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

Abstract

The invention discloses an SAR image recalibration method and device based on sea surface reanalysis wind field data, and relates to the technical field of image processing, and the method comprises the steps: cutting an SAR remote sensing image, and obtaining a plurality of SAR sub-images; performing consistency check on each sub-image, and removing unqualified sub-images; performing interpolation processing on the reanalyzed wind field data according to the central longitude and latitude of the SAR sub-image to obtain wind field data at the central position of the sub-image; according to the satellite shooting position and the position of the SAR sub-image after interpolation, an incident angle at the center position of the SAR sub-image is calculated; calculating an average backscattering coefficient of the SAR sub-images; 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] The present application relates to the technical field of image processing, and particularly to a method and device for SAR image rescaling based on sea surface reanalysis wind field data. Background Art

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

[0003] Traditional qualitative remote sensing methods have been difficult to meet the growing application requirements, and the demand for quantitative remote sensing inversion for typical targets and land-sea surface environments has gradually increased. Therefore, it is particularly important to perform radiometric calibration on SAR images. Radiometric calibration is a key step in quantitatively processing SAR image data, aiming to obtain the RCS (Radar Cross-Section) or radar backscattering coefficient of the target from the SAR image data, and establish a mapping relationship between the pixel power of the SAR image and the RCS of the ground target or the radar backscattering coefficient of the land-sea surface environment, so as to ensure that the satellite observation data can truly reflect the actual surface physical quantities.

[0004] Although the existing SAR radiometric calibration technologies have made certain progress, there are still some defects. Especially for micro-small commercial SAR satellites, due to their characteristics such as light weight, low cost, and fast development cycle, there is a certain gap in quantitative indicators compared with traditional large SAR satellites. Sometimes, there may be a large deviation in the radiometric calibration performance of SAR images. In addition, the existing external calibration methods mainly rely on ground calibration sources such as corner reflectors, etc., but these calibration sources are difficult to deploy in vast areas such as the ocean, which limits the wide application of SAR radiometric calibration. At the same time, traditional calibration methods often have difficulty accurately describing the influence of the sea surface state on the radar backscattering coefficient of SAR images, thus affecting the calibration accuracy. Summary of the Invention

[0005] The purpose of the present application is to provide a method and device for SAR image rescaling based on sea surface reanalysis wind field data.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a method for SAR image rescaling based on sea surface reanalysis wind field data, including:

[0008] Cropping the SAR remote sensing image to obtain a plurality of SAR sub-images;

[0009] Performing a consistency check on each sub-image to remove unqualified sub-images;

[0010] Interpolate the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image to obtain the wind field data at the center position of the sub-image;

[0011] Calculate the incident angle at the center position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image;

[0012] Calculate the average backscattering coefficient of the SAR sub-image;

[0013] Calculate the correction coefficient according to the average backscattering coefficient, the incident angle and the wind field data;

[0014] Correct the backscattering coefficient in each SAR sub-image according to the correction coefficient to obtain the original SAR correction data.

[0015] Optionally, the step of cropping the SAR remote sensing image to obtain a plurality of SAR sub-images includes:

[0016] Crop the SAR remote sensing image according to the size of the highest spatial resolution of the reanalysis wind field data to obtain a plurality of SAR sub-images;

[0017] Number the images in a certain order, and each sub-image has its own number, indicating the SAR sub-image corresponding to the i-th row and the j-th column.

[0018] Optionally, the step of performing a consistency check on each sub-image and removing unqualified sub-images includes:

[0019] Calculate the pixel size of the sub-image;

[0020] Use the target recognition algorithm to identify the sizes of sea surface targets such as ships, land, and oil spills, and obtain the area of each sub-image;

[0021] Calculate the proportion of the sea surface target in the sub-image;

[0022] Compare the size of the consistency check threshold with the proportion of the sea surface target in the sub-image, and remove the sub-images that do not meet the consistency check.

[0023] Optionally, the step of interpolating the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image to obtain the wind field data at the center position of the sub-image includes:

[0024] Interpolate the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image;

[0025] Obtain the wind speed data and wind direction data at the center position of the sub-image.

[0026] Optionally, the step of calculating the incident angle at the center position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image includes:

[0027] Calculating the incident angle at the center position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image;

[0028] Or obtaining the incident angle at the center position of the SAR sub-image through interpolation processing based on the incident angle data recorded in the SAR data.

[0029] Optionally, the step of calculating the average backscattering coefficient of the SAR sub-image includes:

[0030] Adding up each pixel value in the SAR sub-image and dividing by the number of pixels to obtain the average backscattering coefficient of the SAR sub-image.

[0031] Optionally, the step of calculating the correction coefficient according to the average backscattering coefficient, the incident angle, and the wind field data includes:

[0032] Calculating the correction coefficient according to the average backscattering coefficient, the incident angle, and the wind field data through a function specified by a preset standard.

[0033] In a second aspect, the present application provides a SAR image recalibration device based on reanalysis wind field data of the sea surface, including:

[0034] An acquisition module, configured to crop the SAR remote sensing image to obtain a plurality of SAR sub-images;

[0035] A processing module, configured to perform a consistency check on each sub-image and remove unqualified sub-images;

[0036] Interpolating the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image to obtain the wind field data at the center position of the sub-image;

[0037] Calculating the incident angle at the center position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image;

[0038] Calculating the average backscattering coefficient of the SAR sub-image;

[0039] Calculating the correction coefficient according to the average backscattering coefficient, the incident angle, and the wind field data;

[0040] An output module, configured to correct the backscattering coefficient in each SAR sub-image according to the correction coefficient to obtain the original SAR correction data.

[0041] Optionally, the obtaining module is further configured to:

[0042] Crop the SAR remote sensing image according to the size of the highest spatial resolution of the reanalysis wind field data to obtain a number of SAR sub-images;

[0043] Number the images in a certain order, and each sub-image has its own number, indicating the SAR sub-image corresponding to the i-th row and the j-th column.

[0044] Optionally, the processing module is further configured to:

[0045] Calculate the pixel size of the sub-image;

[0046] Use the target recognition algorithm to identify the sizes of sea surface targets such as ships, land, and oil spills, and obtain the area of each sub-image;

[0047] Calculate the proportion of the sea surface target in the sub-image;

[0048] Compare the size of 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.

[0049] Optionally, the processing module is further configured to:

[0050] Interpolate the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image;

[0051] Obtain the wind speed data and wind direction data at the central position of the sub-image.

[0052] Optionally, the processing module is further configured to:

[0053] Calculate the incident angle at the central position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image;

[0054] Or obtain the incident angle at the central position of the SAR sub-image through interpolation processing according to the incident angle data recorded in the SAR data.

[0055] Optionally, the processing module is further configured to:

[0056] Add up the pixel values in the SAR sub-image and divide by the number of pixels to obtain the average backscattering coefficient of the SAR sub-image.

[0057] Optionally, the processing module is further configured to:

[0058] Calculate the correction coefficient according to the average backscattering coefficient, the incident angle, and the wind field data through a function specified by a preset standard.

[0059] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the SAR image rescaling method based on sea surface reanalysis wind field data described in any one of the above.

[0060] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the SAR image rescaling method based on sea surface reanalysis wind field data described in any one of the above.

[0061] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the steps of the SAR image rescaling method based on sea surface reanalysis wind field data described in any one of the above.

[0062] According to the specific embodiments provided by the present application, the following technical effects are disclosed in the present application:

[0063] The present application provides a SAR image rescaling method and device based on sea surface reanalysis wind field data. By cropping the SAR remote sensing image into multiple sub-images and performing strict consistency checks, the quality of the subsequent processed sub-images is ensured, and the impact of unqualified images on the final result is avoided. Using the reanalysis wind field data to accurately interpolate the wind field information at the center position of the sub-image, and accurately calculating the incident angle according to the satellite shooting position and the sub-image position, key parameters for the subsequent correction coefficient calculation are provided. By calculating the average backscattering coefficient of each qualified sub-image and combining it with the wind field data, incident angle, etc., the correction coefficient can be deduced. Finally, the backscattering coefficient in the original SAR image is corrected according to these correction coefficients, thereby obtaining a quantitative SAR image and improving the radiometric calibration accuracy of the SAR image. Description of the Drawings

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0065] Figure 1 It is a schematic flowchart of a SAR image rescaling method based on sea surface reanalysis wind field data provided by an embodiment of the present application;

[0066] Figure 2 It is one of the schematic principle diagrams of a SAR image rescaling method based on sea surface reanalysis wind field data provided by an embodiment of the present application;

[0067] Figure 3 Schematic diagram II of the principle of a SAR image rescaling method provided in an embodiment of the present application based on sea surface reanalysis wind field data;

[0068] Figure 4 Schematic diagram III of the principle of a SAR image rescaling method provided in an embodiment of the present application based on sea surface reanalysis wind field data;

[0069] Figure 5 Functional module schematic diagram of a SAR image rescaling device provided in an embodiment of the present application;

[0070] Figure 6 Schematic diagram of the structure of a computer device provided in an embodiment of the present application. Detailed implementation manners

[0071] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0072] As Figure 1 shown, some embodiments of the present application provide a SAR image rescaling method based on sea surface reanalysis wind field data. In the embodiments of the present application, the following steps 101 to 107 are included. Among them:

[0073] Step 101: Crop the SAR remote sensing image to obtain a plurality of SAR sub-images.

[0074] In the embodiments of the present application, the SAR (Synthetic Aperture Radar) remote sensing image is an image of the earth's surface obtained by a synthetic aperture radar system. The SAR system generates an image by transmitting microwave signals and receiving their reflected signals, and has the imaging ability of all-weather and all-day. Cropping means dividing a complete SAR remote sensing image into multiple smaller image blocks according to certain rules, and these image blocks are called SAR sub-images. The purpose of cropping is to facilitate subsequent processing and analysis.

[0075] Refer to 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 (for example, the spatial resolution of ERA5 reanalysis wind field data is 0.25°, about 25 kilometers). The size of each cropped SAR sub-image is 25 kilometers × 25 kilometers. The system numbers each sub-image in the order from left to right and from top to bottom. For example, the sub-image number of the i-th row and j-th column is s_ij. The cropped SAR sub-images will be used for subsequent consistency checks and wind field data interpolation processing.

[0076] Step 102, perform a consistency check on each sub-image and remove the unqualified sub-images.

[0077] In the embodiment of the present application, the consistency check refers to the quality assessment of the SAR sub-image to determine whether it meets the preset quality standard. The purpose of the consistency check is to ensure that the proportion of the sea surface area in the sub-image is relatively high and to avoid including too many non-sea surface targets (such as ships, land, oil spills, etc.) that may affect subsequent wind field data inversion and radiometric calibration. The unqualified sub-images refer to the sub-images that fail to pass the preset threshold after the consistency check. These sub-images usually contain too many non-sea surface targets and are not suitable for subsequent wind field data inversion and radiometric calibration.

[0078] 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 of the sub-image. Then, the system uses the target recognition algorithm to detect non-sea surface targets such as ships, land, and oil spills in the sub-image and calculates the pixel proportion of these targets in the sub-image. If the proportion of non-sea surface targets is less than the preset threshold (for example, 5%), the sub-image passes the consistency check; otherwise, the system marks the sub-image as unqualified and removes it. The sub-images that pass the consistency check will be used for subsequent wind field data interpolation and radiometric calibration.

[0079] Step 103, perform interpolation processing on the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image to obtain the wind field data at the center position of the sub-image.

[0080] In the embodiment of the present application, the reanalysis wind field data is a wind field data set generated by fusing multiple data sources such as numerical model forecasts, offshore buoy observations, and satellite remote sensing observations through data assimilation technology. The reanalysis wind field data can provide wind speed and wind direction information with a long time series and has a high spatio-temporal resolution. Interpolation processing refers to estimating the values of unknown data points through mathematical methods (such as linear interpolation, nearest neighbor interpolation, polynomial interpolation, etc.) based on the values of known data points. In this step, the purpose of interpolation processing is to obtain the wind speed and wind direction data at the center position of the SAR sub-image according to the spatial distribution of the reanalysis wind field data.

[0081] The system interpolates the reanalysis wind field data according to the central longitude and latitude coordinates of the SAR sub-images that pass the consistency test. The reanalysis wind field data is usually distributed in a grid form, and the system calculates the wind speed and wind direction data at the center position of the SAR sub-image through an interpolation algorithm (such as linear interpolation). After the interpolation process, the system obtains the wind speed data U_ij and wind direction data at the center position of each SAR sub-image. These data will be used for subsequent incidence angle calculation and backscatter coefficient correction.

[0082] Step 104: Calculate the incidence angle at the center position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image.

[0083] In the embodiment of the present application, the satellite shooting position refers to the spatial position of the SAR satellite when shooting the SAR image, usually including information such as the satellite's orbital altitude and shooting angle. The satellite shooting position is an important parameter for calculating the incidence angle of the SAR image. The incidence angle refers to the angle between the microwave signal emitted by the SAR satellite and the normal of the ground target surface. The magnitude of the incidence angle affects the backscatter intensity of the SAR image and is an important parameter in SAR radiometric calibration.

[0084] The system calculates the incidence angle θ_ij at the center position of each SAR sub-image according to the shooting position of the SAR satellite and the center position of the interpolated SAR sub-image. The calculation of the incidence angle is usually based on geometric relationships. The system uses trigonometric functions to calculate the incidence angle through the satellite's orbital parameters and the geographical coordinates of the sub-image.

[0085] Step 105: Calculate the average backscatter coefficient of the SAR sub-image.

[0086] In the embodiment of the present application, the backscatter coefficient refers to the ratio of the intensity of the target reflection signal received by the SAR system to the intensity of the transmitted signal, usually denoted by σ. The backscatter coefficient reflects the scattering characteristics of the target surface and is an important parameter for SAR image quantitative processing. The average backscatter coefficient refers to the average value of the backscatter coefficients of all pixels in the SAR sub-image. The purpose of calculating the average backscatter coefficient is to simplify the subsequent correction coefficient calculation.

[0087] The system sums the backscatter coefficients of each pixel in the SAR sub-image that passes the consistency test, and then divides by the total number of pixels in the sub-image to obtain the average backscatter coefficient σ_ij of the sub-image.

[0088] Step 106: Calculate the correction coefficient according to the average backscatter coefficient, the incidence angle, and the wind field data.

[0089] In the embodiments of the present application, the correction coefficient refers to a parameter used to adjust the backscattering coefficient of the SAR sub-image. The calculation of the correction coefficient is based on the Geophysical Model Function (GMF). By comparing the difference between the actual backscattering coefficient and the theoretical backscattering coefficient, the correction coefficient is determined. The Geophysical Model Function is an empirical or theoretical function that describes the relationship between the Normalized Radar Cross Section (NRCS) of the sea surface and parameters such as wind speed, incident angle, and wind direction. Commonly used GMFs include CMOD5.N and CMOD7.

[0090] The system calculates the correction coefficient p_ij using the Geophysical Model Function (such as CMOD5.N) based on the average backscattering coefficient σ_ij, the incident angle θ_ij, and the wind field data (wind speed U_ij and wind direction ).

[0091] Step 107: Correct the backscattering coefficient in each SAR sub-image according to the correction coefficient to obtain the original SAR correction data.

[0092] In the embodiments of the present application, the original SAR correction data refers to the SAR sub-image data adjusted by the correction coefficient. The corrected data can more accurately reflect the backscattering characteristics of the sea surface and improve the quantitative application ability of the SAR image.

[0093] The system corrects the backscattering coefficient σ_mn in each SAR sub-image according to the correction coefficient p_ij to obtain the corrected backscattering coefficient σ'_mn.

[0094] In the embodiments of the present application, by cropping the SAR remote sensing image into multiple sub-images and performing strict consistency checks, the quality of the sub-images for subsequent processing is ensured, and the influence of unqualified images on the final result is avoided. Using the reanalysis wind field data to accurately interpolate the wind field information at the center position of the sub-image, and accurately calculating the incident angle according to the satellite shooting position and the sub-image position, key parameters for the subsequent calculation of the correction coefficient are provided. By calculating the average backscattering coefficient of each qualified sub-image and combining it with the wind field data, incident angle, etc., the correction coefficient can be deduced. Finally, according to these correction coefficients, the backscattering coefficient in the original SAR image is corrected, thereby obtaining a quantitative SAR image and improving the radiometric calibration accuracy of the SAR image.

[0095] Optionally, step 101 includes:

[0096] Step 1011: Crop the SAR remote sensing image according to the size of the highest spatial resolution of the reanalysis wind field data to obtain a number of SAR sub-images.

[0097] In the embodiments of the present application, the reanalysis wind field data is a dataset that integrates various sources and types of data (such as numerical model forecasts, offshore buoy observations, scatterometer / radiometer satellite remote sensing observations, etc.). By means of data assimilation technology, historical data is reconstructed, and sea surface wind field information is provided, which has high spatial and temporal resolutions. The highest spatial resolution refers to the fineness of the spatial information in the reanalysis wind field data, that is, the size of the smallest geographical unit that can be distinguished. The SAR sub-image is a small-area image cropped from the original SAR image and matching the spatial resolution of the reanalysis wind field data.

[0098] The system first identifies the highest spatial resolution of the reanalysis wind field data. Taking the ERA5 reanalysis wind field data as an example, its spatial resolution is 0.25°, which is approximately equivalent to a geographical area of 25Km×25Km. Subsequently, the system performs cropping processing on each input SAR image to ensure that the spatial range of each cropped SAR sub-image matches the highest spatial resolution of the reanalysis wind field data, that is, the size of each sub-image is 25Km×25Km. In this way, the original large-area SAR image is divided into multiple small-area SAR sub-images with consistent spatial resolutions, providing a basis for subsequent processing and analysis.

[0099] Step 1012, number the images in a certain order, and each sub-image has its own number, indicating the SAR sub-image corresponding to the i-th row and the j-th column.

[0100] In the embodiments of the present application, the system numbers the cropped SAR sub-images in a predetermined order (such as from left to right and from top to bottom). Each sub-image has a unique number, which is composed of the row number "i" and the column number "j", jointly indicating the position of the sub-image in the two-dimensional image array. For example, the number "sij" represents the SAR sub-image in the i-th row and the j-th column. Such a numbering method helps the system quickly locate and access specific SAR sub-images in subsequent processing, improving the processing efficiency and accuracy.

[0101] Specifically, crop each SAR remote sensing image to obtain a number of SAR sub-images, as Figure 2 shown; specifically as follows: Crop each image according to the size of the highest spatial resolution Δρ of the reanalysis wind field data (taking the ERA5 reanalysis wind field data as an example, its spatial resolution is 0.25°, about 25Km, so the cropping size is 25Km×25Km) to obtain a number of SAR sub-images; number the images in a certain order. For example, number the sub-images in the order from left to right and from top to bottom. ij represents the SAR sub-image corresponding to the i-th row and the j-th column. Each sub-image has its own number sij, indicating the SAR sub-image corresponding to the i-th row and the j-th column;

[0102] Optionally, step 102 includes:

[0103] Step 1021, calculate the pixel size of the sub-image.

[0104] In the embodiment of the present application, the system first reads the number of pixel rows (Row) and the number of pixel columns (Col) of any sub-image (for example, the first sub-image s11). Then, according to the number of pixel rows Row and the number of pixel columns Col, the pixel size Pix of the sub-image is calculated through a formula. This formula may be the number of rows multiplied by the number of columns, that is, Pix = Row * Col. After calculating the pixel size, the system knows the specific size and the number of pixels contained in each sub-image, providing basic data for subsequent processing.

[0105] Step 1022, use the target recognition algorithm to identify the sizes of sea surface targets such as ships, land, and oil spills, and obtain the area of each sub-image.

[0106] In the embodiment of the present application, the target recognition algorithm is an algorithm that can automatically identify and locate 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, which reflects the size of the targets.

[0107] The system uses the target recognition algorithm to process each sub-image, automatically identifies the sea surface targets, and calculates the number of pixels occupied by these targets in the sub-image, that is, the pixel size pij occupied by the targets. This step is the basis for subsequent calculation of the proportion of sea surface targets in the sub-image.

[0108] Step 1023, calculate the proportion of the sea surface target in the sub-image.

[0109] In the embodiment of the present application, the proportion of the sea surface target in the sub-image refers to the ratio of the number of pixels occupied by the sea surface target to the total number of pixels in the sub-image, which reflects the relative size of the sea surface target in the sub-image.

[0110] The system calculates the proportion of the sea surface target in the sub-image through a formula according to the pixel size pij of the target obtained in step 1022 and the pixel size Pix of the sub-image obtained 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, that is, the proportion = pij / Pix. After calculating the proportion, the system knows the relative size of the sea surface target in each sub-image.

[0111] Step 1024, compare the size of 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.

[0112] In the embodiments of the present application, the consistency check threshold is a preset value used to determine whether a sub-image meets the consistency requirements. The proportion of the sea surface target in the sub-image refers to the proportion calculated in step 1023. Removing the sub-images that do not meet the consistency check means excluding those sub-images with a proportion exceeding the threshold from subsequent processing.

[0113] The system compares the proportion of the sea surface target in each sub-image with the preset consistency check threshold. If the proportion of a certain sub-image exceeds the threshold, it indicates that the proportion of the sea surface target in this sub-image is too large, which may affect the uniformity and quality of the image. Therefore, the system excludes this sub-image from subsequent processing. This step is an important link to ensure the accuracy and reliability of the subsequent processing results. By removing the sub-images that do not meet the consistency requirements, the system can reduce errors and interference and improve the accuracy and credibility of the final processing results.

[0114] Specifically, calculate the pixel size of the sub-image; read the number of pixel rows Row and the number of pixel columns Col of any sub-image (such as the first sub-image s11), and calculate the pixel size Pix of the sub-image. The formula (1) is as follows:

[0115] Pix = Row × Col (1)

[0116] Use the target recognition algorithm to identify the sizes of sea surface targets such as ships, land, and oil spills, and obtain each sub-image;

[0117] Calculate the proportion of the sea surface target in the sub-image; use the pixel size pij of the target obtained in step Step022 and the pixel size Pix of the sub-image calculated in step Step021 to calculate the proportion ρ of the sea surface target in the sub-image ij , and the formula (2) is as follows:

[0118]

[0119] Compare the consistency check ρ0 (threshold, such as ρ0 = 1.8, which can also be adjusted according to the actual situation of the sea area), and the proportion ρ of the sea surface target in the sub-image obtained in step Step023 ij in size, and remove the sub-images that do not meet the consistency check, that is, the sub-images Sij with ρ ij ≥ ρ0, and obtain the sub-images Sij that meet the consistency check;

[0120] Optionally, the step 103 includes:

[0121] Step 1031, perform interpolation processing on the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image.

[0122] In the embodiments of the present application, the system performs interpolation processing on the reanalysis wind field data according to the central longitude and latitude coordinates of each SAR sub-image. This process involves reading the reanalysis wind field data set, which contains wind field information within a wide geographical range. Then, based on the central position of the SAR sub-image, the system uses interpolation algorithms (such as linear interpolation, nearest neighbor interpolation, polynomial interpolation, etc.) to estimate the wind field data at that position. The result of the interpolation processing is a wind field data value corresponding to the central position of the SAR sub-image, and these data provide the necessary input for subsequent steps.

[0123] Step 1032: Obtain the wind speed data and wind direction data at the central position of the sub-image.

[0124] In the embodiments of the present application, after the interpolation processing is completed, the system extracts the wind speed data and wind direction data corresponding to the central position of the SAR sub-image from the interpolation result. These data are estimated by the interpolation algorithm based on the known data points in the reanalysis wind field data set. The wind speed data provides information about the intensity of the wind field, while the wind direction data provides information about the direction of the wind field. This information is crucial for calculating the correction coefficient and performing SAR image rescaling in subsequent steps. The system uses these wind speed and wind direction data as key inputs for further processing and analysis.

[0125] Specifically, referring to Figure 3 , according to the obtained central longitude and latitude (xi, yi) of the SAR sub-image, perform interpolation processing on the reanalysis wind field data (interpolation algorithms: linear interpolation, nearest neighbor interpolation, polynomial interpolation,...), as Figure 3 shown, Figure 3 The uniformly distributed gray points in E represent the reanalysis wind field data, and the black points represent the positions of the central points of the SAR sub-images Sij (meeting the consistency test); through interpolation processing, obtain the corresponding reanalysis wind field data at the central position (xi, yi) of the sub-image, and the wind field data includes the wind speed data U E (i, j) and the wind direction data φ

[0126] Optionally, step 104 includes: calculating the incident angle at the central position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image, or obtaining the incident angle at the central position of the SAR sub-image through interpolation processing according to the incident angle data recorded in the SAR data.

[0127] In the embodiments of the present application, the system first obtains the precise position information when the satellite takes pictures, as well as the longitude and latitude of the center point of the processed SAR sub-image. Then, the system uses this information, combined with the shape and size of the earth, to calculate the incident angle when the radar beam reaches the center position 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.

[0128] Alternatively, in some cases, the SAR image data may already contain the incident angle information. At this time, the system can directly read this data and apply an interpolation algorithm to estimate the incident angle at the center position 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, polynomial interpolation, etc. Through interpolation processing, the system can generate an incident angle matrix corresponding to the SAR sub-image for subsequent radiometric calibration and data analysis.

[0129] Specifically, calculate the incident angle of the SAR sub-image; according to the satellite shooting position (x0, y0, z0) and the position of the interpolated SAR sub-image Sij, calculate the incident angle θ at the center position of the SAR sub-image Sij (satisfying the consistency test) s (i, j), where i and j represent indices, and the calculation formula is as shown in the formula;

[0130]

[0131] Alternatively, according to the incident angle data recorded in the SAR data, through interpolation processing, obtain the incident angle θ at the center position of the SAR sub-image Sij (satisfying the consistency test) s (i, j);

[0132] Optionally, step 105 includes: adding up each pixel value in the SAR sub-image and dividing by the number of pixels to obtain the average backscattering coefficient of the SAR sub-image.

[0133] In the embodiments of the present application, calculate the average backscattering coefficient of the SAR sub-image; add up each pixel value in the SAR sub-image Sij (satisfying the consistency test) and divide by the number of pixels to obtain the average backscattering coefficient after the SAR sub-image Sij (satisfying the consistency test) where i and j represent indices.

[0134] Optionally, step 106 includes: calculating a correction coefficient according to the average backscattering coefficient, the incident angle, and the wind field data through a function specified by a preset standard.

[0135] In the embodiments of the present application, the system first obtains the average backscattering coefficient of the SAR sub-images that have passed the consistency test. This step is completed by adding all the pixel values in the sub-image and then dividing by the total number of pixels. The obtained average backscattering coefficient represents the average of the sea surface radar backscattering characteristics of the sub-image area. Next, the system calculates the incident angle at the center position of each sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image. 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. At the same time, the system also obtains the wind field data corresponding to the center position of each SAR sub-image, including wind speed and wind direction information. These data describe the dynamic state of the sea surface at this position and have an important impact on the sea surface radar backscattering. Finally, the system uses a function specified by a preset standard, taking the average backscattering coefficient, the incident angle, and the wind field data as inputs, and calculates the correction coefficient. This function is based on theoretical or empirical relationships such as the Geophysical Model Function (GMF), and through complex mathematical operations, it converts the input data into a correction coefficient. The correction coefficient is used to correct the original SAR data in subsequent steps to obtain a more accurate quantitative SAR image. This process is automatically completed without manual intervention.

[0136] Specifically, calculate the correction coefficient p: according to the average backscattering coefficient obtained from the SAR sub-image Sij (meeting the consistency test) The incident angle θ at the center position of the SAR sub-image Sij (meeting the consistency test) calculated in Step 04 s (i, j), the wind field data (wind speed data U E (i, j) and wind direction data φ E (i, j)) obtained in Step 03, calculate the correction coefficient p(i, j), where i and j represent indices; Formula (4) is as follows,

[0137]

[0138] where represents the expected backscattering coefficient calculated by the CMOD function for the SAR sub-image Sij (meeting the consistency test) in the i-th row and j-th column, The calculation formula (5) is as follows:

[0139]

[0140] CMOD5.N represents a wind field model, and φ(i, j) represents the difference between the wind direction and the SAR look direction, which can be expressed as formula (6):

[0141] φ(i, j) = |φ E (i, j) - φ0(i, j)| (6)

[0142] Where φ0(i, j) is a SAR image system parameter, representing the look direction data when the SAR satellite takes pictures, and it is a constant;

[0143] Step Step07: Correct the original SAR data; correct the backscattering coefficient σ0(i, j, m, n) in each SAR sub-image Sij according to the correction coefficient p(i, j) to obtain the original SAR corrected data Where i and j represent sub-image indexes, and m and n represent the position indexes corresponding to the pixels in the sub-image Sij; the correction formula (7) is as follows:

[0144]

[0145] Note: The original SAR corrected data is a matrix consistent with the row and column sizes of the corresponding SAR sub-image;

[0146] Comparison of sea surface SAR images before and after rescaling is as Figure 4 shown.

[0147] Based on the same inventive concept, the embodiment of the present application also provides a SAR image rescaling device based on sea surface reanalysis wind field data for implementing the above-mentioned SAR image rescaling method based on sea surface reanalysis wind field data. The implementation solutions provided by this device to solve problems are similar to the implementation solutions recorded in the above method. Therefore, the specific limitations in one or more embodiments of the SAR image rescaling device based on sea surface reanalysis wind field data provided below can refer to the limitations on the SAR image rescaling method based on sea surface reanalysis wind field data in the above text, and will not be repeated here.

[0148] In an exemplary embodiment, as Figure 5 shown, a SAR image rescaling device 20 based on sea surface reanalysis wind field data is provided, including:

[0149] An acquisition module 201, configured to crop the SAR remote sensing image to obtain a plurality of SAR sub-images;

[0150] A processing module 202, configured to perform consistency check on each sub-image and remove unqualified sub-images;

[0151] Interpolate the reanalysis wind field data according to the central longitude and latitude of the SAR sub-image to obtain the wind field data at the center position of the sub-image;

[0152] Calculate the incident angle at the center position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image;

[0153] Calculate the average backscattering coefficient of the SAR sub - image;

[0154] Calculate a correction coefficient according to the average backscattering coefficient, the incident angle, and the wind field data;

[0155] An output module 203, configured to correct the backscattering coefficient in each SAR sub - image according to the correction coefficient to obtain original SAR correction data.

[0156] Optionally, the obtaining module 201 is further configured to:

[0157] Crop the SAR remote - sensing image according to the size of the highest spatial resolution of the re - analysis wind field data to obtain a plurality of SAR sub - images;

[0158] Number the images in a certain order, and each sub - image has its own number, representing the SAR sub - image corresponding to the i - th row and the j - th column.

[0159] Optionally, the processing module 202 is further configured to:

[0160] Calculate the pixel size of the sub - image;

[0161] Use an object recognition algorithm to identify the sizes of sea - surface targets such as ships, land, and oil spills, and obtain the area of each sub - image;

[0162] Calculate the proportion of the sea - surface target in the sub - image;

[0163] Compare the consistency - check threshold with the proportion of the sea - surface target in the sub - image, and remove the sub - images that do not meet the consistency check.

[0164] Optionally, the processing module 202 is further configured to:

[0165] Interpolate the re - analysis wind field data according to the central longitude and latitude of the SAR sub - image;

[0166] Obtain the wind speed data and wind direction data at the central position of the sub - image.

[0167] Optionally, the processing module 202 is further configured to:

[0168] Calculate the incident angle at the central position of the SAR sub - image according to the satellite shooting position and the position of the interpolated SAR sub - image;

[0169] Or obtain the incident angle at the central position of the SAR sub - image through interpolation according to the incident - angle data recorded in the SAR data.

[0170] Optionally, the processing module 202 is further configured to:

[0171] Add the pixel values of each pixel in the SAR sub - image and divide by the number of pixels to obtain the average backscattering coefficient of the SAR sub - image.

[0172] Optionally, the processing module 202 is further configured to:

[0173] Calculate a correction coefficient according to the average backscattering coefficient, the incident angle, and the wind field data through a function specified by a preset standard.

[0174] In the embodiment of the present application, by cropping the SAR remote - sensing image into multiple sub - images and performing strict consistency checks, the quality of the subsequent processed sub - images is ensured, and the impact of unqualified images on the final result is avoided. Using the re - analysis wind field data to accurately interpolate the wind field information at the center position of the sub - image, and accurately calculating the incident angle according to the satellite shooting position and the sub - image position provide key parameters for the subsequent calculation of the correction coefficient. By calculating the average backscattering coefficient of each qualified sub - image and combining it with the wind field data, the incident angle, etc., the correction coefficient can be deduced. Finally, the backscattering coefficient in the original SAR image is corrected according to these correction coefficients, thereby obtaining a quantitative SAR image and improving the radiometric calibration accuracy of the SAR image.

[0175] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 6 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non - volatile storage medium and an internal memory. The non - volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non - volatile storage medium. The database of the computer device is used to store SAR image recalibration data based on sea - surface re - analysis wind field data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for SAR image recalibration based on sea - surface re - analysis wind field data.

[0176] Those skilled in the art can understand, Figure 6The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0177] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0178] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0179] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0180] 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 for analysis, stored data, displayed data, 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 relevant data need to comply with relevant regulations.

[0181] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0182] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0183] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0184] Specific examples are used in this article to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present 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 comprises: Crop the SAR remote sensing image to obtain several SAR sub-images; Perform consistency check on each sub-image and remove unqualified sub-images; According to the central longitude and latitude of the SAR sub-image, the reanalyzed wind field data is interpolated to obtain the wind field data at the central position of the sub-image; According to the satellite shooting position and the position of the interpolated SAR sub-image, the incident angle at the center position of the SAR sub-image is calculated; Calculate the average backscatter coefficient of the SAR sub-image; Calculating a correction coefficient according to the average backscattering coefficient, the incident angle and the wind field data; The backscattering coefficient in each SAR sub-image is corrected according to the correction coefficient to obtain original SAR correction data.

2. The SAR image recalibration method based on sea surface reanalysis wind field data according to claim 1 is characterized in that: The step of cropping the SAR remote sensing image to obtain a plurality of SAR sub-images includes: 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; The images are numbered in a certain order, and each sub-image has its own number, indicating the SAR sub-image corresponding to the i-th row and j-th column.

3. The SAR image recalibration method based on sea surface reanalysis wind field data according to claim 1 is characterized in that: The step of performing consistency check on each sub-image and removing unqualified sub-images comprises: Calculate the sub-image pixel size; Use target recognition algorithms to identify the size of sea targets such as ships, land, and oil spills, and obtain the area of ​​each sub-image; Calculate the proportion of sea surface targets in the sub-image; The consistency check threshold is compared with the proportion of the sea surface target in the sub-image, and the sub-image that does not meet the consistency check is removed.

4. The SAR image recalibration method based on sea surface reanalysis wind field data according to claim 1, characterized in that: The step of interpolating the reanalyzed wind field data according to the central longitude and latitude of the SAR sub-image to obtain the wind field data at the central position of the sub-image comprises: According to the central longitude and latitude of the SAR sub-image, the reanalyzed wind field data is interpolated; Get the wind speed and direction data at the center of the sub-image.

5. The SAR image recalibration method based on sea surface reanalysis wind field data according to claim 1, characterized in that: The step of calculating the incident angle at the center position of the SAR sub-image according to the satellite shooting position and the position of the interpolated SAR sub-image comprises: According to the satellite shooting position and the position of the interpolated SAR sub-image, the incident angle at the center position of the SAR sub-image is calculated; Alternatively, the incident angle at the center of the SAR sub-image is obtained through interpolation processing according to the incident angle data recorded in the SAR data.

6. The SAR image recalibration method based on sea surface reanalysis wind field data according to claim 1, characterized in that: The step of calculating the average backscatter coefficient of the SAR sub-image comprises: The average backscatter coefficient of the SAR sub-image is obtained by adding up the values ​​of each pixel in the SAR sub-image and dividing it by the number of pixels.

7. The SAR image recalibration method based on sea surface reanalysis wind field data according to claim 1, characterized in that: The step of calculating the correction coefficient according to the average backscatter coefficient, the incident angle and the wind field data comprises: A correction coefficient is calculated according to the average backscattering coefficient, the incident angle and the wind field data by using a function specified by a preset standard.

8. 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 comprises: An acquisition module is used to crop the SAR remote sensing image to obtain several SAR sub-images; A processing module, used for performing consistency check on each sub-image and removing unqualified sub-images; According to the central longitude and latitude of the SAR sub-image, the reanalyzed wind field data is interpolated to obtain the wind field data at the central position of the sub-image; According to the satellite shooting position and the position of the interpolated SAR sub-image, the incident angle at the center position of the SAR sub-image is calculated; Calculate the average backscatter coefficient of the SAR sub-image; Calculating a correction coefficient according to the average backscattering coefficient, the incident angle and the wind field data; The output module is used to correct the backscatter coefficient in each SAR sub-image according to the correction coefficient to obtain original SAR correction data.

9. A computer device comprising: 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 according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the SAR image recalibration method based on sea surface reanalysis wind field data described in any one of claims 1 to 7 are implemented.

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