Volume scattering model determination device and volume scattering model determination method

Abstract

This volume scattering model determination device is configured so as to comprise: a covariance matrix acquiring unit (1) that acquires a covariance matrix corresponding to a scattering matrix of an observation target obtained by alternately radiating two mutually orthogonal polarized waves toward the observation target; and a parameter setting unit (2) that sets, as model parameters of a generalized volume scattering model having the scattering matrices of a plurality of scattered bodies mapped thereto, a plurality of values of model parameters including parameters respectively indicating the scattering matrix elements of each scattered body, the distribution of the plurality of scattered bodies, and the average tilt angle of the plurality of scattered bodies, and outputs a plurality of generalized volume scattering models including the model parameters that were set. The volume scattering model determination device further comprises: a coefficient adjusting unit (3) that multiplies each of the plurality of generalized volume scattering models output by the parameter setting unit (2) by a fitting coefficient and adjusts each fitting coefficient such that the difference between the covariance matrix and each of the generalized volume scattering models after being multiplied by the fitting coefficient decreases; and a model selecting unit (4) that compares the plurality of differences after the fitting coefficient adjustment by the coefficient adjusting unit (3) to one another, and selects a generalized volume scattering model from among the plurality of generalized volume scattering models output by the parameter setting unit (2) on the basis of the results of comparing the differences.

Description

[Technical Field]

[0001] The present disclosure relates to a volume scattering model determination device and a volume scattering model determination method. [Background technology]

[0002] There is a volume scattering model determination device that determines a volume scattering model from scattering data obtained by irradiating a target with polarized waves. A volume scattering model is a model that represents radio wave scattering from multiple scatterers distributed in space. As an example of such a volume scattering model determination device, Patent Document 1 discloses a device that determines an ungeneralized volume scattering model using scattering components contained in scattering data. The ungeneralized volume scattering model is a model in which scatterers are fixed to dipoles, and the distribution of multiple scatterers and the average tilt angle of the multiple scatterers are both fixed. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-180865 Summary of the Invention [Problem to be solved by the invention]

[0004] The device disclosed in Patent Document 1 has the problem that it may not be possible to correctly obtain a volume scattering model that simulates the features of the scatterers when the scatterers are not dipoles, when the distribution of multiple scatterers dispersed in space differs from a fixed distribution, or when the average tilt angle of the multiple scatterers differs from a fixed average tilt angle.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a volume scattering model determination device that can obtain a generalized volume scattering model that simulates the features of a scatterer. [Means for solving the problem]

[0006] A volume scattering model determination device according to the present disclosure includes a covariance matrix acquisition unit that acquires a covariance matrix corresponding to the scattering matrix of an observation target obtained by alternately irradiating the observation target with two mutually orthogonal polarized waves, and a parameter setting unit that sets a plurality of model parameter values ​​as model parameters of a generalized volume scattering model onto which the scattering matrices of a plurality of scatterers are mapped, the model parameters including parameters indicating elements of the scattering matrix of each scatterer, the distribution of the plurality of scatterers, and the average tilt angle of the plurality of scatterers, and outputs a plurality of generalized volume scattering models including the set model parameters. The volume scattering model determination device also includes a coefficient adjustment unit that multiplies each of the plurality of generalized volume scattering models output from the parameter setting unit by a fitting coefficient and adjusts each fitting coefficient so that a difference between each generalized volume scattering model and the covariance matrix after multiplication by the fitting coefficient is small, and a model selection unit that compares the plurality of differences after adjustment of the fitting coefficients by the coefficient adjustment unit and selects one of the plurality of generalized volume scattering models output from the parameter setting unit based on the comparison result of the differences. [Effects of the Invention]

[0007] According to the present disclosure, a generalized volume scattering model that simulates the feature quantities of a scatterer can be obtained. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a configuration diagram showing a volume scattering model determination device according to a first embodiment. [Figure 2] 1 is a hardware configuration diagram showing hardware of a volume scattering model determination device according to a first embodiment. [Figure 3] FIG. 10 is a hardware configuration diagram of a computer when the volume scattering model determination device is realized by software, firmware, or the like. [Figure 4]10 is a flowchart showing a volume scattering model determination method, which is a processing procedure of the volume scattering model determination device. [Figure 5] FIG. 10 is an explanatory diagram showing the relationship between the fitting coefficient xg and the eigenvalues ​​λ1, λ2, and λ3 of [C0]. [Figure 6] FIG. 1 is an explanatory diagram showing a dipole tilted vertically in the plane of polarization. [Figure 7] FIG. 1 is an explanatory diagram showing a dipole tilted by ψ in the plane of polarization. [Figure 8] Figure 8A is an explanatory diagram showing the relationship between the fitting coefficient xg and the eigenvalues ​​λ1, λ2, and λ3 of [C0] when <[Cm]> and <[Cvg(α,β,σ,ψ0)]> do not match, and Figure 8B is an explanatory diagram showing the relationship between the fitting coefficient xg and the eigenvalues ​​λ1, λ2, and λ3 of [C0] when <[Cm]> and <[Cvg(α,β,σ,ψ0)]> match. [Figure 9] FIG. 1 is an explanatory diagram showing that the sum of eigenvalues ​​becomes zero when x0=(A+B+C) / (a+b+c). [Figure 10] FIG. 1 is an explanatory diagram showing an example of a PS (Polarimetric Signature) shape of an object simulated by a generalized volume scattering model Cvg. [Figure 11] FIG. 10 is a configuration diagram showing a volume scattering model determination device according to a second embodiment. [Figure 12] FIG. 10 is a hardware configuration diagram showing hardware of a volume scattering model determination device according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] In order to explain the present disclosure in more detail, embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0010] Embodiment 1 FIG. 1 is a configuration diagram showing a volume scattering model determination device according to the first embodiment. FIG. 2 is a hardware configuration diagram showing the hardware of the volume scattering model determination device according to the first embodiment. The volume scattering model determination device shown in FIG. 1 includes a covariance matrix acquisition unit 1, a parameter setting unit 2, a coefficient adjustment unit 3, and a model selection unit 4.

[0011] The covariance matrix acquisition unit 1 is realized by, for example, a covariance matrix acquisition circuit 11 shown in FIG. The covariance matrix acquisition unit 1 acquires a covariance matrix Cm corresponding to the scattering matrix [S] of the observation target obtained by alternately irradiating the observation target with two mutually orthogonal polarized waves. The observation target contains one or more scatterers dispersed in space. The covariance matrix acquisition unit 1 outputs the covariance matrix Cm to the coefficient adjustment unit 3.

[0012] The parameter setting unit 2 is realized by, for example, a parameter setting circuit 12 shown in FIG. The parameter setting unit 2 sets multiple values ​​of model parameters (α, β, σ, ψ0) as model parameters of the generalized volume scattering model Cv onto which the scattering matrix [S] of multiple scatterers is mapped, including parameters α and β (see equation (1) described below) indicating elements of the scattering matrix of each scatterer, parameter σ indicating the distribution of multiple scatterers, and parameter ψ0 indicating the average tilt angle of the multiple scatterers. The generalized volume scattering model Cv is a model in which the distribution of multiple scatterers or the average tilt angle of the multiple scatterers can be set arbitrarily. The model parameter α is an element of the scattering matrix [S], and the model parameter α = |α|exp(jψ α The range of α is 0≦|α|≦+1. α ≦+180. The model parameter β is an element of the scattering matrix [S], and the model parameter β = |β|exp(jψ β The range of β is 0≦|β|≦+1. β ≦+180. The model parameter σ is a parameter that indicates the distribution of multiple scatterers, and the value range of the model parameter σ is 0≦σ≦(π 2 / 12) 1 / 2 is. The model parameter ψ0 is a parameter that indicates the average tilt angle of a plurality of scatterers, and the value range of the model parameter ψ0 is −90≦ψ0≦+90. The parameter setting unit 2 outputs a plurality of generalized volume scattering models Cv including the set model parameters (α, β, σ, ψ 0 ) to the coefficient adjustment unit 3 and the model selection unit 4, respectively. Here, the model parameters included in the generalized volume scattering model Cv are (α, β, σ, ψ0). However, this is just one example, and the generalized volume scattering model Cv may include model parameters different from (α, β, σ, ψ0).

[0013] The coefficient adjustment unit 3 is realized by, for example, a coefficient adjustment circuit 13 shown in FIG. The coefficient adjustment unit 3 acquires the covariance matrix Cm from the covariance matrix acquisition unit 1 and acquires a plurality of generalized volume scattering models Cv from the parameter setting unit 2. The coefficient adjustment unit 3 multiplies each of the plurality of generalized volume scattering models by a fitting coefficient x. The coefficient adjustment unit 3 adjusts each fitting coefficient x so that the difference between each generalized volume scattering model Cv and covariance matrix Cm after multiplication by the fitting coefficient becomes small. The coefficient adjustment unit 3 outputs a plurality of differences after adjusting the fitting coefficients to the model selection unit 4 as differences between the respective generalized volume scattering models Cv and the covariance matrices Cm.

[0014] The model selection unit 4 is realized by, for example, a model selection circuit 14 shown in FIG. The model selection unit 4 acquires a plurality of generalized volume scattering models Cv from the parameter setting unit 2, and acquires a plurality of differences after fitting coefficient adjustment from the coefficient adjustment unit 3. The model selection unit 4 compares the multiple differences with each other, and selects one of the multiple generalized volume scattering models Cv based on the comparison result of the multiple differences.

[0015] In Fig. 1, it is assumed that each of the components of the volume scattering model determination device, namely, a covariance matrix acquisition unit 1, a parameter setting unit 2, a coefficient adjustment unit 3, and a model selection unit 4, is realized by dedicated hardware as shown in Fig. 2. In other words, it is assumed that the volume scattering model determination device is realized by a covariance matrix acquisition circuit 11, a parameter setting circuit 12, a coefficient adjustment circuit 13, and a model selection circuit 14. Each of the covariance matrix acquisition circuit 11, the parameter setting circuit 12, the coefficient adjustment circuit 13, and the model selection circuit 14 corresponds to, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof.

[0016] The components of the volume scattering model determination device are not limited to those realized by dedicated hardware, and the volume scattering model determination device may be realized by software, firmware, or a combination of software and firmware. The software or firmware is stored as a program in the memory of a computer. A computer refers to hardware that executes the program, such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), central processing unit, processing unit, arithmetic unit, microprocessor, microcomputer, processor, or DSP (Digital Signal Processor).

[0017] FIG. 3 is a hardware configuration diagram of a computer when the volume scattering model determination device is realized by software, firmware, or the like. When the volume scattering model determination device is realized by software, firmware, or the like, a program for causing a computer to execute the respective processing procedures of the covariance matrix acquisition unit 1, the parameter setting unit 2, the coefficient adjustment unit 3, and the model selection unit 4 is stored in memory 21. Then, a processor 22 of the computer executes the program stored in memory 21.

[0018] 2 shows an example in which each of the components of the volume scattering model determination device is realized by dedicated hardware, and Fig. 3 shows an example in which the volume scattering model determination device is realized by software, firmware, etc. However, this is merely an example, and some of the components in the volume scattering model determination device may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, etc.

[0019] Next, the operation of the volume scattering model determination device shown in FIG. 1 will be described. FIG. 4 is a flowchart showing a volume scattering model determination method, which is a processing procedure of the volume scattering model determination device. FIG. 5 is an explanatory diagram showing an example of a generalized volume scattering model Cv onto which the scattering matrices [S] of five scatterers are mapped. The generalized volume scattering model Cv shown in Figure 5 is mapped with the scattering matrix [S] of a left-handed scatterer that rotates to the left, the scattering matrix [S] of a dextrorotatory scatterer that rotates to the right, the scattering matrix [S] of a dihedral CR scatterer that is a dihedral corner reflector, the scattering matrix [S] of either a dipole scatterer or a wire scatterer, and the scattering matrix [S] of either a spherical scatterer, a planar scatterer, or a three-sided CR scatterer. The scattering matrix [S] of a left-handed scatterer, the scattering matrix [S] of a right-handed scatterer, and the scattering matrix [S] of a three-sided CR or other scatterer are isotropic scatterers, while the scattering matrix [S] of a two-sided CR scatterer and the scattering matrix [S] of a dipole or other scatterer are anisotropic scatterers. The scattering matrix [S] of each scatterer contains elements represented by α and β, as shown in the following formula (1). In particular, the scattering matrix [S] of a left-handed scatterer is expressed as shown in the following formula (2), and the scattering matrix [S] of a right-handed scatterer is expressed as shown in the following formula (3). Furthermore, the scattering matrix [S] of a two-sided CR scatterer is expressed as shown in the following formula (4), the scattering matrix [S] of a dipole or other scatterer is expressed as shown in the following formula (5), and the scattering matrix [S] of a three-sided CR or other scatterer is expressed as shown in the following formula (6).

[0020] TIFF0007728492000001.tif84166

[0021] For example, when horizontally polarized H-polarized waves and vertically polarized V-polarized waves are irradiated onto a dipole tilted vertically in the polarization plane perpendicular to the direction of propagation of the radio waves, as shown in Figure 6, the scattering matrix [S dipole ] is expressed as the following equation (7). As shown in Figure 7, the scattering matrix [S dipole (ψ)] is expressed using a rotation matrix [R(ψ)] as shown in the following equation (8). FIG. 6 is an explanatory diagram showing a dipole tilted vertically in the plane of polarization. FIG. 7 is an explanatory diagram showing a dipole tilted by ψ in the plane of polarization. 6 and 7, the horizontal axis indicates the direction of H polarization, and the vertical axis indicates the direction of V polarization.

[0022] TIFF0007728492000002.tif27166

[0023] The scattering matrix of the scatterer [S dipole (ψ)], the corresponding covariance matrix [C dipole (ψ)] is expressed as the following equation (9).

[0024] TIFF0007728492000003.tif37166

[0025] Such scatterers are distributed as v Covariance matrix C when there are infinitely many according to (ψ) v is expressed as the following equation (10). Scatterer tilt angle and distribution P v By varying (ψ), it is possible to represent a large number of scatterer groups. Distribution P v By replacing with the simpler parameter ρ and solving the integral in equation (10), we can obtain the generalized volume scattering model Cv.

[0026] TIFF0007728492000004.tif19166

[0027] The covariance matrix acquisition unit 1 acquires a covariance matrix Cm corresponding to a scattering matrix [S] of an observation target obtained by alternately irradiating the observation target with two polarized waves that are orthogonal to each other. Specifically, the observation target is, for example, a plurality of observation areas AR1 to AR N When the covariance matrix acquisition unit 1 includes a plurality of observation areas AR1 to AR N From among these, one of the observation areas AR n A covariance matrix Cm corresponding to the scattering matrix [S] of the vector [sigma] is obtained (step ST1 in FIG. 4), where n=1, . . . , N, and N is an integer equal to or greater than 1. The covariance matrix acquisition unit 1 is n The covariance matrix Cm corresponding to the scattering matrix [S] is output to the coefficient adjustment unit 3. Here, one covariance matrix C corresponding to the scattering matrix [S] is 0 is expressed as the following equation (11).

[0028] TIFF0007728492000005.tif34166

[0029] covariance matrix C 0can be calculated from one pixel and corresponds to one scatterer. If a group of surrounding pixels is used to average from multiple scatterers, the covariance matrix Cm corresponding to the scattering matrix [S] can be calculated as shown in Equation (12) below.

[0030] TIFF0007728492000006.tif27166In equation (12), for example, M is 100 (10 pixels vertically by 10 pixels horizontally).

[0031] The parameter setting unit 2 sets a plurality of values ​​for the model parameters (α, β, σ, ψ 0 ) included in the generalized volume scattering model Cv onto which the scattering matrices [S] of a plurality of scatterers are mapped. Specifically, the parameter setting unit 2 sets G values ​​of the model parameters (α, β, σ, ψ) within the range of possible values ​​of the model parameters (α, β, σ, ψ) included in the generalized volume scattering model Cv (step ST2 in FIG. 4), where G is an integer equal to or greater than 2. The parameter setting unit 2 sets G generalized volume scattering models Cv1 to Cv2 including the set model parameters (α, β, σ, ψ0). G are output to the coefficient adjustment unit 3 and the model selection unit 4, where g=1, . . . , G.

[0032] The coefficient adjustment unit 3 acquires the covariance matrix Cm from the covariance matrix acquisition unit 1, and calculates G generalized volume scattering models Cv1 to Cv G Get. The coefficient adjustment unit 3 calculates the generalized volume scattering model Cv g (g=1, ,G) with fitting coefficient x g (g=1, ,G) Generalized volume scattering model Cv g is expressed as a matrix.

[0033] TIFF0007728492000007.tif17166In equation (13), C0 is a redundant component during fitting, and the smaller C0 is, the higher the fitting accuracy is.

[0034] The coefficient adjustment unit 3 calculates the generalized volume scattering model Cv g and the difference ΔC between the covariance matrix Cm g The fitting coefficient x is set so that [C0] is small. g (Step ST3 in FIG. 4). In the volume scattering model determination device shown in FIG. 1, the coefficient adjustment unit 3 adjusts the fitting coefficient x that minimizes [C0]. g The following is required.

[0035] TIFF0007728492000008.tif17166

[0036] Generally, <[Cm]> and <[Cv g (α,β,σ,ψ0)]> often does not match. The fitting coefficient x g As the value of [C0] increases, the eigenvalue λ of [C0] decreases monotonically and becomes negative, as shown in FIG. 8A. Figure 8A shows the relationship between <[Cm]> and <[Cv g (α,β,σ,ψ0)]> does not match, the fitting coefficient x g and the eigenvalues ​​λ1, λ2, and λ3 of [C0]. In the example of FIG. 8A, of the three eigenvalues ​​λ1, λ2, and λ3, the eigenvalue λ3 first becomes a negative value at x', and the sum of all the eigenvalues ​​at that time is λ1+λ2. On the other hand, <[Cm]> and <[Cv g (α,β,σ,ψ0)]> coincides, that is, when [C0] becomes a zero matrix, the eigenvalues ​​λ1, λ2, and λ3 of [C0] simultaneously become zero at x=1, as shown in Figure 8B. Figure 8B shows the relationship between <[Cm]> and <[Cv g (α,β,σ,ψ0)]> and the fitting coefficient x gand the eigenvalues ​​λ1, λ2, and λ3 of [C0].

[0037] From the above, we can see that <[Cv g (α,β,σ,ψ0)]> and create <[Cv g (α,β,σ,ψ0)]> and <[Cm]>, we find the closest <[Cv g When we want to find the eigenvalues ​​λ1, λ2, and λ3 of [C0], we can treat the sum of the eigenvalues ​​λ1, λ2, and λ3 of [C0] as an evaluation index. Here, to analytically examine the eigenvalues ​​λ1, λ2, and λ3 of [C0], we express Equation (13) as Equation (15) below.

[0038] TIFF0007728492000009.tif28166

[0039] The determinant representing the eigenvalues ​​of this matrix is ​​expressed as in the following equation (16).

[0040] TIFF0007728492000010.tif54166

[0041] Due to the relationship between the solution of the cubic equation and the coefficients, the sum of the three eigenvalues ​​can be calculated as shown in the following equation (17).

[0042] TIFF0007728492000011.tif32166

[0043] The covariance matrix is ​​<[Cv g The diagonal elements of <[(α,β,σ,ψ0)]> and <[Cm]> are always positive real numbers. Therefore, as shown in Figure 9, equation (17) becomes a linear function with a negative slope, and the sum of the eigenvalues ​​becomes zero at x0 = (A + B + C) / (a + b + c). FIG. 9 is an explanatory diagram showing that the sum of the eigenvalues ​​is zero when x0=(A+B+C) / (a+b+c). Therefore, <[Cm]> and <[Cv g(α,β,σ,ψ0)]> is identical, then at x=x0=1, the sum of all eigenvalues ​​of [C0] is zero and each eigenvalue is zero. <[Cm]> and <[Cv g Considering that the difference between [Cm] and [Cv (α,β,σ,ψ0)]> increases, the eigenvalues ​​move away from zero. By evaluating the index that accumulates the absolute values ​​of the eigenvalues ​​at x=x0, we can obtain the same [Cv g (α,β,σ,ψ0)]> or [Cv g (α,β,σ,ψ0)]> can be obtained. By applying the square root to the sum of the eigenvalues ​​shown in equation (17), the evaluation function G e If we define (α,β,σ,ψ0), the evaluation function G e The model parameters that minimize (α, β, σ, ψ0) are found.

[0044] TIFF0007728492000012.tif34166

[0045] This index can also be calculated using the relationship between the solution of the cubic equation and the coefficients, as shown in the following equation (19).

[0046] TIFF0007728492000013.tif22166

[0047] Using equation (17), equation (19) can be rewritten as follows: Note that the eigenvalues ​​of a Hermitian matrix are all real numbers, and therefore their simple squares are always positive real numbers.

[0048] TIFF0007728492000014.tif28166

[0049] As a result, at x=x0, the evaluation function G e (α,β,σ,ψ0) can be obtained.

[0050] TIFF0007728492000015.tif19166

[0051] The coefficient adjustment unit 3 adjusts the evaluation function G e Calculate (α,β,σ,ψ0). Evaluation function G e (α,β,σ,ψ0) is the generalized volume scattering model Cv g (g=1, ,G) and the covariance matrix Cm. g The fitting coefficient x g and the covariance matrix Cm is the generalized volume scattering model Cv g If it matches, the evaluation function G e (α,β,σ,ψ0)=0. Fitting coefficient x g and the covariance matrix Cm, and the generalized volume scattering model Cv g The larger the difference between e (α,β,σ,ψ0) becomes larger. The coefficient adjustment unit 3 adjusts the fitting coefficient x g G differences ΔC1 to ΔC after adjusting G As a result, all the evaluation functions G calculated according to Equation (21) e (α, β, σ, ψ0) is output to the model selection unit 4.

[0052] The model selection unit 4 receives G differences ΔC1 to ΔC2 from the coefficient adjustment unit 3. G As a result, all evaluation functions G e (α, β, σ, ψ0) are obtained, and G generalized volume scattering models Cv1 to Cv G Get. The model selection unit 4 selects all the evaluation functions G e (α,β,σ,ψ0) are compared with each other. All evaluation functions G e Comparing (α, β, σ, ψ0) with each other yields G differences ΔC1 to ΔC G This is equivalent to comparing the The model selection unit 4 selects G differences ΔC1 to ΔC GBased on the comparison results, G generalized volume scattering models Cv1 to Cv G One of the generalized volume scattering models is selected from the above (step ST4 in FIG. 4). Specifically, the model selection unit 4 selects G differences ΔC1 to ΔC G For example, the smallest difference ΔC min Identify. Then, the model selection unit 4 selects G generalized volume scattering models Cv1 to Cv G The smallest difference ΔC min Generalized volume scattering model Cv g Select .

[0053] In the volume scattering model determination device shown in FIG. 1, the model selection unit 4 selects G generalized volume scattering models Cv1 to Cv G Among them, the smallest difference ΔC min Generalized volume scattering model Cv g However, this is only an example, and the model selection unit 4 may select a difference ΔC other than the minimum within a range that does not cause practical problems. min Generalized volume scattering model Cv g The model selection unit 4 may select, for example, the second smallest difference ΔC min Generalized volume scattering model Cv g may be selected.

[0054] The generalized volume scattering model Cv selected by the model selection section 4 g is a set of multiple observation areas AR1 to AR N Any of the observation areas AR n This is a generalized volume scattering model for All observation areas AR1~AR N The processing of steps ST1 to ST4 in FIG. 4 is repeated until a generalized volume scattering model for is obtained.

[0055] The generalized volume scattering model Cv selected by the model selection section 4 g is the observation area AR nIt can be expressed by the PS (Polarimetric Signature) shape of the scatterer object existing in (n=1,···,N). Figure 10 shows the generalized volume scattering model Cv g FIG. 10 is an explanatory diagram showing an example of the PS shape of a feature simulated by the In FIG. 10, ψ is the orientation angle and χ is the ellipticity angle. FIG. 10 shows examples of PS shapes when the feature is an urban area, PS shapes when the feature is vegetation, and PS shapes when the feature is the sea surface. FIG. 10 also illustrates examples of PS shapes when the transmission polarization and the reception polarization are parallel polarizations (Co-Pol) and PS shapes when the transmission polarization and the reception polarization are orthogonal polarizations (X-Pol). Therefore, for example, an image processing device (not shown) uses a generalized volume scattering model Cv g By analyzing the PS shape of the feature simulated by the , the type of feature can be identified.

[0056] In the first embodiment described above, the volume scattering model determination device is configured to include: a covariance matrix acquisition unit 1 that acquires a covariance matrix corresponding to the scattering matrix of an observation target obtained by alternately irradiating the observation target with two mutually orthogonal polarized waves; and a parameter setting unit 2 that sets a plurality of values ​​of model parameters as model parameters of a generalized volume scattering model onto which the scattering matrices of a plurality of scatterers are mapped, the model parameters including parameters indicating the elements of the scattering matrix of each scatterer, the distribution of the plurality of scatterers, and the average tilt angle of the plurality of scatterers, and outputs a plurality of generalized volume scattering models including the set model parameters. The volume scattering model determination device also includes: a coefficient adjustment unit 3 that multiplies each of the plurality of generalized volume scattering models output from the parameter setting unit 2 by a fitting coefficient and adjusts each fitting coefficient so that a difference between each generalized volume scattering model and the covariance matrix after multiplication by the fitting coefficient is small; and a model selection unit 4 that compares the plurality of differences after adjustment of the fitting coefficients by the coefficient adjustment unit 3 and selects one of the plurality of generalized volume scattering models output from the parameter setting unit 2 based on the comparison result of the differences. Therefore, the volume scattering model determination device can obtain a generalized volume scattering model that simulates the characteristics of the scatterer.

[0057] Embodiment 2 In the second embodiment, a volume scattering model determination device including a parameter setting unit 5 will be described.

[0058] Fig. 11 is a configuration diagram showing a volume scattering model determination device according to embodiment 2. In Fig. 11, the same reference numerals as in Fig. 1 indicate the same or corresponding parts, and detailed description thereof will be omitted. Fig. 12 is a hardware configuration diagram showing the hardware of a volume scattering model determination device according to embodiment 2. In Fig. 12, the same reference numerals as in Fig. 2 indicate the same or corresponding parts, and detailed description thereof will be omitted. The volume scattering model determination device shown in FIG. 11 includes a covariance matrix acquisition unit 1, a parameter setting unit 5, a coefficient adjustment unit 3, and a model selection unit 4.

[0059] The parameter setting unit 5 is realized by, for example, a parameter setting circuit 15 shown in FIG. The parameter setting unit 5 sets multiple values ​​of model parameters (α, β, σ, ψ0) as model parameters of the generalized volume scattering model Cv onto which the scattering matrix [S] of multiple scatterers is mapped, including parameters α and β indicating elements of the scattering matrix of each scatterer, parameter σ indicating the distribution of multiple scatterers, and parameter ψ0 indicating the average tilt angle of the multiple scatterers. Specifically, the parameter setting unit 5 sets a plurality of values ​​of the model parameters (α, β, σ, ψ0) so that the step size of the values ​​of the anisotropic parameters (σ, ψ0) included in the model parameters (α, β, σ, ψ0) related to an isotropic scatterer is coarser than the step size of the values ​​of the anisotropic parameters (σ, ψ0) included in the model parameters (α, β, σ, ψ0) related to an anisotropic scatterer. The parameter setting unit 5 outputs a plurality of generalized volume scattering models Cv including the set model parameters (α, β, σ, ψ 0 ) to the coefficient adjustment unit 3 and the model selection unit 4, respectively.

[0060] In Fig. 11, it is assumed that the components of the volume scattering model determination device, namely, the covariance matrix acquisition unit 1, the parameter setting unit 5, the coefficient adjustment unit 3, and the model selection unit 4, are each realized by dedicated hardware such as that shown in Fig. 12. In other words, it is assumed that the volume scattering model determination device is realized by a covariance matrix acquisition circuit 11, a parameter setting circuit 15, a coefficient adjustment circuit 13, and a model selection circuit 14. Each of the covariance matrix acquisition circuit 11, the parameter setting circuit 15, the coefficient adjustment circuit 13, and the model selection circuit 14 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.

[0061] The components of the volume scattering model determination device are not limited to those realized by dedicated hardware, and the volume scattering model determination device may be realized by software, firmware, or a combination of software and firmware. When the volume scattering model determination device is realized by software, firmware, or the like, a program for causing a computer to execute the respective processing procedures of the covariance matrix acquisition unit 1, parameter setting unit 5, coefficient adjustment unit 3, and model selection unit 4 is stored in memory 21 shown in Fig. 3. Then, a processor 22 shown in Fig. 3 executes the program stored in memory 21.

[0062] 12 shows an example in which each of the components of the volume scattering model determination device is realized by dedicated hardware, while Fig. 3 shows an example in which the volume scattering model determination device is realized by software, firmware, etc. However, this is merely an example, and some of the components in the volume scattering model determination device may be realized by dedicated hardware, and the remaining components may be realized by software, firmware, etc.

[0063] Next, the operation of the volume scattering model determination device shown in Fig. 11 will be described. However, apart from the parameter setting unit 5, the volume scattering model determination device is the same as that shown in Fig. 1. Therefore, only the operation of the parameter setting unit 5 will be described here.

[0064] In the example of Figure 5, the scattering matrix [S] of the left-handed scatterer, the scattering matrix [S] of the right-handed scatterer, and the scattering matrix [S] of the scatterer such as a three-sided CR are isotropic scatterers, and the scattering matrix [S] of the scatterer with a two-sided CR and the scattering matrix [S] of the scatterer such as a dipole are anisotropic scatterers. Isotropic scatterers are less sensitive to rotation than anisotropic scatterers, so even if the step size of the anisotropic model parameters (σ, ψ0) among the model parameters (α, β, σ, ψ0) for isotropic scatterers is set roughly, there is little problem in obtaining appropriate model parameters.

[0065] Therefore, the parameter setting unit 5 sets a plurality of values ​​of the model parameters (α, β, σ, ψ0) so that the step size of the values ​​of the anisotropic parameters (σ, ψ0) included in the model parameters (α, β, σ, ψ0) related to an isotropic scatterer is coarser than the step size of the values ​​of the anisotropic parameters (σ, ψ0) included in the model parameters (α, β, σ, ψ0) related to an anisotropic scatterer. The parameter setting unit 5 sets G generalized volume scattering models Cv1 to Cv2, each of which includes model parameters (α, β, σ, ψ) with different values. G are output to the coefficient adjustment unit 3 and the model selection unit 4, respectively.

[0066] In the above-described second embodiment, the volume scattering model determination device shown in Fig. 11 is configured so that the parameter setting unit 5 sets a plurality of model parameter values ​​so that the step size of the anisotropic parameter values ​​included in the model parameters related to anisotropic scatterers is coarser than the step size of the anisotropic parameter values ​​included in the model parameters related to anisotropic scatterers. Therefore, the volume scattering model determination device shown in Fig. 11 can obtain a generalized volume scattering model that simulates the characteristics of the scatterers, just like the volume scattering model determination device shown in Fig. 1, and can reduce the processing load on the coefficient adjustment unit 3 more than the volume scattering model determination device shown in Fig. 1.

[0067] 1 and 11, the covariance matrix acquisition unit 1 acquires a covariance matrix Cm corresponding to the scattering matrix [S] of the object to be observed, and outputs the covariance matrix Cm to the coefficient adjustment unit 3. However, this is merely an example, and the covariance matrix acquisition unit 1 may, for example, convert the covariance matrix Cm into a coherency matrix Tm using a transformation matrix L as shown in the following equation (22), and output the coherency matrix Tm as the covariance matrix Cm to the coefficient adjustment unit 3. In other words, the matrix output from the covariance matrix acquisition unit 1 to the coefficient adjustment unit 3 is not limited to the covariance matrix Cm, and may be any matrix that can be converted from the covariance matrix Cm using a transformation matrix.

[0068] TIFF0007728492000016.tif36166 In equation (22), T is a mathematical symbol indicating transposition.

[0069] In addition, the present disclosure allows for free combination of the respective embodiments, modification of any of the components of the respective embodiments, or omission of any of the components of the respective embodiments. [Industrial Applicability]

[0070] The present disclosure is suitable for a volume scattering model determination device and a volume scattering model determination method. [Explanation of symbols]

[0071] 1 Covariance matrix acquisition unit, 2 Parameter setting unit, 3 Coefficient adjustment unit, 4 Model selection unit, 5 Parameter setting unit, 11 Covariance matrix acquisition circuit, 12 Parameter setting circuit, 13 Coefficient adjustment circuit, 14 Model selection circuit, 15 Parameter setting circuit, 21 Memory, 22 Processor.

Claims

1. a covariance matrix acquisition unit that acquires a covariance matrix corresponding to a scattering matrix of an observation target obtained by alternately irradiating the observation target with two polarized waves that are orthogonal to each other; a parameter setting unit that sets a plurality of values ​​of model parameters as model parameters of a generalized volume scattering model onto which scattering matrices of a plurality of scatterers are mapped, the model parameters including parameters indicating elements of the scattering matrices of each scatterer, the distribution of the plurality of scatterers, and the average tilt angle of the plurality of scatterers, and outputs a plurality of generalized volume scattering models including the set model parameters; a coefficient adjustment unit that multiplies each of the plurality of generalized volume scattering models output from the parameter setting unit by a fitting coefficient and adjusts each fitting coefficient so that a difference between each generalized volume scattering model after multiplication by the fitting coefficient and the covariance matrix becomes small; a model selection unit that compares the plurality of differences after the fitting coefficients have been adjusted by the coefficient adjustment unit with each other, and selects one generalized volume scattering model from the plurality of generalized volume scattering models output from the parameter setting unit based on the comparison result of the differences; A volume scattering model determination device comprising:

2. The parameter setting unit 2. The volume scattering model determination device according to claim 1, wherein a plurality of values ​​of the model parameters are set within a range of possible values ​​of the model parameters included in the generalized volume scattering model to which the scattering matrices of the plurality of scatterers are mapped.

3. a scattering matrix of an isotropic scatterer and a scattering matrix of an anisotropic scatterer are mapped in the generalized volume scattering model in which the model parameter values ​​are set by the parameter setting unit; the generalized volume scattering model includes, as model parameters, parameters indicating elements of the scattering matrix and anisotropy parameters; The parameter setting unit 2. The volume scattering model determination device according to claim 1, wherein a plurality of model parameter values ​​are set so that the step width of the anisotropic parameter values ​​included in the model parameters relating to the isotropic scatterer is coarser than the step width of the anisotropic parameter values ​​included in the model parameters relating to the anisotropic scatterer.

4. a covariance matrix acquisition unit that acquires a covariance matrix corresponding to a scattering matrix of an observation target obtained by alternately irradiating the observation target with two polarized waves that are orthogonal to each other; a parameter setting unit sets a plurality of values ​​of model parameters as model parameters of a generalized volume scattering model onto which scattering matrices of a plurality of scatterers are mapped, the model parameters including parameters indicating elements of the scattering matrix of each scatterer, a distribution of the plurality of scatterers, and an average tilt angle of the plurality of scatterers, and outputs a plurality of generalized volume scattering models including the set model parameters; a coefficient adjustment unit multiplying each of the plurality of generalized volume scattering models output from the parameter setting unit by a fitting coefficient, and adjusting each fitting coefficient so that a difference between each generalized volume scattering model after multiplication by the fitting coefficient and the covariance matrix becomes small; a model selection unit that compares the plurality of differences after the fitting coefficients have been adjusted by the coefficient adjustment unit with each other, and selects one generalized volume scattering model from the plurality of generalized volume scattering models output from the parameter setting unit based on the comparison result of the differences; Volume scattering model determination method.

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