Rock abundance inversion methods, systems, storage media, and electronic devices based on optical images and synthetic aperture radar data.

Abstract

This application provides a method, system, storage medium, and electronic device for rock abundance inversion based on optical images and synthetic aperture radar (SAR) data. The method includes the following steps: preprocessing the Mini-RF polarimetric SAR image; extracting regression features between parameters; constructing a RA-SAR inversion model using a linear regression method; and assigning higher weights to significant features in the Stokes parameters to enhance the model's fitting effect. This invention combines Mini-RF SAR data with optical remote sensing data, overcoming the limitations of optical data in certain study areas due to excessive solar incidence angles and insufficient coverage. It also efficiently acquires higher-resolution rock abundance distributions of large-scale lunar craters, compensating for the low resolution of lunar surface rock abundance acquired by the Diviner instrument, and providing a novel technical approach for lunar surface geological evolution research.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to rock abundance inversion methods, systems, computer-readable storage media, and electronic devices based on optical images and synthetic aperture radar data. Background Technology

[0002] Synthetic Aperture Radar (SAR), as an active remote sensing technology, is widely used for detecting the surface and subsurface features of the Earth and planets. SAR's advantages lie in its independence from external factors such as day / night cycles and weather conditions, enabling it to acquire 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. Through analysis of radar echoes, SAR can provide information about topography, geological structure, rock abundance, and, especially at high resolution, effectively reveal the complex geological features of the lunar surface. Currently, SAR systems used in lunar exploration projects include the Mini-SAR aboard Chandrayaan-1, the Mini-RF aboard the LRO, and the DFSAR aboard Chandrayaan-2.

[0003] Lunar craters, as important markers of lunar geological evolution, not only contribute to the study of lunar impact history but also provide crucial information for understanding the composition of the lunar regolith and subsurface materials. The quantity and distribution of rocks in the ejecta of craters provide important clues about impact processes, rock abundance, and other geological features. Studying the rock abundance (RA) of these ejecta is a key task in lunar surface geological evolution research. Currently, lunar surface rock abundance data is derived from the LRO Diviner instrument, but its resolution of 240 m / pixel is too low for quantitative analysis of smaller craters. In existing technologies, optical remote sensing images play a significant role in lunar surface morphology characterization, especially in the identification of rocks in craters and their ejecta. However, while the application of optical images in rock abundance inversion is feasible, it requires rock identification and annotation, which is labor-intensive and tedious, especially in areas with dense crater distribution, where the processing and analysis of optical images become even more complex. Another major limitation of optical data is its susceptibility to factors such as the angle of solar incidence and variations in illumination, which prevents it from providing comprehensive information on the rock abundance of craters. Finally, some lunar craters lack high-resolution optical data. In contrast, synthetic aperture radar (SAR) imagery is unaffected by these factors and has a significant advantage in probing the lunar regolith. High-resolution SAR data, particularly Mini-RF, provides detailed information on the lunar surface and subsurface, overcoming some limitations of optical imagery. However, SAR images themselves also suffer from noise and the inability to directly quantify rock abundance data around craters.

[0004] In summary, existing lunar surface rock abundance data have too low resolution, while optically derived rock abundance data, although with high resolution, suffers from numerous shortcomings. Therefore, effectively combining SAR and optical data to compensate for their respective deficiencies and provide higher resolution and more accurate rock abundance inversion has become a pressing technical challenge. Summary of the Invention

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

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] This application provides a rock abundance inversion method based on optical images and synthetic aperture radar data. The method includes: step S101, acquiring Mini-RF SAR images, NAC optical images, and Diviner RA images of a meteorite crater, and performing preprocessing operations on the acquired image data; step S102, extracting rock information from the optical images of the meteorite crater, and calculating the rock abundance distribution map of the meteorite crater based on the rock information obtained from the optical images and combined with the Diviner RA images; step S103, processing the Mini-RF images... The SAR image is decomposed using Stokes parameters to obtain S1, S2, S3, and S4, and then outlier processing is performed on each of them. S1 and S2 represent the total power and differential power of the horizontal and vertical components, respectively, while S3 and S4 represent two parts with opposite circular polarization. In step S104, a scatter plot of rock abundance and Stokes parameters is plotted based on the rock abundance distribution map of the impact crater calculated in step S102, and the correlation between rock abundance and Stokes parameters S1, S2, S3, and S4 is analyzed. In step S105, the scatter plot is fitted to obtain the corresponding fitting function; the Pearson correlation coefficient is calculated, and the correlation matrix is ​​plotted. In step S106, based on the results of the correlation matrix analysis, a RA-SAR linear regression weighted model is established. In step S107, the RA-SAR model is used to invert the rock abundance of other impact 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 impact crater are acquired, and the acquired image data is preprocessed, specifically as follows:

[0009] Mini-RF SAR, NAC optical, 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 and Diviner RA images were projected onto a unified lunar coordinate reference system to eliminate geometric distortions caused by different observation perspectives. Subsequently, the NAC optical images were mosaicked, generating a complete image of the target area by stitching together multiple optical images covering the study area. Next, geometric corrections were performed on the Mini-RF SAR image and the processed NAC optical image to ensure that both datasets accurately corresponded to the actual locations on the lunar surface. Subsequently, image registration was performed on the three datasets to achieve precise spatial alignment between the Mini-RF SAR data, the NAC optical image, and the Diviner RA image. After registration, irrelevant data outside the study area was removed using masking to ensure that subsequent processing was only applied to the target crater region. Finally, the Refined Lee filtering algorithm was applied to the SAR data to reduce noise while preserving important backscattering features. After this step, the Mini-RF SAR image, NAC optical image, and Diviner RA dataset suitable for subsequent analysis were obtained.

[0010] Preferably, in step S102, rock information is extracted from the optical image of the impact crater, and the rock abundance distribution map of the impact crater is calculated based on the optically statistical rock information and in combination with the Diviner RA image. Specifically:

[0011] While the Diviner instrument can derive rock abundance for lunar craters, its resolution of only 240 m / pixel is unsuitable for quantitative analysis. In contrast, NAC optical images, with their high resolution, can effectively capture rock distribution information around craters. Combining the two allows for the derivation of higher-resolution rock abundance data. First, rock information around craters is labeled and statistically analyzed using optical images. Then, rock coverage is calculated. To maintain resolution consistency with Mini-RFSAR data, the rock information statistically derived from the registered optical and Diviner RA images is resampled based on the SAR data resolution. Within each unit area, the corresponding SAR pixel resolution r is calculated. SAR Calculate rock abundance RA per unit area:

[0012]

[0013] Where S i Let S represent the surface area of ​​the i-th rock target. cell The sum of the total rock area within a unit region, where M is the number of rock targets within the unit region, RA cellThis 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 with a resolution consistent with the Mini-RF SAR image. This results in the generation of a rock abundance distribution map (RA based NAC) based on the optical image.

[0015] Preferably, in step S103, the Mini-RF SAR image is decomposed using 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 difference power of the horizontal and vertical components, respectively, and S3 and S4 are the two parts with opposite circular polarization, specifically:

[0016] First, polarization decomposition is performed on the SAR data to calculate four Stokes parameters, as shown below.

[0017]

[0018] Here, S1 represents the total power of the horizontal and vertical components, indicating the total backscattering intensity. It typically reflects the roughness and reflectivity of the target object. In rock abundance modeling, S1 is the parameter that most directly reflects surface characteristics, thus contributing the most to the model. S2 is the difference power between the horizontal and vertical components, which reveals the anisotropy and polarization characteristics of the target object, helping to distinguish between rocks and fine-grained weathering layers. S3 and S4 are two parts with opposite circular polarization. S3 is related to the geometry and orientation of the target object and can capture complex scattering characteristics for irregular rocks. S4 provides asymmetric information about surface scattering of the target object, but its contribution to rock abundance modeling is relatively small. Where E... H and E V denoted as horizontal polarization and vertical polarization, respectively; Re and Im represent the real part and imaginary part, respectively; and * denotes conjugation.

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

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

[0021] Define 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] Outlier removal: Replace outliers with the mean or median of their neighborhood to ensure data smoothness and consistency. The replacement formula is:

[0023]

[0024] Where S i The current pixel value. Let S′ be the set of its neighboring pixel values. 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 of rock abundance and Stokes parameters is drawn based on the rock abundance distribution map of the impact crater calculated in step S102 above, and the correlation between rock abundance and Stokes parameters S1, S2, S3 and S4 is analyzed respectively, specifically as follows:

[0027] Plot scatter plots by pairing each Stokes parameter S1, S2, S3, and S4 with the RAbased-NAC value. The x-axis of each scatter plot represents the corresponding Stokes parameter value, and the y-axis represents the RAbased-NAC value. The specific scatter plot representation is as follows:

[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 Stokes parameters and rock abundance, thus revealing the impact of different Stokes parameters on RA. Since there is a certain linear relationship between rock abundance and backscattered signal, a linear function fitting method is used to quantify and evaluate the relationship when analyzing the correlation between the two.

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

[0034] Linear fitting was performed on the plotted scatter plots to obtain the fitting functions for RA based-NAC and Stokes parameters S1, S2, S3, and S4, respectively. The slope and intercept of the linear model were determined using the least squares method, and the goodness of fit R was calculated. 2 The value is used to measure the interpretability of the fitted function for the data. The Pearson correlation coefficient r represents the strength and direction of the linear correlation between the variables. Next, the Pearson correlation coefficient r between RA based-NAC and each Stokes parameter is calculated using the following formula:

[0035]

[0036] Among them, X i and Y i These represent the RA-based-NAC value and the Stokes parameter value, respectively. and This represents the mean of the corresponding data, and n is the number of samples. Finally, the Pearson correlation coefficient results are presented in matrix form. A 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.

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

[0038] First, the normalized weights are calculated based on the R-squared values ​​of each Stokes parameter S1, S2, S3, and S4 in the correlation matrix. 2 The values ​​are then normalized according to the following formula to obtain the weighting coefficients 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 the weights ensures that the influence of all parameters is uniformly quantified, and their sum equals 1. Finally, the model is constructed. Based on the normalized weights and the actual values ​​of each Stokes parameter, a multi-parameter linear weighted model of rock abundance is built, as shown in the following formula:

[0041]

[0042] This model achieves high-precision inversion of rock abundance by comprehensively considering the influence of different parameters on rock abundance. The method employs a multi-parameter linear weighted regression model, which fully utilizes the information from each Stokes parameter, improving the model's explanatory power and fitting accuracy. Compared to single-parameter models, the multi-parameter weighted model can more comprehensively reflect the distribution pattern of rock abundance, providing solid support for subsequent high-resolution rock abundance inversion of lunar craters.

[0043] Preferably, in step S107, the rock abundance of other impact craters is inverted using a RA-SAR linear regression weighted model to obtain high-resolution rock abundance data, specifically as follows:

[0044] First, Mini-RF SAR data of the target crater region is acquired. These images undergo a preprocessing process similar to step S101, including image registration, filtering and denoising, and outlier handling, to ensure the quality and accuracy of the input data. Next, Stokes parameters are calculated and extracted: the preprocessed SAR images are decomposed using Stokes parameters, and then an RA-SAR model is applied. Based on the RA-SAR linear regression weighted model established in step S106, the Stokes parameter values ​​of the target crater region are substituted into the model to calculate the rock abundance of each pixel. The rock abundance (RA) distribution of the region is obtained by combining the various Stokes parameters through model weighting. Finally, high-resolution rock abundance is generated. During the inversion process, the same spatial resolution as the Mini-RF SAR images is used to calculate the rock abundance data, ensuring high resolution and accuracy of the output data. A high-resolution rock abundance map of the lunar crater is generated by estimating each pixel using a linear regression model, reflecting the rock distribution characteristics of the region. Through this series of steps, the RA-SAR model can accurately invert the rock abundance of the target area, providing strong support for future lunar surface characteristic analysis, in-depth research on impact craters, and related exploration missions.

[0045] This application also provides a rock abundance inversion system based on optical images and synthetic aperture radar (SAR) data, including: a data acquisition and preprocessing unit configured to receive and process data from Mini-RF SAR, Diviner RA, and NAC optical images. This unit first performs projection operations on the SAR and Diviner RA data and mosaics the optical images, then performs registration operations on the three types of data to ensure spatial consistency. Finally, it filters and denoises the SAR images to remove random noise and improve data quality; and 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 images and Diviner RA data. This unit analyzes rock information in optical images and combines it with preprocessed Diviner RA data to generate rock abundance (RA) data with the same resolution as Mini-RFSAR data. Based on this data, regional calculations are performed to obtain a higher-resolution rock abundance distribution for the target area. The Stokes parameter decomposition unit is configured to perform Stokes parameter decomposition on the processed SAR image, generating scattering feature images for each polarization state (S1, S2, S3, and S4). This unit extracts reflection features under different polarization modes from the processed backscattering information, 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. This model quantifies the contribution of each Stokes parameter to rock abundance, ultimately generating an RA-SAR model. The high-resolution rock abundance inversion unit is configured to perform rock abundance inversion on other impact 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, which is the high-resolution rock abundance data of the impact crater.

[0046] This application also provides a computer-readable storage medium storing a computer program, the program being a rock abundance inversion method based on optical images and synthetic aperture radar data as described above.

[0047] This application also provides an electronic device, including: a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the rock abundance inversion method based on optical images and synthetic aperture radar data as described above.

[0048] Beneficial effects:

[0049] The rock abundance inversion method based on optical images and synthetic aperture radar data provided in this application first acquires Mini-RF SAR images, NAC optical remote sensing images, and Diviner RA images of lunar craters. Preprocessing operations such as registration and masking are performed on the data to ensure high quality and accuracy. Subsequently, the rock abundance data calculated from the optical and Diviner RA images is registered with the SAR images to establish a mapping relationship between Stokes parameters and rock abundance. Correlation analysis is used to create scatter plots and extract regression features between parameters. Based on this, a linear regression method is used to construct a RA-SAR inversion model (Rock Abundance, RA), assigning higher weights to significant features in the Stokes parameters to enhance the model's fitting effect. During the rock abundance inversion process, the preprocessed SAR images are input into the RA-SAR model to invert high-resolution rock abundance in lunar crater regions, generating high-precision rock abundance data that matches the resolution of the SAR images. This invention enables high-resolution rock abundance inversion in lunar craters, overcoming the limitation of the low resolution of only 240 m / pixel obtained by the Diviner instrument. It also overcomes the shortcomings of insufficient optical remote sensing imagery in some areas and the inability of optical images to accurately invert rock abundance in regions with high solar incidence angles. Furthermore, it avoids the noise effects inherent in SAR images, improving the accuracy and reliability of the rock abundance inversion results. In addition, the rock abundance data retrieved using this method can provide important data support for the study of lunar surface geological evolution, further promoting the implementation of lunar exploration missions, especially in areas such as lunar landing site selection. The technical means of this invention enable efficient and accurate acquisition of rock abundance distribution in lunar craters, contributing to a deeper understanding of the lunar surface and its geological evolution. Attached Figure Description

[0050] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. Wherein:

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

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

[0053] Figure 3 A schematic diagram illustrating the results of extracting rock information from optical images of meteorite craters and registering it with Diviner RA data, as provided in some embodiments of this application;

[0054] Figure 4This is a schematic diagram illustrating the process of resampling rock abundance from an optical image, according to some embodiments of this application.

[0055] Figure 5 This is a schematic diagram of the S1, S2, S3 and S4 parameters obtained by Stokes parameter decomposition of SAR images of a meteorite crater provided according to some embodiments of this application.

[0056] Figure 6 Scatter plot of RA based-NAC and Stokes parameters according to some embodiments of this application;

[0057] Figure 7 This is a schematic diagram of the correlation matrix obtained by performing correlation analysis between RA based-NAC and Stokes parameters according to some embodiments of this application;

[0058] Figure 8 This is a schematic diagram of high-resolution rock abundance results of two impact craters obtained by RA-SAR model inversion according to some embodiments of this application; Detailed Implementation

[0059] The present application will now be described in detail with reference to the accompanying drawings and embodiments. Various examples are provided by way of explanation and not by way of limitation. In fact, those skilled in the art will recognize that modifications and variations can be made to the present application without departing from the scope or spirit thereof. For example, a feature shown or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desirable that the present application encompass such modifications and variations that fall within the scope of the appended claims and their equivalents.

[0060] Exemplary methods

[0061] Figure 1 This is a flowchart illustrating a rock abundance inversion method based on optical images and synthetic aperture radar data, according to some embodiments of this application; Figure 1 As shown, this method for inverting rock abundance based on optical images and synthetic aperture radar data includes:

[0062] Step S101: Acquire Mini-RF SAR image, NAC optical image and Diviner RA image of the impact crater, and perform preprocessing operations on the acquired image data;

[0063] In this embodiment, Mini-RF SAR images, NAC optical images, and Diviner RA images of the crater are acquired, and a series of preprocessing operations are performed on the data. First, the Mini-RF SAR images and Diviner RA images are projected onto a unified lunar coordinate reference system to eliminate geometric distortions caused by different observation perspectives. Subsequently, the NAC optical images are mosaicked to generate a complete image of the target area by stitching together multiple optical images covering the study area. Next, geometric corrections were performed on the Mini-RF SAR image and the processed NAC optical image to ensure that both datasets accurately corresponded to the actual locations on the lunar surface. Subsequently, image registration was performed on the three datasets to achieve precise spatial alignment between the Mini-RF SAR data, the NAC optical image, and the Diviner RA image. After registration, irrelevant data outside the study area was removed using masking to ensure that subsequent processing was only applied to the target crater region. Finally, the Refined Lee filtering algorithm was applied to the SAR data to reduce noise while preserving important backscattering features. After this step, the Mini-RF SAR image, NAC optical image, and Diviner RA dataset suitable for subsequent analysis were obtained.

[0064] Step S102: Extract rock information from the optical image of the impact crater, and calculate the rock abundance distribution map of the impact crater based on the rock information obtained from optical statistics and in combination with the Diviner RA image.

[0065] While the Diviner instrument can derive rock abundance for lunar craters, its resolution of only 240 m / pixel is unsuitable for quantitative analysis. In contrast, NAC optical images, with their high resolution, can effectively capture rock distribution information around craters. Combining the two allows for the derivation of higher-resolution rock abundance data. First, rock information around craters is labeled and statistically analyzed using optical images. Then, rock coverage is calculated. To maintain resolution consistency with Mini-RFSAR data, the rock information statistically derived from the registered optical and Diviner RA images is resampled based on the SAR data resolution. Within each unit area, the corresponding SAR pixel resolution T is calculated. SAR Calculate rock abundance RA per unit area:

[0066]

[0067] Where S i Let S represent the surface area of ​​the i-th rock target. cell The sum of the total rock area within a unit region, where M is the number of rock targets within the unit region, RA cellThis 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 its resolution consistent with the Mini-RF SAR image, and finally a rock abundance distribution map RA basedNAC based on optical image is generated.

[0069] Step S103: Perform Stokes parameter decomposition on the Mini-RF SAR image to obtain S1, S2, S3 and S4, and then process the outliers respectively; where S1 and S2 are the total power and difference power of the horizontal and vertical components, respectively, and S3 and S4 are the two parts with opposite circular polarization.

[0070] In this embodiment of the application, the SAR data is first polarized decomposed to calculate four Stokes parameters, as shown below.

[0071]

[0072] Here, S1 represents the total power of the horizontal and vertical components, indicating the total backscattering intensity. It typically reflects the roughness and reflectivity of the target object. In rock abundance modeling, S1 is the parameter that most directly reflects surface characteristics, thus contributing the most to the model. S2 is the difference power between the horizontal and vertical components, which reveals the anisotropy and polarization characteristics of the target object, helping to distinguish between rocks and fine-grained weathering layers. S3 and S4 are two parts with opposite circular polarization. S3 is related to the geometry and orientation of the target object and can capture complex scattering characteristics for irregular rocks. S4 provides asymmetric information about surface scattering of the target object, but its contribution to rock abundance modeling is relatively small. Where E... H and E V denoted as horizontal polarization and vertical polarization, respectively; Re and Im represent the real part and imaginary part, respectively; and * denotes conjugation.

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

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

[0075] Define 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] Outlier removal: Replace outliers with the mean or median of their neighborhood to ensure data smoothness and consistency. The replacement formula is:

[0077]

[0078] Where S i The current pixel value. Let S′ be the set of its neighboring pixel values. 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: Based on the rock abundance distribution map of the impact crater calculated in step S102 above, draw a scatter plot between rock abundance and Stokes parameters, and analyze the correlation between rock abundance and Stokes parameters S1, S2, S3 and S4 respectively.

[0081] Plot scatter plots by pairing each Stokes parameter S1, S2, S3, and S4 with the RAbased-NAC value. The x-axis of each scatter plot represents the corresponding Stokes parameter value, and the y-axis represents the RAbased-NAC value. The specific scatter plot representation is as follows:

[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 Stokes parameters and rock abundance, thus revealing the impact of different Stokes parameters on RA. Since there is a certain linear relationship between rock abundance and backscattered signal, a linear function fitting method is used to quantify and evaluate the relationship when analyzing the correlation between the two.

[0087] Step S105: Fit the scatter plot to obtain the corresponding fitting function; calculate the Pearson correlation coefficient and plot the correlation matrix;

[0088] In this embodiment, linear fitting is performed on the plotted scatter plot to obtain fitting functions for RA based-NAC and Stokes parameters S1, S2, S3, and S4, respectively. The slope and intercept of the linear model are determined using the least squares method based on the linear fitting results, and the goodness of fit R is calculated. 2 The value is used to measure the interpretability of the fitted function for the data. The Pearson correlation coefficient r represents the strength and direction of the linear correlation between the variables. Next, the Pearson correlation coefficient r between RA based-NAC and each Stokes parameter is calculated using the following formula:

[0089]

[0090] Among them, X i and Y i These represent the RA-based-NAC value and the Stokes parameter value, respectively. and This represents the mean of the corresponding data, and n is the number of samples. Finally, the Pearson correlation coefficient results are presented in matrix form. A 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.

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

[0092] In this embodiment of the application, firstly, normalized weights are calculated based on the R values ​​of each Stokes parameter S1, S2, S3, and S4 in the correlation matrix. 2 The values ​​are then normalized according to the following formula to obtain the weighting coefficients 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 the weights ensures that the influence of all parameters is uniformly quantified, and their sum equals 1. Finally, the model is constructed. Based on the normalized weights and the actual values ​​of each Stokes parameter, a multi-parameter linear weighted model of rock abundance is built, as shown in the following formula:

[0095]

[0096] This model achieves high-precision inversion of rock abundance by comprehensively considering the influence of different parameters on rock abundance. The method employs a multi-parameter linear weighted regression model, which fully utilizes the information from each Stokes parameter, improving the model's explanatory power and fitting accuracy. Compared to single-parameter models, the multi-parameter weighted model can more comprehensively reflect the distribution pattern of rock abundance, providing solid support for subsequent high-resolution rock abundance inversion of lunar craters.

[0097] Step S107: Use the RA-SAR linear regression weighted model to invert the rock abundance of other impact craters to obtain high-resolution rock abundance data;

[0098] In this embodiment, firstly, Mini-RF SAR data of the target crater region is acquired. These images undergo a preprocessing process similar to step S101, including image registration, filtering and denoising, and outlier handling, to ensure the quality and accuracy of the input data. Then, Stokes parameters are calculated and extracted: the preprocessed SAR images are decomposed using Stokes parameters, and then an RA-SAR model is applied. Based on the RA-SAR linear regression weighted model established in step S106, the Stokes parameter values ​​of the target crater region are substituted into the model to calculate the rock abundance of each pixel. The rock abundance (RA) distribution of the region is obtained by combining the various Stokes parameters through model weighting. Finally, high-resolution rock abundance is generated. During the inversion process, the same spatial resolution as the Mini-RF SAR images is used to calculate the rock abundance data, ensuring the high resolution and accuracy of the output data. A high-resolution rock abundance map of the lunar crater is generated by estimating each pixel using a linear regression model, reflecting the rock distribution characteristics of the region. Through this series of steps, the RA-SAR model can accurately invert the rock abundance of the target area, providing strong support for future lunar surface characteristic analysis, in-depth research on impact craters, and related exploration missions.

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

[0100] Exemplary System

[0101] Figure 2 This is a schematic diagram of the structure of a rock abundance inversion system based on optical images and synthetic aperture radar data, according to some embodiments of this application; such as Figure 2 As shown, the rock abundance inversion system based on optical images and synthetic aperture radar (SAR) data includes: a data acquisition and preprocessing unit configured to receive and process data from Mini-RF SAR, Diviner RA, and NAC optical images. This unit first performs projection operations on the SAR and Diviner RA data and mosaics the optical images, then performs registration operations on the three types of data to ensure spatial consistency. Finally, the SAR images are filtered and denoised to remove random noise and improve data quality; and a rock abundance extraction and calculation unit configured to calculate high-resolution rock abundance in the crater region based on the rock information extracted from the optical images and Diviner RA data. This unit analyzes rock information in optical images and combines it with preprocessed Diviner RA data to generate rock abundance (RA) data with the same resolution as Mini-RF SAR data. Based on this data, regional calculations are performed to obtain a higher-resolution rock abundance distribution for the target area. The Stokes parameter decomposition unit is configured to perform Stokes parameter decomposition on the processed SAR images, generating scattering feature images for each polarization state (S1, S2, S3, and S4). This unit extracts reflection features under different polarization modes from the processed backscattering information, 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. This model quantifies the contribution of each Stokes parameter to rock abundance, ultimately generating an RA-SAR model. The high-resolution rock abundance inversion unit is configured to perform rock abundance inversion on other impact 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, which is the high-resolution rock abundance data of the impact crater.

[0102] The rock abundance inversion system based on optical images and synthetic aperture radar data provided in this application embodiment can realize 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 effect, which will not be described in detail here.

[0103] Figure 3(a) is a schematic diagram of the results after extracting rocks around the impact crater using NAC optical images; Figure 3 (b) is a magnified view of the extracted rock section, corresponding to... Figure 3 The part within the red rectangle in (a); Figure 3 (c) shows the rock information extracted from the optical image and the results after registration with the Diviner RA image; Figure 3 (d) Corresponding to Figure 3 (c) is an enlarged view 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 from 240 m / pixel to 60 m / pixel and 15 m / pixel. Figure 5 The images show the results of S1, S2, S3, and S4 after Stokes parameter decomposition of the SAR image of the impact crater. Figure 6 A scatter plot of rock abundance and Stokes parameters is shown, along with the results after function fitting. Figure 7 The correlation matrix between rock abundance and Stokes parameters is calculated based on the results of scatter plot fitting. S1 has the highest correlation with rock abundance, while S4 has the lowest correlation. Using this correlation matrix, a RA-SAR linear weighted regression model can be constructed to invert high-resolution rock abundance distribution maps of other impact craters. Figure 8 The image shows the results of rock abundance inversion for two impact craters using the RA-SAR model, with a resolution of 15 m / pixel, which is much higher than the 240 m / pixel resolution of Diviner RA.

[0104] Exemplary device

[0105] This application provides an electronic device, including a storage device and 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, it implements the rock abundance inversion method based on optical images and synthetic aperture radar data as described above. Since the steps of the rock abundance inversion method based on optical images and synthetic aperture radar data have been described in detail in the specific implementation examples, they will not be repeated here.

[0106] A computer-readable storage medium stores a computer program thereon. When executed by a processor, the computer program causes the device containing the computer-readable storage medium to perform 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 the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), random access memory, and other memories.

[0107] If the modules / units integrated in the electronic device described in this application are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

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

[0109] The computer-readable storage medium stores computer-readable instructions, which 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 as described in any of the above embodiments.

[0110] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

[0113] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0114] Note that the above description is merely a preferred embodiment and application of the technical principles of the present invention. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the specific embodiments described herein, and may include many other effective embodiments without departing from the concept of the present invention. 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 mutually. Each embodiment focuses on describing 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 description of the method embodiments.

Claims

1. A method for inverting rock abundance based on optical images and synthetic aperture radar data, characterized in that, Includes the following steps: Step S101: Acquire Mini-RF SAR image, NAC optical image and Diviner RA image of the impact crater, and perform preprocessing operations on the acquired image data; Step S102: Extract rock information from the optical image of the impact crater, and calculate the rock abundance distribution map RA based-NAC of the impact crater based on the rock information obtained by optical statistics and in combination with the Diviner RA image; Step S103: Perform Stokes parameter decomposition on the Mini-RF SAR image to obtain S1, S2, S3 and S4, and then process the outliers respectively; where S1 and S2 are the total power and difference power of the horizontal and vertical components, respectively, and S3 and S4 are the two parts with opposite circular polarization. Step S104: Based on the rock abundance distribution map of the impact crater calculated in step S102 above, draw a scatter plot between rock abundance and Stokes parameters, and analyze the correlation between rock abundance and Stokes parameters S1, S2, S3 and S4 respectively. Step S105: Fit the scatter plot to obtain the corresponding fitting function; calculate the Pearson correlation coefficient and plot the correlation matrix; Step S106: Based on the results of the above correlation matrix analysis, establish a RA-SAR linear regression weighted model; Step S107: Use the RA-SAR linear regression weighted model to invert the rock abundance of other impact 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: First, the Mini-RF SAR and Diviner RA images are projected onto a unified lunar coordinate reference system to eliminate geometric distortions caused by different observation perspectives. Subsequently, the NAC optical images were mosaicked to generate a complete image of the target area by stitching together multiple optical images covering the study area. Next, geometric corrections were performed on the Mini-RF SAR image and the processed NAC optical image to ensure that the two types of data could accurately correspond to the actual locations on the lunar surface. Subsequently, image registration was performed on the three types of data to achieve precise spatial alignment between Mini-RF SAR data and NAC optical images and DivinerRA images; After registration, irrelevant data outside the study area is removed through masking to ensure that subsequent processing is only applied to the target impact crater area; Finally, the Refined Lee filtering algorithm is applied to the SAR data to reduce noise while preserving 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, Step S102 specifically includes the following steps: First, rock information around the impact crater was labeled and statistically analyzed using optical images. Then, rock coverage was calculated. To ensure consistency with the resolution of the Mini-RF SAR data, the rock information statistically analyzed in the registered optical and Diviner RA images was resampled based on the SAR data resolution. Within each unit area, the corresponding SAR pixel resolution r was calculated. SAR Calculate rock abundance RA per unit area: Where S i Let S represent the surface area of ​​the i-th rock target. cell The sum of the total rock area within a unit region, where M is the number of rock targets within the unit region, RA cell This 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 its resolution consistent with the Mini-RF SAR image, and finally a rock abundance distribution map RA based-NAC based on 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, as shown below; Among them, E HL and E VL These represent horizontal and vertical polarization, respectively; Re and Im represent the real and imaginary parts, respectively; and * represents conjugation. Then, outlier detection and processing are performed, calculating the pixel value distribution for each Stokes parameter and removing outliers; the specific process is as follows: Calculate statistical metrics: For each Stokes parameter S1, S2, S3, and S4, calculate the mean μ and standard deviation σ of its pixel values; 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; Outlier removal: Replace outliers with the mean or median of their neighborhood to ensure data smoothness and consistency; the replacement formula is: Where S i The current pixel value. S is the set of neighboring pixel values. i ′ 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: Plot scatter plots by pairing each Stokes parameter S1, S2, S3, and S4 with the RA-based-NAC value. The x-axis of each scatter plot represents the corresponding Stokes parameter value, and the y-axis represents the RA-based-NAC value. The specific scatter plot representation is as follows: 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 Stokes parameters and rock abundance, thus revealing the impact of different Stokes parameters on RA. Since there is a certain linear relationship between rock abundance and backscattered signal, a linear function fitting method is used to quantify and evaluate the 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 plotted scatter plot to obtain the fitting functions of RA based-NAC and Stokes parameters S1, S2, S3 and S4 respectively. The slope and intercept of the linear model were determined using the least squares method, and the goodness of fit R was calculated. 2 The value is used to measure the interpretability of the fitted function for 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 between RA-based-NAC and each Stokes parameter is calculated using the following formula: Among them, X i and Y i These represent the RA-based-NAC value and the Stokes parameter value, respectively. and This 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, and the correlation matrix between RA based-NAC and Stokes parameters is constructed. The matrix is ​​then 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, normalized weights are calculated based on the goodness of fit R of each Stokes parameter S1, S2, S3, and S4 in the correlation matrix. 2 value, Then, normalization is performed according to the following formula to obtain the weighting coefficients of each parameter. These weights reflect the relative contribution of each Stokes parameter to rock abundance RA: Similar to the calculation, the normalization of the weights ensures that the influence of all parameters is uniformly quantified, and their sum equals 1; Finally, a model is constructed based on the normalized weights and the actual values ​​of each Stokes parameter, as shown in the following formula:

8. A rock abundance inversion system based on optical images and synthetic aperture radar data, characterized in that: include: The 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 performs projection operations on the SAR data and Diviner RA data, and mosaics the optical images. Then, it performs registration operations on the three types of data to ensure spatial consistency. Finally, it performs filtering and denoising processing on the SAR images to remove random noise from the images and improve data quality. The rock abundance extraction and calculation unit is configured to calculate the high-resolution rock abundance of the crater region based on rock information extracted from optical images and Diviner RA data. This unit analyzes rock information in optical images and combines it with preprocessed Diviner RA data to generate rock abundance RA data with the same resolution as Mini-RF SAR data. Based on this data, regional calculations are performed to obtain a higher resolution rock abundance distribution in the target area. The Stokes parameter decomposition unit is configured to perform Stokes parameter decomposition on the processed SAR image to generate scattering feature images for each polarization state. This unit processes the backscattering information to extract reflection features under different polarization modes, 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. This model quantifies the contribution of each Stokes parameter to rock abundance, and finally generates the RA-SAR model. A high-resolution rock abundance inversion unit, configured to perform rock abundance inversion on other impact 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: The memory, the processor, and the program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the rock abundance inversion method based on optical images and synthetic aperture radar data as described in any one of claims 1-7.

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