0085] For example, mean filtering is used for P1(g) x g y After processing, P2(g) is obtained. x g y Assume the size of the mean filter is N. w The specific calculation formula is as follows:

[0086]

[0087] Set point density threshold P th ∈[0,1], for P2(g) x g y Perform pixel-by-pixel thresholding to obtain the Boolean image P. bool (g x g y ):

[0088]

[0089] Parallel straight lines are constructed using the tangent of the baseline slope α (tanα) as the slope. The number of non-zero pixels and the two-dimensional ground distance plane coordinates (g) of different straight lines are counted. x g y The mean of the non-zero pixel coordinates on different straight lines is calculated and used as the extraction result of the center line of the urban target facade.

[0090] H1(g) x g y ) and P bool (g x g yMultiplying these two values ​​yields the height statistics chart H2(g) of the city's target facades. x g y The specific calculation formula is as follows:

[0091] H2(g x g y ) = P bool (g x g y )·H1(g x g y )

[0092] Based on the elevation range, different height layers are defined, and a layered height statistical map H of the target is obtained. p (g x g y ), statistics H p (g x g y The number of non-zero pixels in each layer is used to estimate the height of the city target facade. The center height corresponding to the height statistics map of the layer with the most non-zero pixels is taken as the height estimation result.

[0093] The geometric information h(g) of the urban target in the ground distance coordinate system is formed by extracting the centerline of the urban target facade and estimating its height. x g y The height range h of the target point is determined using the signal-to-noise ratio (SNR) of a 2D SAR image. rg (g x g y Specifically:

[0094] When SNR(g) x g y ) < SNR th hour,

[0095] h rg (g x g y )=h(g x g y );

[0096] When SNR(g) x g y ) < SNR th hour,

[0097] h rg (g x g y )∈{[h(g x g y )-Δh1,h(g x g y )+Δh1]∪[h(gx,gy )-Δh2,h(g x g y )+Δh2]},

[0098] Among them, SNR th For the signal-to-noise ratio threshold, Δh1=(SNR(g) x g y -SNR th )·Δh / 2, where Δh is the proportionality coefficient and Δh2 is the constant term.

[0099] In operation S124, the height range of the observed target in the ground distance coordinate system is transformed into the elevation range s in the slant distance coordinate system. rg (a, r), update the sparse observation matrix Φ.

[0100] In operation S125, the gradient descent method is used to solve and update the scattering coefficient vector of the observed target along the elevation direction.

[0101] For example, γ is solved and updated using the gradient method. (t+1) The specific expression is:

[0102]

[0103] In operation S126, based on the updated sparse observation matrix and scattering coefficient vector, the first and second intermediate variables are solved using the threshold iteration algorithm and the Bregman iteration algorithm, respectively. The Lagrange multiplier variables are updated, and the residuals are calculated by combining the first and second intermediate variables until the residuals satisfy the iteration termination condition.

[0104] For example, update

[0105]

[0106] Solve using the Iterative Shrinkage-Thresholding Algorithm (ISTA):

[0107]

[0108]

[0109] Where sgn(·) is the sign function.

[0110] renew

[0111]

[0112]

[0113] Solve using the Bregman iterative algorithm:

[0114]

[0115]

[0116]

[0117] Where χ>0 is a parameter. It means that g(X) is in X (t) The subgradient at a given point, according to the chain rule:

[0118]

[0119] in accordance with and Definition, Then we have:

[0120]

[0121]

[0122] Where i, k = 1, 2, ..., Num. Assume X = {x1, x2, ..., xn} Num}, then we have:

[0123]

[0124]

[0125] Updated d (t+1) :

[0126] d (t+1) =d (t) -Gγ (t+1) +z (t+1)

[0127] in, G is [I] T I T ] T .

[0128] Calculate the iteration parameters:

[0129] res=||γ (t+1) -γ (t) ||2 / ||γ (t) ||2

[0130] t = t + 1

[0131] Determine whether both res > ∈ T and t < T are satisfied simultaneously. max If yes, stop the iteration; otherwise, return to step S122.

[0132] In operation S127, the three-dimensional scattering coefficients of the residuals that satisfy the iteration termination condition are used as the three-dimensional scattering information of the observed target.

[0133] Based on the same inventive concept, embodiments of the present invention also provide a device for determining the three-dimensional scattering information of an observed target.

[0134] Figure 3 A block diagram of a device for determining three-dimensional scattering information of an observation target according to an embodiment of the present invention is shown schematically.

[0135] like Figure 3 As shown, the device 300 for determining the three-dimensional scattering information of the observed target may include, for example, a construction module 310 and a solution module 320.

[0136] Module 310 is used to construct a SAR tomography model based on morphological regularization by taking advantage of the sparse characteristics of the small number of strong scattering points in a single pixel of the observed target and the continuity of the target scattering coefficient in the spatial structure, combined with L1 regularization and morphological regularization terms.

[0137] The solver module 320 is used to solve the SAR tomography model based on the alternating direction multiplier algorithm with geometric constraints to determine the three-dimensional scattering information of the observed target.

[0138] It should be noted that the implementation details and technical effects of the device for determining the three-dimensional scattering information of the observed target correspond to the implementation details and technical effects of the method for determining the three-dimensional scattering information of the observed target in the embodiment section, and will not be repeated here.

[0139] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention, or at least part of the functions of any one or more of them, can be implemented in a single module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be implemented by being divided into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be at least partially implemented as hardware circuits, such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits (ASICs), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, and firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0140] For example, any plurality of the building module 310 and the solving module 320 can be combined into one module / unit / subunit, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits can be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of the present invention, at least one of the building module 310 and the solving module 320 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the building module 310 and the solving module 320 can be at least partially implemented as a computer program module, which, when run, can perform corresponding functions.

[0141] Figure 4 A block diagram of an electronic device suitable for implementing the methods described above, according to an embodiment of the present invention, is shown schematically. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0142] like Figure 4As shown, an electronic device 400 according to an embodiment of the present invention includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage portion 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0143] RAM 403 stores various programs and data required for the operation of electronic device 400. Processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Processor 401 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 402 and / or RAM 403. It should be noted that programs may also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in one or more memories.

[0144] According to an embodiment of the present invention, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to a bus 404. The electronic device 400 may also include one or more of the following components connected to the I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0145] According to embodiments of the present invention, the method flow according to embodiments of the present invention can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by processor 401, it performs the functions defined in the system of the embodiments of the present invention. According to embodiments of the present invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0146] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0147] According to embodiments of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0148] For example, according to embodiments of the present invention, a computer-readable storage medium may include one or more memories other than ROM 402 and / or RAM 403 described above.

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or pairings fall within the scope of this invention.

Claims

1. A method for determining three-dimensional scattering information of an observed target, characterized in that, The determination method adopts a SAR tomography method based on morphological regularization, and comprises the following steps: A SAR tomography model based on morphological regularization is constructed by using the sparse characteristics of the observation target single pixel point and the continuity of the target scattering coefficient in the spatial structure, combining an L1 regularization term and a morphological regularization term; The SAR tomography model is solved based on an alternating direction multiplier algorithm with geometric constraints to determine the three-dimensional scattering information of the observation target; In the formula, the SAR tomography model based on morphological regularization is is: wherein, is the observation vector of the observation target, is the sparse observation matrix, is the scattering coefficient vector of the observation target along the elevation direction, is the noise interference vector, is the morphological regularization term, is the parameter of the L1 regularization term, is the parameter of the morphological regularization term, is the weight parameter, is the all-one column vector, and a three-dimensional sphere is selected as the structural element, is the size of the structural element, and represent the morphological open operation and the close operation, respectively; , is the number of observation channels of the SAR tomography, represents the elevation value of the discrete point in the elevation direction, , is the number of discretization along the elevation direction, is the scattering coefficient of the observation target at the elevation value , represents the elevation frequency, is the vertical distance between the channel baseline and the reference baseline, is the wavelength, is the slant range of the SAR radar and the observation target.

2. The determination method according to claim 1, characterized in that, The SAR tomography model based on morphological regularization is constructed by the following steps: For the mth observation channel of SAR tomography, the backscattering coefficient of the observation target is integrated along the height direction to obtain the continuous echo signal of the mth observation channel; The continuous echo signal of the mth observation channel is discretized to obtain the discrete echo signal of the mth observation channel; The SAR tomography model based on morphological regularization is constructed according to the discrete echo signal, the L1 regularization term and the morphological regularization term.

3. The determination method according to claim 2, characterized in that, The discrete echo signal is: wherein, is the discrete echo signal obtained from the th channel SAR tomography, , is the number of observation channels of the SAR tomography, denotes the elevation value of the th discrete point in the elevation direction, , is the number of discretization along the elevation direction, is the scattering coefficient of the observation target at the elevation value , denotes the elevation direction frequency, is the vertical distance between the th channel baseline and the reference baseline, is the wavelength, is the slant range between the SAR radar and the observation target, is the noise disturbance term of the th channel.

4. The determination method according to claim 1, characterized in that, The SAR tomography model is solved based on the alternating direction multiplier algorithm with geometric constraints to determine the three-dimensional scattering information of the observation target, which comprises the following steps: A first intermediate variable, a second intermediate variable and a Lagrange multiplier variable are defined and initialized, the scattering coefficient vector is initialized, the augmented Lagrange penalty parameter, the maximum iteration step, the iteration step number and the iteration termination condition are set; Point density statistics and height statistics of the three-dimensional point cloud in the geodetic coordinate system are solved based on the SAR tomography model; The height direction range of the observation target is determined according to the point density statistics and the height statistics; The height direction range of the observation target in the geodetic coordinate system is converted into the height direction range in the slant range coordinate system, and the sparse observation matrix is updated; The scattering coefficient vector of the observation target along the height direction is solved and updated by using the gradient descent method; The first intermediate variable and the second intermediate variable are solved by using the threshold iteration algorithm and the Bregman iteration algorithm respectively based on the updated sparse observation matrix and the scattering coefficient vector; The Lagrange multiplier variable is updated, and the residual error is calculated in combination with the first intermediate variable and the second intermediate variable until the residual error meets the iteration termination condition; The three-dimensional scattering coefficient under the condition that the residual error meets the iteration termination condition is taken as the three-dimensional scattering information of the observation target.

5. The determination method according to claim 4, characterized in that, The point density statistics and the height statistics of the three-dimensional point cloud in the geodetic coordinate system are solved based on the SAR tomography model, which comprises the following steps: The scattering coefficient vector is normalized, and the normalized scattering coefficient vector is converted from the slant range coordinate system to the geodetic coordinate system to obtain the scattering coefficient vector in the azimuth direction, the range direction and the height direction in the geodetic coordinate system; The three-dimensional point cloud is obtained by setting the threshold value of the scattering coefficient; The point density statistics and the height statistics are obtained by projecting the three-dimensional point cloud to a two-dimensional plane with the azimuth direction and the range direction as the reference.

6. The determination method according to claim 4, characterized in that, The height direction range of the observation target is determined according to the point density statistics and the height statistics, which comprises the following steps: The facade center line of the observation target is extracted according to the point density statistics; The height of the facade of the observation target is estimated according to the height statistics. Determine geometric information of the observation target by using facade center lines and heights of the observation target; Determine height range of the observation target by using signal-to-noise ratio of a two-dimensional SAR image based on the geometric information.

7. A device for determining three-dimensional scattering information of an object of interest, characterized in that The determining device is implemented by using a SAR tomography method based on morphological regularization, and comprises: A construction module, configured to construct a SAR tomography model based on morphological regularization by using sparse characteristics of a single pixel point of the observation target, i.e. less strong scattering points, and characteristics that target scattering coefficients have continuity in spatial structure, combining an L1 regularization term and a morphological regularization term; A solving module, configured to solve the SAR tomography model based on an alternating direction multiplier algorithm of geometric constraints to determine three-dimensional scattering information of the observation target. In the formula, the SAR tomography model based on morphological regularization is is: wherein, is the observation vector of the observation target, is the sparse observation matrix, is the scattering coefficient vector of the observation target along the elevation direction, is the noise interference vector, is the morphological regularization term, is the parameter of the L1 regularization term, is the parameter of the morphological regularization term, is the weight parameter, is the all-one column vector, and a three-dimensional sphere is selected as the structural element, is the size of the structural element, and represent the morphological opening operation and the closing operation, respectively; , is the number of observation channels of the SAR tomography, represents the elevation value of the discrete point in the elevation direction, , is the number of discretization along the elevation direction, is the scattering coefficient of the observation target at the elevation value , represents the elevation frequency, is the vertical distance between the channel baseline and the reference baseline, is the wavelength, is the slant range of the SAR radar and the observation target.

8. An electronic device, comprising: Comprise: One or more processors; Memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more programs make the one or more processors implement the method in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Executable instructions are stored thereon, which make the processor implement the method in any one of claims 1 to 6 when executed by the processor.

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