Rock abundance inversion method and system based on optical image and synthetic aperture radar data, storage medium and electronic equipment

Abstract

The invention provides a rock abundance inversion method and system based on an optical image and synthetic aperture radar data, a storage medium and electronic equipment. The method comprises the following steps: carrying out preprocessing operation on a polarimetric SAR image of Mini-RF; and extracting regression features among the parameters, constructing an RA-SAR inversion model by adopting a linear regression method, and endowing a higher weight for significant features in the Stokes parameters so as to enhance the fitting effect of the model. According to the invention, Mini-RF SAR data and optical remote sensing data are combined, so that the problems of inapplicability and insufficient coverage of optical data in a part of research areas due to an overlarge incident angle of the sun are solved, and higher-resolution rock abundance distribution of large-scale lunar meteorite craters can be obtained in a high-efficiency manner; the defect that the resolution ratio of the lunar surface rock abundance obtained by DiViner equipment is too low is overcome, and a brand new technical path is provided for lunar surface geological evolution research.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a rock abundance inversion method, system, computer-readable storage medium and electronic device based on optical images and synthetic aperture radar data. Background Art

[0002] Synthetic Aperture Radar (SAR) is an active remote sensing technology that is widely used in the detection of the surface and underground features of the earth and planets. The advantage of SAR is that it is not limited by external factors such as day and night, weather, etc., and can obtain high-resolution remote sensing data under various environmental conditions. It is particularly suitable for detecting the surface and subsurface characteristics of celestial bodies such as the moon. By analyzing radar echoes, SAR can provide information about topography, geological structure, rock abundance, etc., especially at high resolution, it can effectively reveal the complex geological features of the lunar surface. At present, SARs that have been used in lunar exploration projects include Mini-SAR on Chandrayaan-1, Mini-RF on LRO, and DFSAR on Chandrayaan-2.

[0003] As an important symbol of lunar geological evolution, craters on the lunar surface not only help to study the lunar impact history, but also provide key information for understanding the composition of lunar regolith and underground materials. The number and distribution of rocks on the sputtering layer of craters can provide important clues about the impact process, rock abundance and other geological characteristics. Studying the rock abundance (RA) of the sputtering layer of these craters is a key task in the study of lunar surface geological evolution. At present, the rock abundance data of the lunar surface is derived based on the LRO Diviner device, and its resolution of 240m / piexl is too low to quantitatively analyze smaller craters. In the prior art, optical remote sensing images have played an important role in the characterization of lunar surface morphology, especially in the identification of rocks in craters and their sputtering layers. However, although the application of optical images in rock abundance inversion is feasible, it requires the identification and annotation of rocks, which is labor-intensive and cumbersome, especially in areas with densely distributed craters, where the processing and analysis of optical images becomes more complicated. Another major limitation of optical data is that it is affected by factors such as the angle of solar incidence and changes in illumination, which makes it impossible to provide comprehensive information on the rock abundance of craters. Finally, some craters on the lunar surface lack high-resolution optical data. In contrast, synthetic aperture radar (SAR) images are not affected by the above factors and have significant advantages in detecting the lunar surface regolith. In particular, high-resolution SAR data such as Mini-RF can provide detailed information on the lunar surface and subsurface, overcoming some limitations of optical images. However, SAR images themselves also have problems such as noise and the inability to directly and quantitatively derive rock abundance data around craters.

[0004] In summary, the existing lunar surface rock abundance resolution is too low, and although the optically derived rock abundance has a high resolution, it has many defects. Therefore, how to effectively combine SAR and optical data to make up for their respective shortcomings to provide higher resolution and more accurate rock abundance inversion has become a technical challenge that needs to be solved urgently. Summary of the invention

[0005] The purpose of this application is to provide a rock abundance inversion method, system, storage medium and electronic device based on optical images and synthetic aperture radar data to solve the problems existing in the above-mentioned prior art.

[0006] In order to achieve the above objectives, this application provides the following technical solutions:

[0007] The present application provides a rock abundance inversion method based on optical images and synthetic aperture radar data, the method comprising: step S101, obtaining a Mini-RF SAR image, a NAC optical image and a Diviner RA image of a crater, and performing a preprocessing operation on the image data obtained above; step S102, extracting rock information from the optical image of the crater, and calculating a rock abundance distribution map of the crater based on the optically statistical rock information and the Diviner RA image; step S103, performing a preprocessing operation on the Mini-RF The SAR image is decomposed into Stokes parameters to obtain S1, S2, S3 and S4, and then outlier processing is performed on them respectively; wherein S1 and S2 are the total power and the difference power of the horizontal and vertical components respectively, and S3 and S4 are two opposite circularly polarized parts respectively; step S104, drawing a scatter plot between rock abundance and Stokes parameters according to the rock abundance distribution map of the crater calculated in the above step S102, and analyzing the correlation between rock abundance and Stokes parameters S1, S2, S3 and S4 respectively; step S105, fitting the scatter plot to obtain the corresponding fitting function; calculating the Pearson correlation coefficient and drawing the correlation matrix; step S106, establishing a RA-SAR linear regression weighted model based on the results of the above correlation matrix analysis; step S107, using the RA-SAR model to perform rock abundance inversion on other craters to obtain high-resolution rock abundance data.

[0008] Preferably, in step S101, Mini-RF SAR images, NAC optical images and DivinerRA images of the crater are acquired, and preprocessing operations are performed on the acquired image data, specifically:

[0009] Mini-RF SAR images, NAC optical images, and Diviner RA images of the crater were acquired, and a series of preprocessing operations were performed on the data. First, the Mini-RF SAR images and Diviner RA images were projected into a unified lunar coordinate reference system to eliminate the geometric deformation caused by different observation angles; then, the NAC optical image was mosaicked to generate a complete image of the target area by splicing multiple optical images covering the study area. Next, the Mini-RF SAR images and the processed NAC optical images were geometrically corrected to ensure that the two types of data accurately corresponded to the actual positions on the lunar surface. Subsequently, the three types of data were image registered to achieve precise alignment of the Mini-RF SAR data with the NAC optical images and the Diviner RA images in terms of spatial distribution. After the registration, irrelevant data outside the study area were removed through mask processing to ensure that subsequent processing was only for the target crater area. Finally, the Refined Lee filtering algorithm was applied to the SAR data to reduce noise while retaining important backscattering features. After completing this step, the Mini-RF SAR images, NAC optical images, and Diviner RA datasets suitable for subsequent analysis were obtained.

[0010] Preferably, in step S102, rock information in the optical image of the meteorite crater is extracted, and the rock abundance distribution map of the meteorite crater is calculated based on the optical statistical rock information and combined with the Diviner RA image, specifically:

[0011] For lunar craters, although the Diviner instrument can deduce their rock abundance, its resolution is only 240m / piexl, which is not suitable for quantitative analysis; the NAC optical image can capture the rock distribution information around the crater with its high resolution. Combining the two can deduce rock abundance data with higher resolution. First, the rock information around the crater is annotated and counted through the optical image; then the rock coverage is calculated. In order to be consistent with the resolution of Mini-RFSAR data, the rock information counted in the registered optical image and the Diviner RA image is resampled based on the resolution of the SAR data. In each unit area, the resolution r corresponding to the SAR pixel is calculated. SAR Calculate the rock abundance RA per unit area:

[0012]

[0013] Where S i represents the surface area of ​​the i-th rock target, S cell The total rock area in the unit area, M is the number of rock targets in the unit area, RA cellIt indicates the rock coverage per unit area, i.e., rock abundance;

[0014] Finally, the rock abundance data is output and the resampled rock abundance results are saved as raster data to make their resolution consistent with the Mini-RF SAR image, and finally the rock abundance distribution map based on the optical image is generated.

[0015] Preferably, in step S103, the Mini-RF SAR image is subjected to Stokes parameter decomposition to obtain S1, S2, S3 and S4, and then outlier processing is performed on them respectively; wherein S1 and S2 are the total power and the difference power of the horizontal and vertical components respectively, and S3 and S4 are two opposite circularly polarized parts respectively, specifically:

[0016] First, the SAR data is polarized and decomposed to obtain four Stokes parameters, which are shown below.

[0017]

[0018] Here S1 is the total power of the horizontal and vertical components, which represents the total backscattering intensity, and usually reflects the roughness and reflectivity of the target object. In rock abundance modeling, S1 is the parameter that most directly reflects the surface characteristics, and therefore contributes the most to the model. S2 is the difference power between the horizontal and vertical components, which can reveal the anisotropy and polarization characteristics of the target object, and helps to distinguish between rocks and weathering fine particles. S3 and S4 are two opposite circularly polarized parts, respectively. S3 is related to the geometry and directionality of the target object, and can capture complex scattering characteristics for irregular rocks. S4 can provide asymmetric information about the surface scattering of the target object, but its contribution to rock abundance modeling is relatively small. Among them, E H and E V are horizontal polarization and vertical polarization, Re and Im represent the real part and imaginary part, respectively, and * represents conjugation.

[0019] Then, outlier detection and processing are performed, the pixel value distribution of each Stokes parameter is calculated, and outliers are removed. The specific process is as follows:

[0020] Calculate statistical indicators: For each Stokes parameter (S1, S2, S3 and S4), calculate the mean μ and standard deviation σ of its pixel values.

[0021] Define the outlier range: Set the outlier threshold range to [μ-kσ, μ+kσ], where k is an adjustable parameter, and pixel values ​​outside this range are defined as outliers.

[0022] Remove outliers: Replace outliers with the mean or median value in the neighborhood to ensure smoothness and consistency of the data. The replacement formula is:

[0023]

[0024] Where S i is the current pixel value, is the set of its neighborhood pixel values, S′ i This is the result after removing outliers.

[0025] Finally, the processing results are output and the processed S1, S2, S3 and S4 parameters are saved as raster data for subsequent correlation analysis and RA-SAR model construction.

[0026] Preferably, in step S104, a scatter plot between rock abundance and Stokes parameters is drawn according to the rock abundance distribution map of the crater calculated in step S102, and the correlation between rock abundance and Stokes parameters S1, S2, S3 and S4 is analyzed respectively, specifically:

[0027] Draw a scatter plot, use each Stokes parameter S1, S2, S3 and S4 to pair with RAbased-NAC, and draw a scatter plot respectively. The horizontal axis of each scatter plot is the corresponding Stokes parameter value, and the vertical axis is the value of RAbased-NAC. The specific scatter plot is represented as:

[0028] Scatter(S1,RA based-NAC )

[0029] Scatter(S2,RA based-NAC )

[0030] Scatter(S3,RA based-NAC )

[0031] Scatter(S4,RA based-NAC )

[0032] Each scatter plot shows the preliminary relationship between the Stokes parameter and rock abundance, thus revealing the impact of different Stokes parameters on RA. Since there is a certain linear relationship between rock abundance and backscattering signal, the linear function fitting method is used to quantify and evaluate their relationship when analyzing the correlation between the two.

[0033] Preferably, in step S105, a scatter plot is fitted to obtain a corresponding fitting function; the Pearson correlation coefficient is calculated, and a correlation matrix is ​​drawn, specifically:

[0034] Linear fitting was performed on the drawn scatter plot to obtain the fitting functions of RA based-NAC and Stokes parameters S1, S2, S3 and S4 respectively. The linear fitting results were used to determine the slope and intercept of the linear model by the least squares method, and the fitting degree R was calculated at the same time. 2 The value is used to measure the explanatory power of the fitting function on the data. Among them, the Pearson correlation coefficient r represents the strength and direction of the linear correlation between variables. Next, the Pearson correlation coefficient r value of RA based-NAC and each Stokes parameter is calculated, and the formula is:

[0035]

[0036] Among them, X i and Y i Respectively represent the RA based-NAC value and the Stokes parameter value, and is the mean of the corresponding data, and n is the number of samples. Finally, the Pearson correlation coefficient results are presented in matrix form, the correlation matrix of RA based-NAC and Stokes parameters is constructed, and the matrix is ​​visualized to intuitively reflect the strength of the correlation between the variables.

[0037] Preferably, in step S106, based on the above analysis results of the correlation matrix, a RA-SAR linear regression weighted model is established, specifically:

[0038] First, the normalized weights are calculated based on the R of each Stokes parameter S1, S2, S3, S4 in the correlation matrix. 2 The value is then normalized according to the following formula to obtain the weighted coefficient of each parameter: These weights reflect the relative contribution of each Stokes parameter to rock abundance (RA):

[0039]

[0040] Similar to the calculation, the normalization of weights ensures that the influence of all parameters is uniformly quantified, and their sum is equal to 1. Finally, the model is constructed. According to the normalized weights and the actual values ​​of each Stokes parameter, a multi-parameter linear weighted model of rock abundance is constructed, as shown in the following formula:

[0041]

[0042] The model achieves high-precision inversion of rock abundance by comprehensively considering the effects of different parameters on rock abundance. This method uses a multi-parameter linear weighted regression model, which can make full use of the information of each Stokes parameter and improve the explanatory power and fitting accuracy of the model. Compared with the single-parameter model, the multi-parameter weighted model can more comprehensively reflect the distribution law of rock abundance, providing solid support for the subsequent high-resolution rock abundance inversion of lunar craters.

[0043] Preferably, in step S107, the RA-SAR linear regression weighted model is used to perform rock abundance inversion on other craters to obtain high-resolution rock abundance data, specifically:

[0044] First, obtain the Mini-RF SAR data of the target crater area. These images need to go through a preprocessing process similar to step S101, including image registration, filtering and denoising, outlier processing, etc., to ensure the quality and accuracy of the input data. Then, Stokes parameter calculation and extraction: Stokes parameter decomposition is performed on the preprocessed SAR image, and then the RA-SAR model is applied. According to the RA-SAR linear regression weighted model established in step S106, the Stokes parameter value of the target crater area is substituted into the model to calculate the rock abundance of each pixel. The rock abundance (RA) distribution of the area is obtained by combining various Stokes parameters in a weighted manner. Finally, high-resolution rock abundance is generated. During the inversion process, the rock abundance data is calculated using the same spatial resolution as the Mini-RF SAR image to ensure the high resolution and accuracy of the output data. Each pixel is estimated by a linear regression model to generate a high-resolution rock abundance map of the lunar crater, reflecting the rock distribution characteristics of the area. Through this series of steps, the RA-SAR model can accurately invert the rock abundance in the target area, providing strong support for future lunar surface characteristics analysis, in-depth research on craters and related exploration missions.

[0045] The embodiment of the present application also provides a rock abundance inversion system based on optical images and synthetic aperture radar data, including: a data acquisition and preprocessing unit, configured to receive and process data from Mini-RF SAR, Diviner RA and NAC optical images. The unit first projects the SAR data and Diviner RA data, mosaics the optical image, and then performs registration operations on the three types of data to ensure the spatial consistency of the data. Finally, the SAR image is filtered and denoised to remove random noise in the image to improve data quality; a rock abundance extraction and calculation unit, configured to calculate the high-resolution rock abundance of the crater area based on the rock information extracted from the optical image and Diviner RA data. This unit generates rock abundance (RA) data with the same resolution as Mini-RFSAR data by analyzing the rock information in the optical image and combining it with the preprocessed Diviner RA data. Based on these data, regional calculations are performed to obtain rock abundance distribution in the target area with higher resolution. The Stokes parameter decomposition unit is configured to perform Stokes parameter decomposition on the processed SAR image to generate scattering feature images of each polarization state (S1, S2, S3 and S4). This unit extracts the reflection features under different polarization modes by processing the backscattering information obtained, providing basic data for rock abundance inversion. The correlation analysis and RA-SAR model construction unit is configured to perform correlation analysis based on the scatter plot between RA based-NAC and Stokes parameters. This unit uses the Pearson correlation coefficient to calculate the relationship between rock abundance and each Stokes parameter, and constructs a linear regression weighted model. Through this model, the contribution of each Stokes parameter to rock abundance is quantified, and finally a RA-SAR model is generated. The high-resolution rock abundance inversion unit is configured to perform rock abundance inversion for other craters based on the constructed RA-SAR linear regression model. This unit inputs SAR data into the RA-SAR model, calculates high-resolution rock abundance data of the target area, and outputs a rock abundance distribution map, which is the high-resolution rock abundance data of the crater.

[0046] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored, wherein the program is a rock abundance inversion method based on optical images and synthetic aperture radar data as described above.

[0047] An embodiment of the present application also provides an electronic device, comprising: a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the rock abundance inversion method based on optical images and synthetic aperture radar data as described above is implemented.

[0048] Beneficial effects:

[0049] In the rock abundance inversion method based on optical images and synthetic aperture radar data provided by the present application, firstly, Mini-RF SAR images, NAC optical remote sensing images and Diviner RA images of the crater are obtained, and the data are pre-processed such as registration and masking to ensure the high quality and accuracy of the data. Subsequently, the mapping relationship between Stokes parameters and rock abundance is constructed by the registration operation of the rock abundance data calculated by the optical image and the DivinerRA image with the SAR image, and a scatter plot is drawn using correlation analysis to extract the regression features between the parameters. Based on this, a RA-SAR inversion model (Rock Abundance, RA) is constructed using a linear regression method, and a higher weight is given to the significant features in the Stokes parameters to enhance the fitting effect of the model. In the process of rock abundance inversion, the pre-processed SAR image is input into the RA-SAR model, and the high-resolution rock abundance of the lunar crater area is inverted to generate high-precision rock abundance data matching the resolution of the SAR image. Through the present invention, high-resolution rock abundance inversion can be achieved in lunar craters, which makes up for the defect that the resolution of lunar surface rock abundance obtained by the Diviner device is only 240m / piexl, which is too low, and overcomes the lack of optical remote sensing images in some areas and the inability of optical images to accurately invert rock abundance in some areas with high solar incidence angles. At the same time, it avoids the influence of noise existing in SAR images themselves, and improves the accuracy and reliability of rock abundance inversion results. In addition, the rock abundance data inverted by this method can provide important data support for the study of lunar surface geological evolution, further promote the implementation of lunar exploration missions, and are especially important in the selection of lunar landing sites. Through the technical means of the present invention, the rock abundance distribution of lunar craters can be efficiently and accurately obtained, which helps to deepen the understanding of the lunar surface and its geological evolution process. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. Among them:

[0051] Figure 1 This is a flowchart of the application;

[0052] Figure 2 This is a schematic diagram of the structure of this application;

[0053] Figure 3 A schematic diagram of the results of extracting rock information from an optical image of a meteorite crater and registering it with Diviner RA data provided in some embodiments of the present application;

[0054] Figure 4A schematic diagram of a rock abundance resampling process based on rocks extracted from optical images according to some embodiments of the present application;

[0055] Figure 5 A schematic diagram of S1, S2, S3 and S4 parameters obtained by performing Stokes parameter decomposition on a SAR image of a crater provided in some embodiments of the present application;

[0056] Figure 6 A scatter plot of RA based-NAC and Stokes parameters provided according to some embodiments of the present application;

[0057] Figure 7 A result schematic diagram of a correlation matrix obtained by performing correlation analysis on RA based-NAC and Stokes parameters according to some embodiments of the present application;

[0058] Figure 8 A schematic diagram of high-resolution rock abundance results of two craters based on RA-SAR model inversion according to some embodiments of the present application; DETAILED DESCRIPTION

[0059] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. Each example is provided by way of explanation of the present application and does not limit the present application. In fact, it will be clear to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the present application. For example, a feature shown or described as a part of an embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desired that the present application includes such modifications and variations within the scope of the appended claims and their equivalents.

[0060] Exemplary Methods

[0061] Figure 1 A schematic flow chart of a rock abundance inversion method based on optical images and synthetic aperture radar data provided according to some embodiments of the present application; Figure 1 As shown, the rock abundance inversion method based on optical images and synthetic aperture radar data includes:

[0062] Step S101, acquiring Mini-RF SAR images, NAC optical images and Diviner RA images of the crater, and performing preprocessing operations on the acquired image data;

[0063] In the embodiment of the present application, Mini-RF SAR images, NAC optical images and Diviner RA images of the crater are obtained, and a series of preprocessing operations are performed on the data. First, the Mini-RF SAR images and Diviner RA images are projected into a unified lunar coordinate reference system to eliminate the geometric deformation caused by different observation angles; then, the NAC optical image is mosaicked to generate a complete target area image by splicing multiple optical images covering the study area. Next, the Mini-RF SAR images and the processed NAC optical images were geometrically corrected to ensure that the two types of data accurately corresponded to the actual positions on the lunar surface. Subsequently, the three types of data were image registered to achieve precise alignment of the Mini-RF SAR data with the NAC optical images and the Diviner RA images in terms of spatial distribution. After the registration, irrelevant data outside the study area were removed through mask processing to ensure that subsequent processing was only for the target crater area. Finally, the Refined Lee filtering algorithm was applied to the SAR data to reduce noise while retaining important backscattering features. After completing this step, the Mini-RF SAR images, NAC optical images, and Diviner RA datasets suitable for subsequent analysis were obtained.

[0064] Step S102, extracting rock information from the optical image of the meteorite crater, and calculating the rock abundance distribution map of the meteorite crater based on the optical statistical rock information and the Diviner RA image;

[0065] For lunar craters, although the Diviner instrument can deduce their rock abundance, its resolution is only 240m / piexl, which is not suitable for quantitative analysis; the NAC optical image can capture the rock distribution information around the crater with its high resolution. Combining the two can deduce rock abundance data with higher resolution. First, the rock information around the crater is annotated and counted through the optical image; then the rock coverage is calculated. In order to be consistent with the resolution of Mini-RFSAR data, the rock information counted in the registered optical image and the Diviner RA image is resampled based on the resolution of the SAR data. In each unit area, the resolution T corresponding to the SAR pixel is calculated. SAR Calculate the rock abundance RA per unit area:

[0066]

[0067] Where S i represents the surface area of ​​the i-th rock target, S cell The total rock area in the unit area, M is the number of rock targets in the unit area, RA cellIt indicates the rock coverage per unit area, i.e., rock abundance;

[0068] Finally, the rock abundance data is output and the resampled rock abundance results are saved as raster data to make their resolution consistent with the Mini-RF SAR image, and finally the rock abundance distribution map RA basedNAC based on the optical image is generated.

[0069] Step S103, performing Stokes parameter decomposition on the Mini-RF SAR image to obtain S1, S2, S3 and S4, and then performing outlier processing on them respectively; wherein S1 and S2 are the total power and the difference power of the horizontal and vertical components respectively, and S3 and S4 are two opposite circularly polarized parts respectively;

[0070] In the embodiment of the present application, polarization decomposition is first performed on the SAR data to calculate four Stokes parameters, which are shown below respectively.

[0071]

[0072] Here S1 is the total power of the horizontal and vertical components, which represents the total backscattering intensity, and usually reflects the roughness and reflectivity of the target object. In rock abundance modeling, S1 is the parameter that most directly reflects the surface characteristics, and therefore contributes the most to the model. S2 is the difference power between the horizontal and vertical components, which can reveal the anisotropy and polarization characteristics of the target object, and helps to distinguish between rocks and weathering fine particles. S3 and S4 are two opposite circularly polarized parts, respectively. S3 is related to the geometry and directionality of the target object, and can capture complex scattering characteristics for irregular rocks. S4 can provide asymmetric information about the surface scattering of the target object, but its contribution to rock abundance modeling is relatively small. Among them, E H and E V are horizontal polarization and vertical polarization, Re and Im represent the real part and imaginary part, respectively, and * represents conjugation.

[0073] Then, outlier detection and processing are performed, the pixel value distribution of each Stokes parameter is calculated, and outliers are removed. The specific process is as follows:

[0074] Calculate statistical indicators: For each Stokes parameter (S1, S2, S3 and S4), calculate the mean μ and standard deviation σ of its pixel values.

[0075] Define the outlier range: Set the outlier threshold range to [μ-kσ, μ+kσ], where k is an adjustable parameter, and pixel values ​​outside this range are defined as outliers.

[0076] Remove outliers: Replace outliers with the mean or median value in the neighborhood to ensure smoothness and consistency of the data. The replacement formula is:

[0077]

[0078] Where S i is the current pixel value, is the set of its neighborhood pixel values, S′ i This is the result after removing outliers.

[0079] Finally, the processing results are output and the processed S1, S2, S3 and S4 parameters are saved as raster data for subsequent correlation analysis and RA-SAR model construction.

[0080] Step S104, drawing a scatter plot between rock abundance and Stokes parameters based on the rock abundance distribution map of the crater calculated in step S102, and analyzing the correlation between rock abundance and Stokes parameters S1, S2, S3 and S4 respectively;

[0081] Draw a scatter plot, use each Stokes parameter S1, S2, S3 and S4 to pair with RAbased-NAC, and draw a scatter plot respectively. The horizontal axis of each scatter plot is the corresponding Stokes parameter value, and the vertical axis is the value of RAbased-NAC. The specific scatter plot is represented as:

[0082] Scatter(S1,RA based-NAC )

[0083] Scatter(S2,RA based-NAC )

[0084] Scatter(S3,RA based-NAC )

[0085] Scatter(S4,RA based-NAC )

[0086] Each scatter plot shows the preliminary relationship between the Stokes parameter and rock abundance, thus revealing the impact of different Stokes parameters on RA. Since there is a certain linear relationship between rock abundance and backscattering signal, the linear function fitting method is used to quantify and evaluate their relationship when analyzing the correlation between the two.

[0087] Step S105, fitting the scatter plot to obtain a corresponding fitting function; calculating the Pearson correlation coefficient and drawing a correlation matrix;

[0088] In the embodiment of the present application, a linear fit is performed on the drawn scatter plot to obtain the fitting functions of RA based-NAC and Stokes parameters S1, S2, S3 and S4 respectively. The linear fitting results are used to determine the slope and intercept of the linear model by the least squares method, and the fitting degree R is calculated at the same time. 2 The value is used to measure the explanatory power of the fitting function on the data. Among them, the Pearson correlation coefficient r represents the strength and direction of the linear correlation between variables. Next, the Pearson correlation coefficient r value of RA based-NAC and each Stokes parameter is calculated, and the formula is:

[0089]

[0090] Among them, X i and Y i Respectively represent the RA based-NAC value and the Stokes parameter value, and is the mean of the corresponding data, and n is the number of samples. Finally, the Pearson correlation coefficient results are presented in matrix form, the correlation matrix of RA based-NAC and Stokes parameters is constructed, and the matrix is ​​visualized to intuitively reflect the strength of the correlation between the variables.

[0091] Step S106: Based on the above analysis results of the correlation matrix, a RA-SAR linear regression weighted model is established;

[0092] In the embodiment of the present application, first, the normalized weight calculation is performed based on the R of each Stokes parameter S1, S2, S3 and S4 in the correlation matrix. 2 The value is then normalized according to the following formula to obtain the weighted coefficient of each parameter: These weights reflect the relative contribution of each Stokes parameter to rock abundance (RA):

[0093]

[0094] Similar to the calculation, the normalization of weights ensures that the influence of all parameters is uniformly quantified, and their sum is equal to 1. Finally, the model is constructed. According to the normalized weights and the actual values ​​of each Stokes parameter, a multi-parameter linear weighted model of rock abundance is constructed, as shown in the following formula:

[0095]

[0096] The model achieves high-precision inversion of rock abundance by comprehensively considering the effects of different parameters on rock abundance. This method uses a multi-parameter linear weighted regression model, which can make full use of the information of each Stokes parameter and improve the explanatory power and fitting accuracy of the model. Compared with the single-parameter model, the multi-parameter weighted model can more comprehensively reflect the distribution law of rock abundance, providing solid support for the subsequent high-resolution rock abundance inversion of lunar craters.

[0097] Step S107, using the RA-SAR linear regression weighted model to perform rock abundance inversion on other meteorite craters to obtain high-resolution rock abundance data;

[0098] In an embodiment of the present application, first, the Mini-RF SAR data of the target crater area is obtained. These images need to undergo a preprocessing process similar to step S101, including image registration, filtering and denoising, outlier processing, etc., to ensure the quality and accuracy of the input data. Then, Stokes parameter calculation and extraction: Stokes parameter decomposition is performed on the preprocessed SAR image, and then the RA-SAR model is applied. According to the RA-SAR linear regression weighted model established in step S106, the Stokes parameter value of the target crater area is substituted into the model to calculate the rock abundance of each pixel. The rock abundance (RA) distribution of the area is obtained by combining various Stokes parameters in a weighted manner through the model. Finally, high-resolution rock abundance is generated. During the inversion process, the rock abundance data is calculated using the same spatial resolution as the Mini-RF SAR image to ensure the high resolution and accuracy of the output data. Each pixel is estimated by a linear regression model to generate a high-resolution rock abundance map of the lunar crater, reflecting the rock distribution characteristics of the area. Through this series of steps, the RA-SAR model can accurately invert the rock abundance in the target area, providing strong support for future lunar surface characteristics analysis, in-depth research on craters and related exploration missions.

[0099] The present invention uses polarimetric SAR (Synthetic Aperture Radar) data obtained from the LRO (Lunar Reconnaissance Orbiter) Mini-RF device to extract Stokes parameters (S1, S2, S3 and S4), and combines the high-resolution rock abundance data obtained by inverting optical data (LROC NAC) and Diviner RA data (LRO Diviner Rock Abundance, 240m / piexl) to establish a rock abundance inversion model. The present invention is of great significance for studying the distribution information of lunar surface rocks and selecting landing sites for future lunar exploration.

[0100] Exemplary Systems

[0101] Figure 2 Schematic diagram of a rock abundance inversion system based on optical images and synthetic aperture radar data provided according to some embodiments of the present application; Figure 2 As shown; the rock abundance inversion system based on optical images and synthetic aperture radar data includes: a data acquisition and preprocessing unit, configured to receive and process data from Mini-RF SAR, DivinerRA and NAC optical images. The unit first projects the SAR data and Diviner RA data, mosaics the optical image, and then performs registration operations on the three types of data to ensure the spatial consistency of the data. Finally, the SAR image is filtered and denoised to remove random noise in the image to improve the data quality; the rock abundance extraction and calculation unit is configured to calculate the high-resolution rock abundance of the crater area based on the rock information extracted from the optical image and Diviner RA data. This unit generates rock abundance (RA) data with the same resolution as Mini-RF SAR data by analyzing the rock information in the optical image and combining it with the preprocessed Diviner RA data. Based on these data, regional calculations are performed to obtain rock abundance distribution in the target area with higher resolution. The Stokes parameter decomposition unit is configured to perform Stokes parameter decomposition on the processed SAR image to generate scattering feature images of each polarization state (S1, S2, S3 and S4). This unit extracts the reflection characteristics under different polarization modes by processing the backscattering information obtained, providing basic data for rock abundance inversion. The correlation analysis and RA-SAR model construction unit is configured to perform correlation analysis based on the scatter plot between RA based-NAC and Stokes parameters. This unit uses the Pearson correlation coefficient to calculate the relationship between rock abundance and each Stokes parameter, and constructs a linear regression weighted model. Through this model, the contribution of each Stokes parameter to rock abundance is quantified, and finally a RA-SAR model is generated. The high-resolution rock abundance inversion unit is configured to perform rock abundance inversion for other craters based on the constructed RA-SAR linear regression model. This unit inputs SAR data into the RA-SAR model, calculates high-resolution rock abundance data of the target area, and outputs a rock abundance distribution map, which is the high-resolution rock abundance data of the crater.

[0102] The rock abundance inversion system based on optical images and synthetic aperture radar data provided in the embodiment of the present application can implement any of the above-mentioned steps and processes of rock abundance inversion based on optical images and synthetic aperture radar data, and achieve the same technical effects, which will not be repeated here one by one.

[0103] Figure 3(a) is a schematic diagram of the result after using NAC optical images to extract rocks around the crater; Figure 3 (b) is a magnified image of the extracted rock, corresponding to Figure 3 The red rectangular box in (a); Figure 3 (c) shows the results of rock information extracted from optical images and Diviner RA image registration; Figure 3 (d) Correspondence Figure 3 (c) A magnified image of the registration result. Figure 4 A schematic diagram of calculating high-resolution rock abundance based on optical images is given, showing the process of resampling the resolution from 240m / pixel to 60m / pixel and 15m / pixel. Figure 5 The results of S1, S2, S3 and S4 after Stokes parameter decomposition of the SAR image of the crater. Figure 6 The scatter plot between rock abundance and Stokes parameters is drawn, and the results after function fitting are given. Figure 7 It is the correlation matrix of rock abundance and Stokes parameters calculated based on the results of scatter plot fitting, among which S1 has the highest correlation with rock abundance, and S4 has the lowest correlation with rock abundance. This correlation matrix can be used to construct a RA-SAR linear weighted regression model, and then invert the high-resolution rock abundance distribution maps of other craters. Figure 8 This is the result of using the RA-SAR model to invert the rock abundance of two craters. The resolution is 15m / pixel, which is much higher than the 240m / pixel resolution of Diviner RA.

[0104] Exemplary Devices

[0105] The present application provides an electronic device, including a storage device, a processor, the processor is suitable for executing various programs; the memory is used to store multiple programs; when the memory executes the programs on the processor, the rock abundance inversion method based on optical images and synthetic aperture radar data as described above is implemented. Since the steps of the rock abundance inversion method based on optical images and synthetic aperture radar data have been introduced in detail in the specific implementation method example, they will not be described in detail here.

[0106] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the device where the computer-readable storage medium is located executes the rock abundance inversion method based on optical images and synthetic aperture radar data as described above. The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory and other memory.

[0107] If the module / unit integrated in the electronic device described in this application is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware devices through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0108] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0109] Computer-readable instructions are stored in the computer-readable storage medium, and the computer-readable instructions are executed by a processor in an electronic device to implement the rock abundance inversion method based on optical images and synthetic aperture radar data described in any of the above embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0111] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

[0113] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0114] Note that the above are only preferred embodiments of the present invention and the principles of the application technology. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention is described in more detail through the above embodiments, the present invention is not limited to the specific embodiments described herein, and may include more other effective embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

[0115] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

Claims

1. A rock abundance inversion method based on optical images and synthetic aperture radar data, characterized in that: The steps include: Step S101, acquiring Mini-RF SAR images, NAC optical images and Diviner RA images of the crater, and performing preprocessing operations on the acquired image data; Step S102, extracting rock information from the optical image of the meteorite crater, and calculating the rock abundance distribution map of the meteorite crater based on the optical statistical rock information and the Diviner RA image; Step S103, performing Stokes parameter decomposition on the Mini-RF SAR image to obtain S1, S2, S3 and S4, and then performing outlier processing on them respectively; wherein S1 and S2 are the total power and the difference power of the horizontal and vertical components respectively, and S3 and S4 are two opposite circularly polarized parts respectively; Step S104, drawing a scatter plot between rock abundance and Stokes parameters based on the rock abundance distribution map of the crater calculated in step S102, and analyzing the correlation between rock abundance and Stokes parameters S1, S2, S3 and S4 respectively; Step S105, fitting the scatter plot to obtain a corresponding fitting function; calculating the Pearson correlation coefficient and drawing a correlation matrix; Step S106: Based on the above analysis result of the correlation matrix, a RA-SAR linear regression weighted model is established; Step S107: Use the RA-SAR linear regression weighted model to perform rock abundance inversion on other craters to obtain high-resolution rock abundance data.

2. The rock abundance inversion method based on optical images and synthetic aperture radar data according to claim 1, characterized in that: Step S101 specifically includes the following steps: Firstly, the Mini-RF SAR images and Diviner RA images are projected into a unified lunar coordinate reference system to eliminate the geometric deformation caused by different observation angles. Subsequently, the NAC optical image was mosaicked to generate a complete target area image by stitching together multiple optical images covering the study area; Next, the Mini-RF SAR image and the processed NAC optical image were geometrically corrected to ensure that the two data could accurately correspond to the actual position on the lunar surface; Subsequently, the three types of data were image registered to achieve accurate alignment of the Mini-RF SAR data with the NAC optical image and the DivinerRA image in terms of spatial distribution; After the registration is completed, mask processing is used to remove irrelevant data outside the study area to ensure that subsequent processing is only targeted at the target crater area; Finally, a Refined Lee filtering algorithm is applied to the SAR data to reduce noise while retaining important backscattering features.

3. The rock abundance inversion method based on optical images and synthetic aperture radar data according to claim 1, characterized in that: The step S102 specifically includes the following steps: First, the rock information around the crater is annotated and counted through optical images. Then, the rock coverage is calculated. In order to be consistent with the resolution of Mini-RF SAR data, the rock information counted in the registered optical image and Diviner RA image is resampled based on the resolution of SAR data. In each unit area, the resolution r corresponding to the SAR pixel is SAR Calculate the rock abundance RA per unit area: Where S i represents the surface area of ​​the i-th rock target, S xell The total rock area in the unit area, M is the number of rock targets in the unit area, RA cell It indicates the rock coverage per unit area, i.e., rock abundance; Finally, the rock abundance data is output and the resampled rock abundance results are saved as raster data to make their resolution consistent with the Mini-RF SAR image, and finally the rock abundance distribution map RA based NAC based on the optical image is generated.

4. The rock abundance inversion method based on optical images and synthetic aperture radar data according to claim 1, characterized in that: Step S103 specifically includes the following steps: First, polarization decomposition is performed on the SAR data to calculate four Stokes parameters, which are shown below: Among them, E H and E V are horizontal polarization and vertical polarization, Re and Im represent the real part and imaginary part, respectively, and * represents conjugation; Then, outlier detection and processing are performed, the pixel value distribution of each Stokes parameter is calculated, and outliers are removed; the specific process is as follows: Calculate statistical indicators: For each Stokes parameter S1, S2, S3 and S4, calculate the mean μ and standard deviation σ of its pixel value; Define the outlier range: Set the outlier threshold range to [μ-kσ,μ+kσ], where k is an adjustable parameter, and pixel values ​​outside this range are defined as outliers; Remove outliers: Replace outliers with the mean or median value in the neighborhood to ensure the smoothness and consistency of the data; the replacement formula is: Where S i is the current pixel value, is the set of its neighborhood pixel values, S i ′ This is the result after removing outliers; Finally, the processing results are output and the processed S1, S2, S3 and S4 parameters are saved as raster data for subsequent correlation analysis and RA-SAR model construction.

5. The rock abundance inversion method based on optical images and synthetic aperture radar data according to claim 1, characterized in that: Step S104 specifically includes the following steps: Draw a scatter plot, use each Stokes parameter S1, S2, S3 and S4 to pair with RA based-NAC, and draw a scatter plot respectively: the horizontal axis of each scatter plot is the corresponding Stokes parameter value, and the vertical axis is the value of RA based-NAC; the specific scatter plot is expressed as: Scatter(S1,RA based-NAC ) Scatter(S2,RA based-NAC ) Scatter(S3,RA based-NAC , Scatter(S4,RA based-NAC , Each scatter plot shows the preliminary relationship between the Stokes parameter and rock abundance, thus revealing the impact of different Stokes parameters on RA. Since there is a certain linear relationship between rock abundance and backscattering signal, the linear function fitting method is used to quantify and evaluate their relationship when analyzing the correlation between the two.

6. The rock abundance inversion method based on optical images and synthetic aperture radar data according to claim 1, characterized in that: Step S105 specifically includes the following steps: Linear fitting was performed on the drawn scatter plots to obtain the fitting functions of RA based-NAC and Stokes parameters S1, S2, S3 and S4 respectively; The linear fitting results are used to determine the slope and intercept of the linear model by the least squares method, and the fitting degree R is calculated at the same time. 2 The value is used to measure the explanatory power of the fitting function on the data; among them, the Pearson correlation coefficient r represents the strength and direction of the linear correlation between variables; Next, the Pearson correlation coefficient r value between RA based-NAC and each Stokes parameter is calculated, and the formula is: Among them, X i and Y i Respectively represent the RA based-NAC value and the Stokes parameter value, and is the mean of the corresponding data, and n is the number of samples; Finally, the Pearson correlation coefficient results are presented in matrix form, the correlation matrix between RA based-NAC and Stokes parameters is constructed, and the matrix is ​​visualized to intuitively reflect the strength of the correlation between the variables.

7. The rock abundance inversion method based on optical images and synthetic aperture radar data according to claim 1, characterized in that: Step S106 specifically includes the following steps: First, the normalized weights are calculated based on the fit R of each Stokes parameter S1, S2, S3, and S4 in the correlation matrix. 2 value, Then, normalize and calculate according to the following formula to obtain the weighted coefficient of each parameter: These weights reflect the relative contribution of each Stokes parameter to the rock abundance RA: Similar to the calculation, the normalization of weights ensures that the influence of all parameters is uniformly quantified and their sum is equal to 1; Finally, the model is constructed. According to the normalized weights and the actual values ​​of each Stokes parameter, a multi-parameter linear weighted model of rock abundance is constructed, as shown in the following formula:

8. A rock abundance inversion system based on optical images and synthetic aperture radar data, characterized by: include: A data acquisition and preprocessing unit is configured to receive and process data from Mini-RF SAR, Diviner RA and NAC optical images; the unit first projects the SAR data and Diviner RA data, mosaics the optical images, and then registers the three data to ensure spatial consistency of the data; and finally filters and denoises the SAR images to remove random noise in the images to improve data quality; a rock abundance extraction and calculation unit configured to calculate high-resolution rock abundance in the crater region based on rock information extracted from the optical image and the Diviner RA data; This unit generates rock abundance RA data with the same resolution as Mini-RF SAR data by analyzing the rock information in the optical image and combining it with the pre-processed Diviner RA data. Based on this data, regional calculations are performed to obtain higher-resolution rock abundance distribution in the target area. A Stokes parameter decomposition unit is configured to perform Stokes parameter decomposition on the processed SAR image to generate scattering characteristic images of each polarization state; The unit extracts the reflection characteristics under different polarization modes by processing the obtained backscattering information, providing basic data for rock abundance inversion; The correlation analysis and RA-SAR model building unit is configured to perform correlation analysis based on the scatter plot between RA based-NAC and Stokes parameters. This unit uses the Pearson correlation coefficient to calculate the relationship between rock abundance and each Stokes parameter, and builds a linear regression weighted model to quantify the contribution of each Stokes parameter to rock abundance, and finally generates a RA-SAR model; The high-resolution rock abundance inversion unit is configured to perform rock abundance inversion for other craters based on the constructed RA-SAR linear regression model. This unit inputs SAR data into the RA-SAR model, calculates high-resolution rock abundance data for the target area, and outputs a rock abundance distribution map for subsequent analysis and application.

9. A storage device storing a plurality of programs, characterized in that: The program application is loaded and executed by a processor to implement the rock abundance inversion method based on optical images and synthetic aperture radar data as described in any one of claims 1-7.

10. An electronic device, characterized in that: include: A memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the rock abundance inversion method based on optical images and synthetic aperture radar data as described in any one of claims 1 to 7 is implemented.

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