Complex lunar surface scattering coefficient image simulation method and system based on multiple data association

Abstract

The application discloses a complex lunar surface scattering coefficient image simulation method and system based on multiple data jointing, and the method comprises the following steps: firstly, terrain camera data is used to acquire terrain height data of a special geological structure of the moon as model geometric information input; secondly, a lunar soil weathering layer electromagnetic scattering model is established by using a vector radiation transfer theory and a lunar surface stone double-component method to simulate the interaction between electromagnetic waves and lunar rocks, the lunar surface, weathering layer particles and the subsurface; finally, a radar imaging mechanism is simulated based on a mapping projection algorithm, the scattering coefficient on each scattering facet is calculated by using the electromagnetic scattering model, and the scattering coefficient is mapped to an imaging area by using a ray tracing method, a shadow judgment method and the like, so that the scattering echo of each region of the complex lunar surface is rapidly estimated, and the fast scattering coefficient image simulation is realized.

Description

Technical Field

[0001] The present invention belongs to the field of electromagnetic scattering calculation in complex scenes, and specifically relates to a method and system for simulating complex lunar surface scattering coefficient images based on the combination of multiple data. Background Art

[0002] Different geological structures record important information about the moon's internal activities and its external space environment. Studying the interaction mechanism between electromagnetic waves and the moon's complex geological structures and establishing an electromagnetic scattering model of complex geological structures on the lunar surface can provide prior knowledge and data discrimination for lunar geological exploration of landing sites before actual lunar exploration activities, and can further understand the rich information about crater impacts and underground materials.

[0003] The actual lunar topography is complex and diverse, and radar scattering echoes exhibit complex interdependencies and uncertainties with lunar surface parameters, rock scattering, and radar parameters, making predictions difficult using single topographic data. Early studies of lunar surface scattering focused solely on one-dimensional analyses of the relationship between radar scattering echoes and incident angle and regolith parameters, without considering the specific scattering of surface rocks. Subsequently, vector radiative transfer theory was often used to analyze the relationship between lunar regolith parameters and radar echoes, but it did not systematically consider the varying rock fractions under different lunar surface geological structures. Bidirectional ray tracing methods were also commonly used to simulate high-order scattering from lunar rock surfaces. However, this approach ignores the dominant role of regolith scattering in degenerate craters or other areas of lunar surface rock fragmentation. Therefore, considering the influence of actual lunar surface topography on the local incident angle and the varying rock fractions across different lunar regions, we used rock abundance data as input to determine the location and fraction of lunar rock distribution and develop an electromagnetic scattering model for complex lunar surfaces containing rocks. The lunar surface is also highly variable. In addition to features like craters and rilles, it also contains complex structures like lava skylights. To account for the shadowing effects of terrain, a mapping and projection algorithm is used to simulate the scattering coefficient. The scattering contributions of scattering elements are geometrically summed on the slanted range plane (the mapping plane), while their extinction and shadowing effects are cumulatively added on the ground range plane (the projection plane).

[0004] The Terrain Camera Dataset (DTM) is an important data foundation for analyzing lunar surface topography. Compared to traditional DEM elevation data, it is more suitable for analyzing the unique geological structures of the Moon, enabling a more detailed interpretation of lunar topography and geological structure characteristics. It is currently the most important tool for studying lunar topography and landforms. The Rock Abundance Dataset (RA) provides important data information on the large-scale distribution and proportion of rocks on the Moon. Summary of the Invention

[0005] To address these issues, the present invention proposes a method and system for simulating complex lunar surface scattering coefficient images based on a combination of multiple data. This method utilizes terrain camera data (DTM) to obtain terrain height data for a specific lunar geological structure as model geometry input. Secondly, a lunar regolith electromagnetic scattering model is established using vector radiation transfer theory and a two-component method for lunar surface rocks to simulate the interaction between electromagnetic waves and lunar surface rocks, the lunar surface, regolith particles, and the subsurface. Finally, based on the mapping projection algorithm (MPA) to simulate the SAR imaging mechanism, the electromagnetic scattering model is used to calculate the scattering coefficient for each scattering element. The scattering coefficient is then mapped to the imaging area through methods such as ray tracing and shadow judgment, achieving rapid estimation of scattered echoes from various regions of the complex lunar surface and rapid scattering coefficient image simulation.

[0006] The technical solution for achieving the purpose of the present invention is as follows: In a first aspect, the present invention provides a method for simulating a complex lunar surface scattering coefficient image based on a combination of multiple data, comprising the following steps:

[0007] Step 1: Input the DTM dataset as the actual terrain geometry model information and the rock abundance dataset as the actual lunar surface rock distribution location and proportion information. Preprocess the two datasets to obtain the terrain and rock distribution information in matrix form. Align the DTM terrain grid data with the rock abundance grid data.

[0008] Step 2: Simulate the electromagnetic scattering caused by electromagnetic waves entering a certain grid. If the rock abundance data size of a certain grid is 0, then surface scattering, subsurface scattering, and buried particle scattering will occur in this area. If the rock abundance data size of a certain grid is not 0, then direct scattering from lunar surface rocks and rock-rock secondary scattering will also occur in this area.

[0009] Step 3: Substitute the electromagnetic scattering coefficient calculated for each grid into the mapping projection algorithm, and map the scattering contribution of each surface element to the corresponding mapping area number to simulate the scattering coefficient image, that is, simulate the radar scattering mechanism in the actual environment to reflect the SAR image characteristics.

[0010] In a second aspect, the present invention provides a complex lunar surface scattering coefficient image simulation system based on a combination of multiple data, comprising:

[0011] The first module is used to input the DTM dataset as the actual terrain geometry model information and the rock abundance dataset as the actual lunar surface rock distribution location and proportion information. The two datasets are preprocessed to obtain the terrain and rock distribution information in matrix form. The DTM terrain grid data and the rock abundance grid data are then aligned.

[0012] The second module is used to simulate the electromagnetic scattering caused by electromagnetic waves entering a certain grid. If the rock abundance data size of a certain grid is 0, then surface scattering, subsurface scattering, and buried particle scattering will occur in this area. If the rock abundance data size of a certain grid is not 0, then direct scattering from lunar surface rocks and rock-rock secondary scattering will also occur in this area.

[0013] The third module is used to substitute the electromagnetic scattering coefficient calculated for each grid into the mapping projection algorithm, and map the scattering contribution of each facet to the corresponding mapping area number to simulate the scattering coefficient image, that is, to simulate the radar scattering mechanism in the actual environment to reflect the SAR image characteristics.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the program.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect.

[0016] Compared with the existing technology, the present invention has the following beneficial effects: the present invention proposes a complex lunar surface scattering coefficient image simulation method based on the combination of multiple data. The method first uses terrain camera data (DTM) to obtain terrain height data of a special geological structure on the moon as model geometric information input; secondly, the vector radiation transfer theory and the lunar surface rock dual-component method are used to establish an electromagnetic scattering model of the lunar soil regolith to simulate the interaction between electromagnetic waves and lunar surface rocks, lunar surface, regolith particles and subsurface; finally, based on the mapping projection algorithm (MPA) to simulate the radar imaging mechanism, the electromagnetic scattering model is used to calculate the scattering coefficient on each scattering surface element, and the scattering coefficient is mapped to the imaging area through ray tracing, shadow judgment and other methods, so as to realize the rapid estimation of scattered echoes in various areas of the complex lunar surface and the rapid scattering coefficient image simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a digital topographic map and rock abundance map of the crater in the example of the present invention, as well as a local incidence angle and slope map.

[0018] Figure 2 It is a schematic diagram of the mapping projection algorithm in the example of the present invention.

[0019] Figure 3 It is the measured scattering coefficient image and the measured scattering coefficient image in the example of the present invention.

[0020] Figure 4 It is a comparison diagram of the measured and simulated histograms in the example of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] A method for simulating scattering coefficient images of lunar terrain based on a terrain camera dataset, a rock abundance dataset, and a mapping projection algorithm is as follows:

[0023] Step 1: Take the terrain camera data and rock abundance data as input, and convert the data storage format into a matrix format through Matlab reading and writing to obtain the elevation and lunar surface rock distribution, proportion and other information. Figure 1 As shown, its longitude range is: 62.15°W~61.73°W, and its latitude range is: 13.7°N~14.1°N. Use longitude and latitude information to align Figure 1 (a) with Figure 1 (b) Grid size. The original rock abundance data resolution is 236.8 m / pixel, which is reduced to 14.8 m / pixel using cubic spline interpolation. The grid size in both figures is 501*501.

[0024] Step 2: Based on the measured SAR data labels of the selected area, set the radar incident angle and azimuth angle, and combine the terrain camera data information to obtain the local incident angle and slope information. Figure 1 As shown, (a) is the local incident angle image, and (b) is the slope information; Figure 1 (a) The red arrow indicates the radar incident direction.

[0025] Step 3: The scattered echo value on a certain grid is the superposition of multiple scattering mechanisms. The electromagnetic scattering algorithm calculated for each grid depends on the rock abundance data information, such as Figure 1 As shown in (b), if the current rock abundance grid value is 0, then the surface scattering M0 and subsurface scattering M b , buried particle scattering M p If the current rock abundance grid value is not 0, then calculate the surface scattering M0, subsurface scattering M b , buried particle scattering M p and the direct scattering of M by lunar rocks rock , rock-rock secondary M rock-rock There are five scattering mechanisms. The five scattering mechanisms are:

[0026] M0=[R sur ](1)

[0027] M b =[T sur ]·[R sub]·[T sur ](2)

[0028] M P =[T sur ]·[P]·[T sur ](3)

[0029] M rock =[R rock1 ](4)

[0030] M rock-rock =[R rock1 ]·[R rock2 ](5) Among them, R sur Represents the lunar surface roughness reflectivity matrix, R sub Represents the subsurface roughness reflectivity matrix, R rock Represents the stone surface reflectivity matrix. The suffixes 1 and 2 represent different stones. The corresponding reflectivity matrix is ​​calculated using the integral equation method in the following sections. sur represents the lunar surface transmittance matrix, where the incident angle and transmission angle satisfy Snell's theorem; P represents the particle scattering matrix, which is approximated as ellipsoidal particles using the Generalized Rayleigh-Gans (GRG) method and is mainly related to the electromagnetic wave incident angle, particle size, and orientation angle.

[0031] Assuming that the number of meshes in the geometric model is m×n, the relationship between the scattering coefficient within a unit mesh and the five-term Muller scattering matrix is:

[0032] M m,n =(1-RA m,n )·(M0+M b +M p )+RA m,n ·(M rock +M rock-rock )(6) Where M m,n Characterizes the scattering coefficient of the nth grid in the mth section. RA m,n is the rock abundance data, representing the proportion of rock in the nth grid in the mth section.

[0033] Step 4: The scattering contributions of the scattering targets are cumulatively summed on the slant range plane (mapping plane) according to geometric principles, while their extinction and shadowing effects are cumulatively summed on the ground range plane (projection plane). The scattered echo coefficients of each grid obtained in the previous step are used to simulate the scattering coefficient imaging through the MPA algorithm.

[0034] The radar is actually far away from the lunar surface structure, so the lunar surface scattering contribution received by the radar at different locations is considered to be the same. Therefore, by independently calculating the scattering coefficient for each row (in the y direction of the crater) of the lunar surface structure, a scattering coefficient map for the entire lunar surface scene can be obtained. Figure 2 As shown in the figure, the spaceborne radar flies along the y direction, and the radar field of view shows a crater geological structure. The resolution of the crater geometric model in the x and y directions is R x and R y The mth row in the y direction is selected as the incident plane slice to establish the projection surface PA and the mapping surface MA, and all target scattered energy in the arc PA is mapped to MA (because the radar is actually far away from the lunar surface, it is approximately considered that the arc PA is perpendicular to the mapping surface MA). The resolution of the projection surface is the same as the resolution of the lunar surface geometric model. The mapping surface resolution along the radar line of sight and the y direction are R r ×R y During the imaging simulation, the scattering contribution S on each projection unit n is calculated in sequence along the x direction under the mth section. m,n , and S m,n According to the distance from the projection unit to the radar, it is equidistantly mapped to the mapping surface MA. At this time, the mapping unit E on MA i,j The index number q is:

[0035] q=round[l (E→M) / R r ](7)

[0036] The geometric model resolution is a known parameter, namely R x Known, then R r =R x ·sinθ. The number of grids on the mapping surface is N MA , and the solution is

[0037] N MA =round[l (A→M) / R r ](8) Where round represents the mathematical algorithm of rounding, l (E→M) Indicates the distance from the intersection of the mapping ray MP and the mapping surface MA to point M at this moment. (A→M) Indicates the distance between point A (end point) and point M (starting point) on the mapping surface.

[0038] The relationship between the size of the complex lunar surface modeling scene and imaging is shown in Table 1.

[0039] Table 1

[0040]

[0041] One geometric model unit corresponds to only one mapping unit, but the scattering coefficient in one mapping unit may be the cumulative scattering contribution of multiple geometric model units. Figure 2 As shown, the scattering contributions of the nIth geometric model unit and the nI+1th geometric model unit along the x direction under the mIth section are mapped to the mapping unit E with index number q. p,q Therefore, the scattering coefficient of the mapping surface unit is the accumulation of the scattering coefficients of one or more geometric model units. When the scattering coefficients of the projection units are calculated in sequence along the x direction, the array E is updated according to whether it is mapped to the same mapping unit according to the following formula: p,q :

[0042] E p,q :=E p,q +S m,n (9)

[0043] Where: = means reassignment. m,n Represents the scattering coefficient of the nth grid in the mth section. At this time, the distance from the nI grid and the nI+l grid to the radar is the same and both can be mapped to (p, q). Figure 2 Here n can be nI and nI+1.

[0044] It should be noted that when calculating the projection surface in a loop, the projection plane scattering array S needs to be initialized at the beginning of each row so that each element is a unit matrix.

[0045] Step 5: Post-process the obtained mapping plane array E to obtain the scattered echo intensity of each area of ​​the complex terrain and establish a fast simulation model of electromagnetic scattering in actual complex terrain.

[0046] In this example, the crater Galilaei-E was selected, with the central range at -61.95°W and 13.9°N. Based on the measured SAR data tags in the selected area, the radar input frequency was set to 2.38 GHz, the incident angle was set to 52.97°, and the azimuth angle was set to 90.43°. The dielectric constants of the lunar soil and buried particles are ε re =2.7+j0.002 and ε p =6.0+j0.01. The exponential power spectrum is selected as the spectrum function of the rough surface. The rough surface parameters of the lunar soil surface and the rock surface are shown in Table 2.

[0047] Table 2

[0048]

[0049] from Figure 1(b) The rock abundance results of the Galilaei-E area can obtain information on rock distribution and rock proportion. From the image, it can be observed that there are more rocks on the inner wall of the crater, especially in some areas on the right side of the crater, where the rock proportion reaches 22%, while the flat lunar surface area outside the crater is only about 2%. The simulation results of this section are shown in (b). The co-polarization backscatter coefficient of the crater wall on the right side of the crater is as high as 5dB. This is because the right side of the crater wall is facing the direction of the radar line of sight, and the rock abundance in this area is relatively high. The secondary scattering of rock to rock and rock to lunar surface in the surface and weathering layer accounts for the main contribution. The co-polarization backscatter coefficient of the crater bottom and background environment is as low as about -17dB. At this time, the local incident angle of the radar in this area increases, which is mainly dominated by the contribution of lunar surface scattering and weathering layer particle scattering, and the secondary scattering contribution decreases. Observation Figure 3 (a) (b) It is found that the measured co-polarization scattering coefficient images of the crater and the simulated images in this section both show that the co-polarization backscattering echo is higher in the right crater wall area due to the scattering contribution of rocks, while the co-polarization backscattering coefficient is lower due to the scattering contribution of a small amount of rocks in the crater bottom and background. Figure 4 This is a comparison of the histograms of the actual measurement and simulation of the present invention, and the comparison result is good.

[0050] Based on the same inventive concept, the present invention also provides a complex lunar surface scattering coefficient image simulation system based on the combination of multiple data, including:

[0051] The first module is used to input the DTM dataset as the actual terrain geometry model information and the rock abundance dataset as the actual lunar surface rock distribution location and proportion information. The two datasets are preprocessed to obtain the terrain and rock distribution information in matrix form. The DTM terrain grid data and the rock abundance grid data are then aligned.

[0052] The second module is used to simulate the electromagnetic scattering caused by electromagnetic waves entering a certain grid. If the rock abundance data size of a certain grid is 0, then surface scattering, subsurface scattering, and buried particle scattering will occur in this area. If the rock abundance data size of a certain grid is not 0, then direct scattering from lunar surface rocks and rock-rock secondary scattering will also occur in this area.

[0053] The third module is used to substitute the electromagnetic scattering coefficient calculated for each grid into the mapping projection algorithm, and map the scattering contribution of each facet to the corresponding mapping area number to simulate the scattering coefficient image, that is, to simulate the radar scattering mechanism in the actual environment to reflect the SAR image characteristics.

[0054] The specific implementation methods of the above modules are the same as the aforementioned complex lunar surface scattering coefficient image simulation method based on the combination of multiple data, and will not be repeated here.

[0055] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A complex lunar surface scattering coefficient image simulation method based on multiple data combinations, characterized by: The following steps are involved: Step 1: Input the DTM dataset as the actual terrain geometry model information, and the rock abundance dataset as the actual lunar surface rock distribution location and proportion information. Preprocess the above two data to obtain the terrain and rock distribution information in matrix form. Align the DTM terrain grid data and rock abundance grid data; Step 2: Simulate the electromagnetic scattering caused by electromagnetic waves entering a certain grid. If the rock abundance data size of a certain grid is 0, it means that surface scattering, subsurface scattering and buried particle scattering occur in the corresponding area. If the rock abundance data size of a certain grid is not 0, direct scattering of lunar surface rocks and rock-rock secondary scattering will occur in the corresponding area; The scattered echo value on the grid is the superposition of multiple scattering mechanisms; if the current rock abundance grid value is 0, then the surface scattering M0, subsurface scattering M b , buried particle scattering M p These three scattering mechanisms; if the current rock abundance grid value is not 0, then calculate the surface scattering M0, subsurface scattering M b , buried particle scattering M p and the direct scattering of M by lunar rocks rock , rock-rock secondary M rock-rock Scattering There are five scattering mechanisms; the five scattering mechanisms are: M0=[R sur ] (1) M b =[T sur ]·[R sub ]·[T sur ] (2) M P =[T sur ]·[P]·[T sur ] (3) M rock =[R rock1 ] (4) M rock-rock =[R rock1 ]·[R rock2 ] (5) where R sur Represents the lunar surface roughness reflectivity matrix, R sub Represents the subsurface roughness reflectivity matrix, R rock Represents the stone surface reflectivity matrix, and the suffixes 1 and 2 represent different stones; T sur represents the lunar surface transmittance matrix; P represents the particle scattering matrix; Assuming that the number of meshes in the geometric model is m×n, the relationship between the scattering coefficient within a unit mesh and the five-term Muller scattering matrix is: M m,n =(1-RA m,n )·[M0+M b +M p ]+RA m,n ·[M rock +M rock-rock ] (6) Among them, M m,n Characterizes the scattering coefficient of the nth grid in the mth section; RA m,n is the rock abundance data, representing the proportion of rocks in the nth grid in the mth section; Step 3: Substitute the electromagnetic scattering coefficient calculated for each grid into the mapping projection algorithm, and map the scattering contribution of each surface element to the corresponding mapping area number to simulate the scattering coefficient image, that is, simulate the radar scattering mechanism in the actual environment to reflect the SAR image characteristics.

2. The method for simulating complex lunar surface scattering coefficient images based on multiple data combinations according to claim 1, characterized in that: The DTM and rock abundance data were preprocessed, that is, the terrain data storage format was converted into a matrix grid format; the two data were regionally matched and corrected using ENVI; and the rock abundance data were interpolated using cubic spline to have the same resolution as the DTM.

3. The method for simulating complex lunar surface scattering coefficient images based on multiple data combinations according to claim 1, characterized in that: The specific process of the mapping projection algorithm is as follows: The scattering coefficient of each row of lunar surface structure is calculated independently to obtain the scattering coefficient map of the entire lunar surface scene; the spaceborne radar flies along the azimuth direction, and the radar field of view is a crater geological structure; the resolution of the crater geometric model in the range and azimuth directions is R x and R y ; Select a certain azimuth as the incident plane slice to establish the projection surface PA and the mapping surface MA, and map all the target scattered energy in the arc PA to MA; the resolution of the projection surface is the same as the resolution of the lunar surface geometric model; the resolution of the mapping surface in the range direction and azimuth direction is R r ×R y During the imaging simulation, the scattering contribution S on each projection unit n is calculated in sequence along the distance direction at the mth section. m,n , and S m,n According to the distance from the projection unit to the radar, it is equidistantly mapped to the mapping surface MA. At this time, the mapping unit E on MA p,q The index number q is: q=round[l (E→M) / R r ] (7) The geometric model resolution is a known parameter, namely R x Known, then R r =R x sinθ; the number of grids on the mapping surface is N MA , and the solution is N MA =round[l (A→M) / R r ] (8) Where round means rounding the data, θ means the radar incident angle, l (E→M) Indicates the distance from the intersection of the mapping ray MP and the mapping surface MA to point M at this moment; l (A→M) Indicates the distance between the end point A and the starting point M on the mapping surface; A geometric model unit corresponds to only one mapping unit, but the scattering coefficient in a mapping unit may be the accumulation of scattering contributions from multiple geometric model units; the scattering contributions from the mth section down the distance to the nIth geometric model unit and the nI+lth geometric model unit are both mapped to the mapping unit E with index number q. p,q Therefore, the scattering coefficient of the mapping surface element is the accumulation of the scattering coefficients of one or more geometric model elements, written as: E p,q :=E p,q +S m,n (9) Among them: = means reassignment; S m,n Represents the scattering coefficient of the nth grid in the mth section, and the projection surface index n mapped to (p, q) is taken as nI and nI+l; During calculation, each grid in the azimuth direction is calculated in sequence. At the beginning of each row, the S array needs to be initialized so that each element is a unit matrix.

4. A complex lunar surface scattering coefficient image simulation system based on multiple data combinations, characterized by: include: The first module is used to input the DTM dataset as the actual terrain geometry model information and the rock abundance dataset as the actual lunar surface rock distribution location and proportion information, pre-process the above two data sets, and obtain the terrain and rock distribution information in matrix form; Align the DTM terrain grid data and rock abundance grid data; The second module is used to simulate the electromagnetic scattering caused by electromagnetic waves entering a certain grid. If the rock abundance data size of a certain grid is 0, then surface scattering, subsurface scattering and buried particle scattering will occur in the corresponding area. If the rock abundance data size of a certain grid is not 0, then the corresponding area will also have direct scattering from lunar surface rocks and rock-rock secondary scattering; The scattered echo value on a certain grid is the superposition of multiple scattering mechanisms; If the current rock abundance grid value is 0, then calculate the surface scattering M0, subsurface scattering M b , buried particle scattering M p These three scattering mechanisms; if the current rock abundance grid value is not 0, then calculate the surface scattering M0, subsurface scattering M b , buried particle scattering M p and the direct scattering of M by lunar rocks rock , rock-rock secondary M rock-rock Scattering There are five scattering mechanisms; the five scattering mechanisms are: M0=[R sur ] (1) M b =[T sur ]·[R sub ]·[T sur ] (2) M P =[T sur ]·[P]·[T sur ] (3) M rock =[R rock1 ] (4) M rock-rock =[R rock1 ]·[R rock2 ] (5) where R sur Represents the lunar surface roughness reflectivity matrix, R sub Represents the subsurface roughness reflectivity matrix, R rock Represents the stone surface reflectivity matrix. The suffixes 1 and 2 represent different stones. The corresponding reflectivity matrix is ​​calculated using the integral equation method in the following sections. sur represents the lunar surface transmittance matrix, where the incident angle and transmission angle satisfy Snell's theorem; P represents the particle scattering matrix; Assuming that the number of meshes in the geometric model is m×n, the relationship between the scattering coefficient within a unit mesh and the five-term Muller scattering matrix is: M m,n =(1-RA m,n )·[M0+M b +M p ]+RA m,n ·[M rock +M rock-rock ] (6) Among them, M m,n Characterizes the scattering coefficient of the nth grid in the mth section; RA m,n is the rock abundance data, representing the proportion of rocks in the nth grid in the mth section; The third module is used to substitute the electromagnetic scattering coefficient calculated for each grid into the mapping projection algorithm, and map the scattering contribution of each facet to the corresponding mapping area number to simulate the scattering coefficient image, that is, to simulate the radar scattering mechanism in the actual environment to reflect the SAR image characteristics.

5. The complex lunar surface scattering coefficient image simulation system based on multiple data combinations according to claim 4 is characterized in that: The DTM and rock abundance data were preprocessed, that is, the terrain data storage format was converted into a matrix grid format; the two data were regionally matched and corrected using ENVI; and the rock abundance data were interpolated using cubic spline to have the same resolution as the DTM.

6. The complex lunar surface scattering coefficient image simulation system based on multiple data combinations according to claim 4 is characterized in that: The principle of the mapping projection algorithm is: The scattering coefficient of each row of lunar surface structure is calculated independently to obtain the scattering coefficient map of the entire lunar surface scene; the spaceborne radar flies along the azimuth direction, and the radar field of view is a crater geological structure; the resolution of the crater geometric model in the range and azimuth directions is R x and R y ; Select a certain azimuth as the incident plane slice to establish the projection surface PA and the mapping surface MA, and map all the target scattered energy in the arc PA to MA; the resolution of the projection surface is the same as the resolution of the lunar surface geometric model; the resolution of the mapping surface in the range direction and azimuth direction is R r ×R y During the imaging simulation, the scattering contribution S on each projection unit n is calculated in sequence along the distance direction at the mth section. m,n , and S m,n According to the distance from the projection unit to the radar, it is equidistantly mapped to the mapping surface MA. At this time, the mapping unit E on MA p,q The index number q is: q=round[l (E→M) / R r ] (7) The geometric model resolution is a known parameter, namely R x Known, then R r =R x sinθ; the number of grids on the mapping surface is N MA , and the solution is N MA =round[l (A→M) / R r ] (8) Where round means rounding the data, θ means the radar incident angle, l (E→M) Indicates the distance from the intersection of the mapping ray MP and the mapping surface MA to point M at this moment; l (A→M) Indicates the distance between the end point A and the starting point M on the mapping surface; A geometric model unit corresponds to only one mapping unit, but the scattering coefficient in a mapping unit may be the accumulation of scattering contributions from multiple geometric model units; the scattering contributions from the mth section down the distance to the nIth geometric model unit and the nI+lth geometric model unit are both mapped to the mapping unit E with index number q. p,q Therefore, the scattering coefficient of the mapping surface element is the accumulation of the scattering coefficients of one or more geometric model elements, written as: E p,q :=E p,q +S m,n (9) Among them: = means reassignment; S m,n Represents the scattering coefficient of the nth grid in the mth section, and the projection surface index n mapped to (p, q) is taken as nI and nI+l; During calculation, each grid in the azimuth direction is calculated in sequence. At the beginning of each row, the S array needs to be initialized so that each element is a unit matrix.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 3 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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