SAR image resampling method and device based on sea surface reanalysis wind field data

Abstract

The application discloses a SAR image rescaling method and device based on sea surface reanalysis wind field data, and relates to the technical field of image processing. The method comprises the following steps: cutting a SAR remote sensing image to obtain a plurality of SAR sub-images; performing consistency inspection on each sub-image to remove unqualified sub-images; performing interpolation processing on reanalysis wind field data according to the central longitude and latitude of the SAR sub-images to obtain wind field data at the central position of the sub-images; calculating an incident angle at the central position of the SAR sub-image according to a satellite shooting position and the position of the interpolated SAR sub-image; calculating an average backscattering coefficient of the SAR sub-image; calculating a correction coefficient according to the average backscattering coefficient, the incident angle and the wind field data; and correcting the backscattering coefficient in each SAR sub-image according to the correction coefficient to obtain original SAR correction data.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a SAR image recalibration method and apparatus based on sea surface reanalysis wind field data. Background Technology

[0002] With the rapid development of spaceborne SAR (Synthetic Aperture Radar) technology, the application fields of SAR images are becoming increasingly widespread.

[0003] Traditional qualitative remote sensing methods are no longer sufficient to meet the growing application demands, and the need for quantitative remote sensing inversion of typical targets and land and sea surface environments is gradually increasing. Therefore, radiometric calibration of SAR images is particularly important. Radiometric calibration is a key step in the quantitative processing of SAR image data, aiming to obtain the RCS (Radar Cross-Section) or radar backscattering coefficient of targets from SAR image data, and to establish a mapping relationship between SAR image pixel power and the RCS of ground targets or the radar backscattering coefficient of land and sea surface environments, so as to ensure that satellite observation data can accurately reflect actual surface physical quantities.

[0004] Despite the progress made in existing SAR radiometric calibration techniques, some shortcomings remain. Particularly for micro-sized commercial SAR satellites, due to their light weight, low cost, and rapid development cycle, there are certain gaps in quantitative indicators compared to traditional large SAR satellites. Sometimes, the radiometric calibration performance of SAR images may exhibit significant deviations. Furthermore, existing external calibration methods primarily rely on ground-based calibration sources, such as corner reflectors, but these sources are difficult to deploy in vast areas such as the ocean, limiting the widespread application of SAR radiometric calibration. Simultaneously, traditional calibration methods often struggle to accurately describe the impact of sea surface conditions on the backscattering coefficient of SAR images when dealing with complex marine environments, thus affecting calibration accuracy. Summary of the Invention

[0005] The purpose of this application is to provide a SAR image recalibration method and apparatus based on sea surface reanalysis wind field data.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a SAR image recalibration method based on sea surface reanalysis wind field data, including: The SAR remote sensing image is cropped to obtain several SAR sub-images; Perform a consistency check on each sub-image and remove any sub-images that do not meet the requirements; Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data is interpolated to obtain the wind field data at the center of the sub-image. Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; Calculate the average backscattering coefficient of the SAR sub-image; The correction factor is calculated based on the average backscattering coefficient, the incident angle, and the wind field data; The backscattering coefficients in each SAR sub-image are corrected according to the correction coefficients to obtain the original SAR corrected data.

[0007] Optionally, the step of cropping the SAR remote sensing image to obtain several SAR sub-images includes: The SAR remote sensing images are cropped according to the highest spatial resolution of the reanalysis wind field data to obtain several SAR sub-images; The images are numbered in a certain order, and each sub-image has its own number, which represents the SAR sub-image corresponding to the i-th row and j-th column.

[0008] Optionally, the step of performing a consistency check on each sub-image and removing unqualified sub-images includes: Calculate the pixel size of the sub-image; Using target recognition algorithms, the size of sea surface targets such as ships, land, and oil spills is identified, and the area of ​​each sub-image is obtained; Calculate the proportion of sea surface targets in the sub-image; Compare the consistency test threshold with the proportion of the sea surface target in the sub-image, and remove sub-images that do not meet the consistency test.

[0009] Optionally, the step of interpolating the reanalysis wind field data based on the center latitude and longitude of the SAR sub-image to obtain the wind field data at the center of the sub-image includes: Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data will be interpolated; Obtain wind speed and wind direction data at the center of the sub-image.

[0010] Optionally, the step of calculating the incident angle at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image includes: Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; Alternatively, the incident angle at the center of the SAR sub-image can be obtained by interpolation based on the incident angle data recorded in the SAR data.

[0011] Optionally, the step of calculating the average backscattering coefficient of the SAR sub-image includes: The average backscattering coefficient of the SAR sub-image is obtained by summing the values ​​of each pixel in the SAR sub-image and dividing by the number of pixels.

[0012] Optionally, the step of calculating the correction coefficient based on the average backscattering coefficient, the incident angle, and the wind field data includes: Based on the average backscattering coefficient, the incident angle, and the wind field data, a correction coefficient is calculated using a function specified by a preset standard.

[0013] Secondly, this application provides a SAR image recalibration device based on sea surface reanalysis wind field data, comprising: The acquisition module is used to crop SAR remote sensing images and acquire several SAR sub-images; The processing module is used to perform consistency checks on each sub-image and remove unqualified sub-images; Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data is interpolated to obtain the wind field data at the center of the sub-image. Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; Calculate the average backscattering coefficient of the SAR sub-image; The correction factor is calculated based on the average backscattering coefficient, the incident angle, and the wind field data; The output module is used to correct the backscattering coefficients in each SAR sub-image according to the correction coefficients, and obtain the original SAR corrected data.

[0014] Optionally, the acquisition module is further configured to: The SAR remote sensing images are cropped according to the highest spatial resolution of the reanalysis wind field data to obtain several SAR sub-images; The images are numbered in a certain order, and each sub-image has its own number, which represents the SAR sub-image corresponding to the i-th row and j-th column.

[0015] Optionally, the processing module is further configured to: Calculate the pixel size of the sub-image; Using target recognition algorithms, the size of sea surface targets such as ships, land, and oil spills is identified, and the area of ​​each sub-image is obtained; Calculate the proportion of sea surface targets in the sub-image; Compare the consistency test threshold with the proportion of the sea surface target in the sub-image, and remove sub-images that do not meet the consistency test.

[0016] Optionally, the processing module is further configured to: Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data will be interpolated; Obtain wind speed and wind direction data at the center of the sub-image.

[0017] Optionally, the processing module is further configured to: Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; Alternatively, the incident angle at the center of the SAR sub-image can be obtained by interpolation based on the incident angle data recorded in the SAR data.

[0018] Optionally, the processing module is further configured to: The average backscattering coefficient of the SAR sub-image is obtained by summing the values ​​of each pixel in the SAR sub-image and dividing by the number of pixels.

[0019] Optionally, the processing module is further configured to: Based on the average backscattering coefficient, the incident angle, and the wind field data, a correction coefficient is calculated using a function specified by a preset standard.

[0020] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the SAR image recalibration method based on sea surface reanalysis wind field data as described above.

[0021] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the SAR image recalibration method based on sea surface reanalysis wind field data described above.

[0022] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the SAR image recalibration method based on sea surface reanalysis wind field data described above.

[0023] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a SAR image recalibration method and apparatus based on sea surface reanalysis wind field data. By cropping the SAR remote sensing image into multiple sub-images and performing rigorous consistency checks, the quality of the sub-images in subsequent processing is ensured, avoiding the influence of substandard images on the final result. Accurate interpolation of the wind field information at the center of the sub-images using reanalysis wind field data, and accurate calculation of the incident angle based on the satellite capture location and the sub-image location, provide key parameters for subsequent correction coefficient calculations. By calculating the average backscattering coefficient of each qualified sub-image and combining it with wind field data and incident angle, correction coefficients can be derived. Finally, the backscattering coefficient in the original SAR image is corrected based on these correction coefficients, thereby obtaining a quantitative SAR image and improving the radiometric calibration accuracy of the SAR image. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a SAR image recalibration method based on sea surface reanalysis wind field data, provided as an embodiment of this application; Figure 2 One of the schematic diagrams illustrating the principle of a SAR image recalibration method based on sea surface reanalysis wind field data provided in an embodiment of this application; Figure 3 A schematic diagram of the principle of a SAR image recalibration method based on sea surface reanalysis wind field data provided in an embodiment of this application (II). Figure 4 The third schematic diagram illustrating the principle of a SAR image recalibration method based on sea surface reanalysis wind field data provided in an embodiment of this application; Figure 5 A schematic diagram of the functional modules of a SAR image recalibration device based on sea surface reanalysis wind field data provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] like Figure 1 As shown, some embodiments of this application provide a SAR image recalibration method based on sea surface reanalysis wind field data. In these embodiments, the method includes steps 101 to 107. Wherein: Step 101: Crop the SAR remote sensing image to obtain several SAR sub-images.

[0028] In this embodiment, SAR (Synthetic Aperture Radar) remote sensing images are images of the Earth's surface acquired by a synthetic aperture radar system. SAR systems generate images by transmitting microwave signals and receiving their reflected signals, providing all-weather, all-day imaging capabilities. Cropping refers to dividing a complete SAR remote sensing image into multiple smaller image patches according to certain rules; these patches are called SAR sub-images. The purpose of cropping is to facilitate subsequent processing and analysis.

[0029] Reference Figure 2 The system first reads the complete SAR remote sensing image, and then crops the SAR image according to the highest spatial resolution of the reanalysis wind field data (e.g., the spatial resolution of ERA5 reanalysis wind field data is 0.25°, approximately 25 km). Each cropped SAR sub-image is 25 km × 25 km in size. The system numbers each sub-image in a left-to-right, top-to-bottom order; for example, the sub-image in the i-th row and j-th column is numbered s_ij. The cropped SAR sub-images will be used for subsequent consistency checks and wind field data interpolation processing.

[0030] Step 102: Perform a consistency check on each sub-image and remove unqualified sub-images.

[0031] In this embodiment, consistency testing refers to a quality assessment of SAR sub-images to determine whether they meet preset quality standards. The purpose of consistency testing is to ensure that the sea surface area accounts for a high proportion in the sub-images, avoiding the inclusion of too many non-sea surface targets (such as ships, land, oil spills, etc.) that could affect subsequent wind field data inversion and radiometric calibration. Unqualified sub-images are those that fail to pass a preset threshold after consistency testing. These sub-images typically contain too many non-sea surface targets and are unsuitable for subsequent wind field data inversion and radiometric calibration.

[0032] The system performs a consistency check on each SAR sub-image. First, the system reads the number of pixel rows and columns of the sub-image and calculates the total pixel size. Then, the system uses a target recognition algorithm to detect non-sea surface targets such as ships, land, and oil spills in the sub-image and calculates the pixel percentage of these targets in the sub-image. If the percentage of non-sea surface targets is less than a preset threshold (e.g., 5%), the sub-image passes the consistency check; otherwise, the system marks the sub-image as unqualified and removes it. Sub-images that pass the consistency check will be used for subsequent wind field data interpolation and radiometric calibration.

[0033] Step 103: Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data is interpolated to obtain the wind field data at the center of the sub-image.

[0034] In this embodiment, the reanalysis wind field data is a wind field dataset generated by fusing multiple data sources, such as numerical model forecasts, marine buoy observations, and satellite remote sensing observations, using data assimilation techniques. Reanalysis wind field data provides long-term wind speed and direction information with high spatiotemporal resolution. Interpolation processing refers to estimating the values ​​of unknown data points based on known data point values ​​using mathematical methods (such as linear interpolation, nearest neighbor interpolation, polynomial interpolation, etc.). In this step, the purpose of interpolation processing is to obtain wind speed and direction data at the center of the SAR sub-image based on the spatial distribution of the reanalysis wind field data.

[0035] The system interpolates the reanalysis wind field data based on the center latitude and longitude coordinates of the SAR sub-images that have passed the consistency check. The reanalysis wind field data is typically distributed in a grid format. The system calculates the wind speed and direction data at the center of the SAR sub-image using an interpolation algorithm (such as linear interpolation). After interpolation, the system obtains the wind speed data U_ij and wind direction data φ_ij at the center of each SAR sub-image. These data will be used for subsequent incident angle calculations and backscattering coefficient corrections.

[0036] Step 104: Calculate the incident angle at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image.

[0037] In this embodiment, the satellite image capture location refers to the spatial position of the SAR satellite when capturing SAR images, typically including information such as the satellite's orbital altitude and image capture angle. The satellite image capture location is a crucial parameter for calculating the incident angle of the SAR image. The incident angle is the angle between the microwave signal emitted by the SAR satellite and the normal to the ground target surface. The magnitude of the incident angle affects the backscattering intensity of the SAR image and is an important parameter in SAR radiometric calibration.

[0038] The system calculates the incident angle θ_ij at the center of each SAR sub-image based on the SAR satellite's capture location and the center location of the interpolated SAR sub-image. The calculation of the incident angle is usually based on geometric relationships; the system uses trigonometric functions to calculate the incident angle using the satellite's orbital parameters and the geographic coordinates of the sub-image.

[0039] Step 105: Calculate the average backscattering coefficient of the SAR sub-image.

[0040] In this embodiment, the backscattering coefficient refers to the ratio of the intensity of the target reflected signal received by the SAR system to the intensity of the transmitted signal, usually represented by σ. The backscattering coefficient reflects the scattering characteristics of the target surface and is an important parameter for quantitative processing of SAR images. The average backscattering coefficient is the average value of the backscattering coefficients of all pixels in the SAR sub-image. The purpose of calculating the average backscattering coefficient is to simplify the subsequent calculation of correction coefficients.

[0041] The system sums the backscattering coefficients of each pixel in the SAR sub-image that has passed the consistency test, and then divides the sum by the total number of pixels in the sub-image to obtain the average backscattering coefficient σ_ij of the sub-image.

[0042] Step 106: Calculate the correction coefficient based on the average backscattering coefficient, the incident angle, and the wind field data.

[0043] In this embodiment, the correction factor refers to the parameter used to adjust the backscattering coefficient of the SAR sub-image. The correction factor is calculated based on the Geophysical Model Function (GMF), and is determined by comparing the difference between the actual backscattering coefficient and the theoretical backscattering coefficient. The Geophysical Model Function is an empirical or theoretical function that describes the relationship between the normalized backscattering coefficient (NRCS) of the sea surface and parameters such as wind speed, incident angle, and wind direction. Commonly used GMFs include CMOD5.N and CMOD7.

[0044] The system calculates the correction coefficient p_ij using geophysical model functions (such as CMOD5.N) based on the average backscattering coefficient σ_ij, the incident angle θ_ij, and wind field data (wind speed U_ij and wind direction φ_ij).

[0045] Step 107: Correct the backscattering coefficients in each SAR sub-image according to the correction coefficients to obtain the original SAR corrected data.

[0046] In this embodiment, the original SAR corrected data refers to the SAR sub-image data adjusted by correction coefficients. The corrected data can more accurately reflect the backscattering characteristics of the sea surface and improve the quantitative application capability of SAR images.

[0047] The system corrects the backscattering coefficient σ_mn in each SAR sub-image according to the correction coefficient p_ij, and obtains the corrected backscattering coefficient σ'_mn.

[0048] This application's embodiments ensure the quality of sub-images in subsequent processing by cropping SAR remote sensing images into multiple sub-images and performing rigorous consistency checks, thus avoiding the impact of substandard images on the final result. Accurate interpolation of wind field information at the center of each sub-image is performed using reanalysis wind field data, and the incident angle is accurately calculated based on the satellite capture location and the sub-image location, providing key parameters for subsequent correction coefficient calculations. By calculating the average backscattering coefficient of each qualified sub-image and combining it with wind field data and incident angles, correction coefficients can be derived. Finally, the backscattering coefficients in the original SAR image are corrected based on these correction coefficients, thereby obtaining a quantitative SAR image and improving the radiometric calibration accuracy of the SAR image.

[0049] Optionally, step 101 includes: Step 1011: The SAR remote sensing image is cropped according to the size of the highest spatial resolution of the reanalysis wind field data to obtain several SAR sub-images.

[0050] In this embodiment, the reanalysis wind field data is a dataset that integrates data from multiple sources and types (such as numerical model forecasts, marine buoy observations, scatterometer / radiometer satellite remote sensing observations, etc.). Historical data is reconstructed through data assimilation techniques, providing sea surface wind field information with high spatial and temporal resolution. The highest spatial resolution refers to the fineness of the spatial information in the reanalysis wind field data, i.e., the smallest resolvable geographic unit. SAR sub-images are small-area images cropped from the original SAR images that match the spatial resolution of the reanalysis wind field data.

[0051] The system first identifies the highest spatial resolution of the reanalysis wind field data. For example, using ERA5 reanalysis wind field data, its spatial resolution is 0.25°, roughly equivalent to a geographical area of ​​25 km × 25 km. Then, the system crops each input SAR image to ensure that the spatial extent of each cropped SAR sub-image matches the highest spatial resolution of the reanalysis wind field data; that is, each sub-image is 25 km × 25 km in size. In this way, the original large-area SAR image is segmented into multiple small SAR sub-images with consistent spatial resolution, providing a foundation for subsequent processing and analysis.

[0052] Step 1012: Number the images in a certain order. Each sub-image has its own number, which represents the SAR sub-image corresponding to the i-th row and j-th column.

[0053] In this embodiment, the system numbers the cropped SAR sub-images according to a predetermined order (e.g., from left to right, from top to bottom). Each sub-image has a unique number, consisting of row number "i" and column number "j", which together indicate the sub-image's position in the two-dimensional image array. For example, the number "sij" represents the SAR sub-image in row i and column j. This numbering method helps the system quickly locate and access specific SAR sub-images in subsequent processing, improving processing efficiency and accuracy.

[0054] Specifically, each SAR remote sensing image is cropped to obtain several SAR sub-images, as shown in Figure 2; specifically, each image is cropped according to the highest spatial resolution of the reanalysis wind field data. The size of the data is cropped (taking ERA5 reanalysis wind field data as an example, its spatial resolution is 0.25°, which is about 25km, so the cropped size is 25km×25km) to obtain several SAR sub-images; the images are numbered in a certain order, for example, the sub-images are numbered from left to right and from top to bottom, ij represents the SAR sub-image corresponding to the i-th row and j-th column, and each sub-image has its own number sij, which represents the SAR sub-image corresponding to the i-th row and j-th column; Optionally, step 102 includes: Step 1021: Calculate the pixel size of the sub-image.

[0055] In this embodiment, the system first reads the number of rows and columns of pixels in any sub-image (e.g., the first sub-image s11). Then, based on the number of rows and columns, the system calculates the pixel size Pix of the sub-image using a formula. This formula may be the number of rows multiplied by the number of columns, i.e., Pix = Row * Col. After calculating the pixel size, the system knows the specific dimensions and number of pixels contained in each sub-image, providing basic data for subsequent processing.

[0056] Step 1022: Using a target recognition algorithm, identify the size of sea surface targets such as ships, land, and oil spills, and obtain the area of ​​each sub-image.

[0057] In this embodiment, the target recognition algorithm is an algorithm capable of automatically identifying and locating specific targets in an image. Here, it is used to identify sea surface targets, such as ships, land, and oil spills. The area of ​​each sub-image refers to the number of pixels occupied by these targets in the sub-image, reflecting the size of the targets.

[0058] The system uses a target recognition algorithm to process each sub-image, automatically identifying sea surface targets and calculating the number of pixels these targets occupy in the sub-image, i.e., the pixel size pij of the target. This step is the basis for subsequent calculations of the proportion of sea surface targets in the sub-image.

[0059] Step 1023: Calculate the proportion of sea surface targets in the sub-image.

[0060] In the embodiments of this application, the proportion of sea surface targets in a sub-image refers to the ratio of the number of pixels occupied by sea surface targets to the total number of pixels in the sub-image, which reflects the relative size of sea surface targets in the sub-image.

[0061] The system calculates the proportion of the sea surface target in the sub-image based on the target's pixel size pij calculated in step 1022 and the sub-image pixel size Pix calculated in step 1021. This formula may be the number of pixels occupied by the target divided by the total number of pixels in the sub-image, i.e., proportion = pij / Pix. After calculating the proportion, the system knows the relative size of the sea surface target in each sub-image.

[0062] Step 1024: Compare the consistency test threshold with the proportion of the sea surface target in the sub-image, and remove the sub-images that do not meet the consistency test.

[0063] In this embodiment, the consistency check threshold is a preset value used to determine whether a sub-image meets the consistency requirements. The proportion of sea surface targets in the sub-image refers to the proportion calculated in step 1023. Removing sub-images that do not meet the consistency check means excluding those sub-images whose proportion exceeds the threshold from subsequent processing.

[0064] The system compares the proportion of sea surface targets in each sub-image with a preset consistency check threshold. If the proportion in a sub-image exceeds the threshold, it indicates that the proportion of sea surface targets in that sub-image is too large, which may affect the uniformity and quality of the image. Therefore, the system excludes that sub-image from subsequent processing. This step is crucial to ensuring the accuracy and reliability of subsequent processing results. By removing sub-images that do not meet the consistency requirements, the system can reduce errors and interference, improving the accuracy and reliability of the final processing results.

[0065] Specifically, calculate the pixel size of the sub-image; read the number of rows (Row) and columns (Col) of any sub-image (such as the first sub-image s11), and calculate the pixel size (Pix) of the sub-image, as shown in formula (1) below: (1) Using target recognition algorithms, the size of sea surface targets such as ships, land, and oil spills is identified, and each sub-image is obtained; Calculate the proportion of sea surface targets in the sub-image; using the target pixel size pij obtained in step 022 and the sub-image pixel size Pix calculated in step 021, calculate the proportion of sea surface targets in the sub-image. Formula (2) is as follows: (2) Comparison Consistency Test (threshold, such as) (This can also be adjusted according to the actual situation of the sea area). Step 023: The proportion of sea surface targets in the sub-image. The size of the image is used to remove sub-images that do not meet the consistency test. Given the sub-image Sij, obtain the sub-image Sij that satisfies the consistency test; Optionally, step 103 includes: Step 1031: Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data is interpolated.

[0066] In this embodiment, the system interpolates the reanalysis wind field data based on the center latitude and longitude coordinates of each SAR sub-image. This process involves reading the reanalysis wind field dataset, which contains wind field information over a wide geographical area. Then, the system estimates the wind field data at the center location of the SAR sub-image using interpolation algorithms (such as linear interpolation, nearest neighbor interpolation, polynomial interpolation, etc.). The result of the interpolation is a wind field data value corresponding to the center location of the SAR sub-image, which provides the necessary input for subsequent steps.

[0067] Step 1032: Obtain wind speed and wind direction data at the center of the sub-image.

[0068] In this embodiment, after interpolation, the system extracts wind speed and wind direction data corresponding to the center position of the SAR sub-image from the interpolation result. These data are estimated using an interpolation algorithm based on known data points in the reanalysis wind field dataset. The wind speed data provides information about wind field intensity, while the wind direction data provides information about wind field direction. This information is crucial for calculating correction coefficients and recalibrating the SAR image in subsequent steps. The system uses this wind speed and wind direction data as key inputs for further processing and analysis.

[0069] Specifically, refer to Figure 3Based on the center latitude and longitude (xi, yi) of the acquired SAR sub-image, the reanalysis wind field data is interpolated (interpolation algorithms: linear interpolation, nearest neighbor interpolation, polynomial interpolation, etc.), as shown in Figure 3. In Figure 3, uniformly distributed gray dots represent the reanalysis wind field data, and black dots represent the location of the center point of the SAR sub-image Sij (satisfying the consistency check). Through interpolation, the reanalysis wind field data corresponding to the center position (xi, yi) of the sub-image is obtained. The wind field data includes wind speed data. Wind direction data , where i and j both represent the indices of the SAR sub-image.

[0070] Optionally, step 104 includes: calculating the incident angle at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image, or obtaining the incident angle at the center of the SAR sub-image through interpolation based on the incident angle data recorded in the SAR data.

[0071] In this embodiment, the system first acquires the precise location information of the satellite image and the latitude and longitude of the center point of the processed SAR sub-image. Then, using this information, combined with the shape and size of the Earth, the system calculates the incident angle when the radar beam reaches the center of the SAR sub-image. This process may involve complex geometric calculations and the application of Earth models to ensure the accuracy of the incident angle calculation.

[0072] Alternatively, in some cases, SAR image data may already contain information about the angle of incidence. In this case, the system can directly read this data and apply an interpolation algorithm to estimate the angle of incidence at the center of each SAR sub-image. The choice of interpolation algorithm depends on the data distribution and accuracy requirements; common interpolation methods include linear interpolation, nearest neighbor interpolation, and polynomial interpolation. Through interpolation, the system can generate an angle of incidence matrix corresponding to the SAR sub-image for subsequent radiometric calibration and data analysis.

[0073] Specifically, calculate the incident angle of the SAR sub-image; based on the satellite image location. And the position of the interpolated SAR sub-image Sij, calculate the incident angle at the center position of the SAR sub-image Sij (satisfying the consistency test). (i,j), where i and j represent indices, and the calculation formula is shown in the figure; (3) Alternatively, the incident angle at the center of the SAR sub-image Sij (which satisfies the consistency check) can be obtained through interpolation based on the incident angle data recorded in the SAR data. (i,j); Optionally, step 105 includes: summing the values ​​of each pixel in the SAR sub-image and dividing by the number of pixels to obtain the average backscattering coefficient of the SAR sub-image.

[0074] In this embodiment, the average backscattering coefficient of the SAR sub-image is calculated; the average backscattering coefficient of the SAR sub-image Sij (which satisfies the consistency test) is obtained by summing the values ​​of each pixel in the SAR sub-image Sij (which satisfies the consistency test) and dividing by the number of pixels. , where i and j represent indices.

[0075] Optionally, step 106 includes: calculating a correction coefficient using a function specified by a preset standard based on the average backscattering coefficient, the incident angle, and the wind field data.

[0076] In this embodiment, the system first obtains the average backscattering coefficient of the SAR sub-images after consistency verification. This step is accomplished by summing all pixel values ​​in the sub-image and then dividing by the total number of pixels. The resulting average backscattering coefficient represents the average value of the sea surface radar backscattering characteristics of the sub-image region. Next, the system calculates the incident angle at the center of each sub-image based on the satellite capture location and the interpolated SAR sub-image location. The incident angle is the angle between the radar beam and the sea surface normal, which is unique for each sub-image and affects the way the radar wave interacts with the sea surface. Simultaneously, the system also obtains wind field data corresponding to the center location of each SAR sub-image, including wind speed and direction information. This data describes the dynamic state of the sea surface at that location and has a significant impact on sea surface radar backscattering. Finally, the system uses a function defined by a preset standard, taking the average backscattering coefficient, incident angle, and wind field data as input, to calculate correction coefficients. This function, based on theoretical or empirical relationships such as the Geophysical Model Function (GMF), converts the input data into correction coefficients through complex mathematical operations. The correction coefficients are used in subsequent steps to correct the original SAR data to obtain a more accurate quantitative SAR image. This process is automated and requires no manual intervention.

[0077] Specifically, the correction coefficient p is calculated based on the average scattering coefficient of the acquired SAR sub-image Sij (which satisfies the consistency check). Step 04 calculates the incident angle at the center of the SAR sub-image Sij (which satisfies the consistency check). (i,j), wind field data (wind speed data) obtained in Step 03 Wind direction data ), calculate the correction coefficient p(i,j), where i and j represent indices; formula (4) is as follows. (4) in This represents the expected backscattering coefficient of the SAR sub-image Sij (satisfied with the consistency test) in the i-th row and j-th column, calculated using the CMOD function. The calculation formula (5) is as follows: (5) CMOD5.N represents a wind field model. The difference between wind direction and SAR line of sight can be expressed as formula (6): (6) in It is a SAR image system parameter, representing the line-of-sight data captured by the SAR satellite, and is a constant; Step 07: Correct the original SAR data; adjust the backscattering coefficients in each SAR sub-image Sij according to the correction coefficient p(i,j). Perform corrections to obtain the original SAR corrected data. Where i and j represent sub-image indices, and m and n represent the position indices of pixels in sub-image Sij; the corrected formula (7) is as follows: (7) Note: Original SAR correction data It is a matrix with the same row and column size as the corresponding SAR sub-image; Comparison of sea surface SAR images before and after recalibration, for example Figure 4 As shown.

[0078] Based on the same inventive concept, this application also provides a SAR image recalibration device based on sea surface reanalysis wind field data for implementing the SAR image recalibration method based on sea surface reanalysis wind field data described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more SAR image recalibration device embodiments based on sea surface reanalysis wind field data provided below can be found in the limitations of the SAR image recalibration method based on sea surface reanalysis wind field data described above, and will not be repeated here.

[0079] In one exemplary embodiment, such as Figure 5 As shown, a SAR image recalibration device 20 based on sea surface reanalysis wind field data is provided, comprising: The acquisition module 201 is used to crop the SAR remote sensing image and acquire several SAR sub-images; Processing module 202 is used to perform consistency checks on each sub-image and remove unqualified sub-images; Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data is interpolated to obtain the wind field data at the center of the sub-image. Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; Calculate the average backscattering coefficient of the SAR sub-image; The correction factor is calculated based on the average backscattering coefficient, the incident angle, and the wind field data; The output module 203 is used to correct the backscattering coefficients in each SAR sub-image according to the correction coefficients, and obtain the original SAR corrected data.

[0080] Optionally, the acquisition module 201 is further configured to: The SAR remote sensing images are cropped according to the highest spatial resolution of the reanalysis wind field data to obtain several SAR sub-images; The images are numbered in a certain order, and each sub-image has its own number, which represents the SAR sub-image corresponding to the i-th row and j-th column.

[0081] Optionally, the processing module 202 is further configured to: Calculate the pixel size of the sub-image; Using target recognition algorithms, the size of sea surface targets such as ships, land, and oil spills is identified, and the area of ​​each sub-image is obtained; Calculate the proportion of sea surface targets in the sub-image; Compare the consistency test threshold with the proportion of the sea surface target in the sub-image, and remove sub-images that do not meet the consistency test.

[0082] Optionally, the processing module 202 is further configured to: Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data will be interpolated; Obtain wind speed and wind direction data at the center of the sub-image.

[0083] Optionally, the processing module 202 is further configured to: Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; Alternatively, the incident angle at the center of the SAR sub-image can be obtained by interpolation based on the incident angle data recorded in the SAR data.

[0084] Optionally, the processing module 202 is further configured to: The average backscattering coefficient of the SAR sub-image is obtained by summing the values ​​of each pixel in the SAR sub-image and dividing by the number of pixels.

[0085] Optionally, the processing module 202 is further configured to: Based on the average backscattering coefficient, the incident angle, and the wind field data, a correction coefficient is calculated using a function specified by a preset standard.

[0086] This application's embodiments ensure the quality of sub-images in subsequent processing by cropping SAR remote sensing images into multiple sub-images and performing rigorous consistency checks, thus avoiding the impact of substandard images on the final result. Accurate interpolation of wind field information at the center of each sub-image is performed using reanalysis wind field data, and the incident angle is accurately calculated based on the satellite capture location and the sub-image location, providing key parameters for subsequent correction coefficient calculations. By calculating the average backscattering coefficient of each qualified sub-image and combining it with wind field data and incident angles, correction coefficients can be derived. Finally, the backscattering coefficients in the original SAR image are corrected based on these correction coefficients, thereby obtaining a quantitative SAR image and improving the radiometric calibration accuracy of the SAR image.

[0087] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 6 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores SAR image recalibration data based on sea surface reanalysis wind field data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a SAR image recalibration method based on sea surface reanalysis wind field data.

[0088] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0089] In one exemplary embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0090] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0091] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0092] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0093] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0094] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0095] 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.

[0096] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A SAR image recalibration method based on sea surface reanalysis wind field data, characterized in that, The SAR image recalibration method based on sea surface reanalysis wind field data includes: The SAR remote sensing image is cropped to obtain several SAR sub-images. The steps include: The SAR remote sensing images are cropped according to the highest spatial resolution of the reanalysis wind field data to obtain several SAR sub-images; The images are numbered in a certain order, and each sub-image has its own number, representing the SAR sub-image corresponding to the i-th row and j-th column; Perform a consistency check on each sub-image and remove unqualified sub-images. The steps include: Calculate the pixel size of the sub-image; Using target recognition algorithms, the size of targets such as ships, land, and oil spill surfaces is identified, and the area of ​​each sub-image is obtained; Calculate the proportion of sea surface targets in the sub-image; Compare the consistency test threshold with the proportion of the sea surface target in the sub-image, and remove the sub-images that do not meet the consistency test. Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data is interpolated to obtain the wind field data at the center of the sub-image; the wind field data includes wind speed data and wind direction data; Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; or obtain the angle of incidence at the center of the SAR sub-image through interpolation based on the angle of incidence data recorded in the SAR data. The steps for calculating the average backscattering coefficient of a SAR sub-image include: The average backscattering coefficient of the SAR sub-image is obtained by summing the values ​​of each pixel in the SAR sub-image and dividing by the number of pixels. The correction coefficient is calculated based on the average backscattering coefficient, the incident angle, and the wind field data; the correction coefficient is calculated using a function specified by a preset standard, which is a geophysical model function. The backscattering coefficients in each SAR sub-image are corrected according to the correction coefficients to obtain the original SAR corrected data; The reanalysis wind field data includes ERA5 reanalysis wind field data, which has a spatial resolution of 0.25°×0.25° and corresponds to a geographic area size of 25 km×25 km; each SAR sub-image is 25 km×25 km in size. The consistency check threshold is a preset proportion threshold. When the proportion of sea surface targets in a sub-image is greater than or equal to the consistency check threshold, the sub-image is marked as unqualified and removed. The formula for calculating the incident angle at the center of the SAR sub-image is as follows: in, Let (x0, y0, z0) represent the incident angle at the center of the SAR sub-image in the i-th row and j-th column, and (x0, y0, z0) represent the satellite capture position. i ,y i () represents the latitude and longitude coordinates of the center position of the SAR sub-image; The correction coefficient is calculated using a function specified by a preset standard, and the calculation formula is as follows: In the formula, p(i,j) represents the correction coefficient of the SAR sub-image in the i-th row and j-th column. This represents the average backscattering coefficient of the SAR sub-image in the i-th row and j-th column. This represents the expected backscattering coefficient calculated using geophysical model functions; The geophysical model function includes any one of CMOD4, CMOD_IFR2, CMOD5.N, or CMOD7; The backscattering coefficients in each SAR sub-image are corrected according to the correction factor. The calculation formula for obtaining the original SAR corrected data is as follows: ； In the formula, This represents the corrected backscattering coefficient. This represents the backscattering coefficient of the pixel in the m-th row and n-th column of the original SAR sub-image.

2. A SAR image recalibration device based on sea surface reanalysis wind field data, characterized in that, The SAR image recalibration device based on sea surface reanalysis wind field data, used to implement the SAR image recalibration method based on sea surface reanalysis wind field data as described in claim 1, comprises: The acquisition module is used to crop SAR remote sensing images to obtain several SAR sub-images; The processing module is used to perform consistency checks on each sub-image and remove unqualified sub-images; Based on the center latitude and longitude of the SAR sub-image, the reanalysis wind field data is interpolated to obtain the wind field data at the center of the sub-image. Calculate the angle of incidence at the center of the SAR sub-image based on the satellite image location and the location of the interpolated SAR sub-image; Calculate the average backscattering coefficient of the SAR sub-image; The correction factor is calculated based on the average backscattering coefficient, the incident angle, and the wind field data; The output module is used to correct the backscattering coefficients in each SAR sub-image according to the correction coefficients, and obtain the original SAR corrected data.

3. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the SAR image recalibration method based on sea surface reanalysis wind field data as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the SAR image recalibration method based on sea surface reanalysis wind field data as described in claim 1.

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