SAR historical data rescaling method and system based on alien cross

Abstract

The present application relates to a kind of SAR historical data rescaling method and system based on different star crossing.The method includes: collecting the same band different satellite's already calibrated spaceborne SAR historical image data, to-be-calibrated spaceborne SAR historical image data, pre-processing, terrain correction, latitude and longitude clipping, registration, and generate rescaling historical data pair;Through resolution resampling processing, and carry out incidence angle correction, construct rescaling cross data pair input into Gaussian mixture model, calculate the calibration coefficient of SAR historical image.By collecting the same band different satellite's spaceborne SAR historical image data, the imaging resolution difference between different loads is solved;By pre-processing, terrain correction, clipping registration and other processing to spaceborne SAR rescaling data pair, Gaussian mixture model is used for clustering fitting, and the calibration coefficient of to-be-calibrated historical data is solved by weighted fitting to different data pairs, which can improve the accuracy of spaceborne SAR historical data rescaling.

Description

Technical Field

[0001] The present invention relates to the technical field of synthetic aperture radar radiation calibration, and in particular to a SAR historical data recalibration method and system based on alien satellite crossover. Background Art

[0002] Absolute radiometric calibration of synthetic aperture radar (SAR) is the process of eliminating systematic errors and accurately converting the digital number (DN) output by the radar system into the backscatter characteristics of the observed target (i.e., backscatter coefficient or radar cross section). This technology is fundamental to the quantitative processing and application of SAR data, ensuring the accuracy, consistency, and comparability of SAR data and meeting the requirements of quantitative remote sensing. Existing SAR radiometric calibration technologies are mainly divided into radiometric calibration based on manual calibrators and radiometric cross-calibration. Radiometric calibration based on manual calibrators involves deploying a certain number of calibrators with known radar cross sections (RCS) at the calibration site and calculating absolute radiometric calibration coefficients based on the relationship between the digital quantized values ​​of their images and the radar cross-section. However, this technology faces limitations in the deployment, maintenance, and number of calibrators, making large-scale calibration difficult. In contrast, radiometric cross-calibration uses stable ground targets as common reference points and uses highly calibrated SAR sensor data as a reference or standard to cross-calibrate the SAR sensors being calibrated, eliminating the need for manual calibrators on the ground.

[0003] Historical data recalibration refers to the recalibration of already collected remote sensing data. In the field of optical remote sensing, historical data recalibration has been widely used due to its important role in improving the accuracy of historical remote sensing data, ensuring data consistency, and supporting long-term research. Historical data recalibration ensures comparability of data across time and between different sensors, enabling more accurate descriptions of environmental changes and assessments of their impacts. Traditional historical data recalibration techniques often rely on cross-calibration of historical data from the same satellite. Because these techniques fail to account for errors caused by payload performance fluctuations, when calibrated data is used to calibrate previously acquired data, significant calibration errors can be introduced due to changes in the ground reference (e.g., rainfall) over this extended time span. Furthermore, the calculation of recalibration coefficients fails to accurately account for the stable statistical characteristics of diverse uniform objects, resulting in low radiometric calibration accuracy, which in turn affects the quantitative inversion accuracy of SAR satellite remote sensing products.

[0004] Therefore, traditional historical data recalibration technology often has the problem of low calibration accuracy. Summary of the Invention

[0005] Based on this, in order to solve the above technical problems, a SAR historical data recalibration method and system based on alien satellite crossing are provided, which can improve the accuracy of historical data recalibration.

[0006] A SAR historical data recalibration method based on heterogeneous satellite crossover, the method comprising:

[0007] Collecting calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data from different satellites in the same band, and extracting data using the same polarization mode from the calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data as spaceborne SAR recalibration data pairs;

[0008] Preprocessing the spaceborne SAR recalibration data pair, performing terrain correction on the preprocessed spaceborne SAR recalibration data pair, performing longitude and latitude clipping on the terrain-corrected calibrated spaceborne SAR historical image data and the to-be-calibrated spaceborne SAR historical image data, and then performing image registration using a SAR image registration method to generate a recalibrated historical data pair;

[0009] performing resolution resampling processing on the recalibrated historical data pairs, and performing incident angle correction on the recalibrated historical data pairs having incident angle differences, to construct recalibrated cross data pairs;

[0010] Inputting the recalibrated cross data pairs into a Gaussian mixture model, fitting the recalibrated cross data pairs through the Gaussian mixture model, and calculating calibration coefficients of SAR historical images;

[0011] The calibration coefficients of the SAR historical images are verified, and the historical data recalibration results are output.

[0012] In one embodiment, collecting calibrated spaceborne SAR historical image data and to-be-calibrated spaceborne SAR historical image data from different satellites in the same band includes:

[0013] The calibrated spaceborne SAR historical image data and the spaceborne SAR historical image data to be calibrated are collected from different satellites in the same band with the same coverage area, the same imaging ascending and descending orbits, and imaging time intervals within the threshold range.

[0014] In one embodiment, preprocessing the spaceborne SAR recalibration data includes:

[0015] performing the same preprocessing operation on the data in the spaceborne SAR recalibration data pair;

[0016] The preprocessing operations include thermal noise removal, multi-view processing, speckle filtering, and radiation correction.

[0017] In one of the embodiments, the pre-processed spaceborne SAR re-scaling data pair is terrain corrected, including:

[0018] The pre-processed spaceborne SAR re-scaling data pair is terrain corrected using a geographic coding method in the same coordinate system, so that the image data of different loads in the pre-processed spaceborne SAR re-scaling data pair correspond to the same geographic information on the pixel points.

[0019] In one of the embodiments, the re-scaling historical data pair is resolution resampled, including:

[0020] The lower resolution in the re-scaling historical data pair is taken as a resampling reference;

[0021] Based on the resampling reference, each pixel point of the image in the re-scaling historical data pair is weighted and calculated by a downsampling function to obtain a new pixel value.

[0022] In one of the embodiments, the re-scaling historical data pair with an incidence angle difference is corrected for the incidence angle, including:

[0023] The incidence angle corresponding to the re-scaling historical data pair is calculated, and if the difference between the incidence angle and a reference incidence angle is within a threshold range, there is no incidence angle difference;

[0024] If the difference between the incidence angle and the reference incidence angle exceeds the threshold range, it is determined that there is an incidence angle difference, and an incidence angle correction model is used for correction.

[0025] In one of the embodiments, a re-scaling cross data pair is constructed, including:

[0026] A first pixel value corresponding to the to-be-scaled historical data in the re-scaling historical data pair is extracted, and the first pixel value is taken as an independent variable of the re-scaling cross data pair;

[0027] A second pixel value corresponding to the already-scaled standard historical data after downsampling in the re-scaling historical data pair is extracted, and the second pixel value is taken as a dependent variable of the re-scaling cross data pair;

[0028] The independent variable and the dependent variable are combined to form a two-dimensional data pair, and the two-dimensional data pair is taken as the data corresponding to the re-scaling cross data pair, and the re-scaling cross data pair is constructed.

[0029] In one of the embodiments, the re-scaling cross data pair is input into a Gaussian mixture model, the re-scaling cross data pair is fitted by the Gaussian mixture model, and a scaling coefficient of the SAR historical image is calculated, including:

[0030] Inputting the recalibrated cross-data pairs into a Gaussian mixture model, extracting the high-frequency region in each of the two-dimensional data pairs, and calculating the interquartile range of the two-dimensional data pairs;

[0031] Based on the high-frequency area, the Freedman-Diaconis rule is used to calculate the bin width, and the joint distribution of the two-dimensional histogram statistics variables is used to obtain the high-frequency area data;

[0032] If the recalibrated cross-data pairs are data pairs of different types of regions, then the high-frequency region data is used to fit the Gaussian mixture model of each two-dimensional Gaussian distribution, and the K-means method is used to initialize the initial parameters of the Gaussian mixture model;

[0033] Gaussian mixture model fitting is performed on the recalibrated cross data pairs to calculate the calibration coefficients of the SAR historical images.

[0034] In one embodiment, performing Gaussian mixture model fitting on the recalibrated cross data pairs to calculate calibration coefficients of SAR historical images includes:

[0035] Fitting the recalibrated cross-data pair with a Gaussian mixture model to extract the corresponding expectation and Gaussian weight of the Gaussian distribution in the high-frequency region;

[0036] The Gaussian weight and the expected ratio corresponding to the Gaussian weight are multiplied to obtain a calibration coefficient of the SAR historical image.

[0037] A SAR historical data recalibration system based on heterogeneous satellite crossover, the system comprising:

[0038] a data acquisition module for collecting calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data from different satellites in the same band, and extracting data using the same polarization mode from the calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data as spaceborne SAR recalibration data pairs;

[0039] a data preprocessing module, configured to preprocess the spaceborne SAR recalibration data pair, perform terrain correction on the preprocessed spaceborne SAR recalibration data pair, perform longitude and latitude clipping on the terrain-corrected calibrated spaceborne SAR historical image data and the to-be-calibrated spaceborne SAR historical image data, and then perform image registration using a SAR image registration method to generate a recalibration historical data pair;

[0040] a data pair construction module, configured to perform resolution resampling processing on the recalibrated historical data pairs, and perform incident angle correction on the recalibrated historical data pairs having incident angle differences, to construct recalibrated cross data pairs;

[0041] a scaling coefficient calculation module, configured to input the rescaling cross data pair into a Gaussian mixture model, fit the rescaling cross data pair by using the Gaussian mixture model, and calculate a scaling coefficient of the SAR historical image;

[0042] a result verification module, configured to verify the scaling coefficient of the SAR historical image, and output a rescaling result of the historical data.

[0043] The SAR historical data rescaling method and system based on cross data of different satellites in the embodiment can solve the difference in imaging resolution between different loads by collecting spaceborne SAR historical image data of the same wave band and different satellites, can expand the data source and application range of historical data radiation rescaling by constructing a rescaling cross data pair after preprocessing, terrain correction, and cutting and registration of the spaceborne SAR rescaling data pair, and can improve the accuracy of spaceborne SAR historical data rescaling by using a Gaussian mixture model to perform clustering fitting, and by performing weighted fitting on different data pairs to solve the scaling coefficient of the historical data to be rescaled. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 FIG. 1 is an application environment diagram of the SAR historical data rescaling method based on cross data of different satellites in one embodiment;

[0045] Figure 2 FIG. 2 is a flowchart of the SAR historical data rescaling method based on cross data of different satellites in one embodiment;

[0046] Figure 3 FIG. 3 is a diagram of a selected rescaling historical data pair region after cutting in one embodiment;

[0047] Figure 4 FIG. 4 is a specific flowchart of solving the scaling coefficient of historical data based on a Gaussian mixture model in one embodiment;

[0048] Figure 5 FIG. 5 is a result diagram of a high-frequency region of a rescaling data pair fitted based on a Gaussian mixture model in one embodiment;

[0049] Figure 6 FIG. 6 is a flowchart of the SAR historical data rescaling method based on cross data of different satellites in another embodiment;

[0050] Figure 7 FIG. 7 is a structural block diagram of the SAR historical data rescaling system based on cross data of different satellites in one embodiment;

[0051] Figure 8 FIG. 8 is an internal structure diagram of the computer device in one embodiment. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0053] The SAR historical data recalibration method based on heterogeneous satellite crossover provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Figure 1 As shown, the application environment includes a computer device 110. The computer device 110 can collect calibrated spaceborne SAR historical image data and to-be-calibrated spaceborne SAR historical image data of different satellites in the same band, extract data using the same polarization mode from the calibrated spaceborne SAR historical image data and to-be-calibrated spaceborne SAR historical image data as spaceborne SAR recalibration data pairs; the computer device 110 can preprocess the spaceborne SAR recalibration data pairs, perform terrain correction on the preprocessed spaceborne SAR recalibration data pairs, and perform latitude and longitude clipping on the terrain-corrected calibrated spaceborne SAR historical image data and to-be-calibrated spaceborne SAR historical image data. SAR image registration methods are used to perform image registration and generate recalibrated historical data pairs. The computer device 110 can perform resolution resampling on the recalibrated historical data pairs and perform incident angle correction on recalibrated historical data pairs with incident angle differences to construct recalibrated cross-data pairs. The computer device 110 can input the recalibrated cross-data pairs into a Gaussian mixture model, fit the recalibrated cross-data pairs using the Gaussian mixture model, and calculate calibration coefficients for the SAR historical images. The computer device 110 can verify the calibration coefficients for the SAR historical images and output historical data recalibration results. The computer device 110 can be, but is not limited to, various personal computers, laptop computers, smartphones, robots, unmanned aerial vehicles, and other devices.

[0054] In one embodiment, Figure 2 As shown, a SAR historical data recalibration method based on alien satellite crossover is provided, comprising the following steps:

[0055] Step 202 : collect calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data from different satellites in the same band, and extract data using the same polarization mode from the calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data as spaceborne SAR recalibration data pairs.

[0056] The computer device can extract recalibration data pairs from satellites with similar SAR payloads in the same band to obtain uniform and stable reference target data for SAR radiometric cross-recalibration. Specifically, in this embodiment, the computer device can obtain historical SAR image data from both standard calibrated and uncalibrated satellite-borne SAR images to select imaging areas and cross-image data pairs that meet the requirements from two satellites with different payloads in the same band.

[0057] In one embodiment, a SAR historical data recalibration method based on heterosatellite crossover is provided, which may also include a process of collecting spaceborne SAR historical image data. The specific process includes: collecting calibrated spaceborne SAR historical image data and spaceborne SAR historical image data to be calibrated, which have the same coverage area, the same imaging ascending and descending orbits, and imaging time intervals within a threshold range under different satellites in the same band.

[0058] The computer device can acquire a set of historical SAR imagery data, including calibrated spaceborne SAR imagery data and uncalibrated spaceborne SAR imagery data. The calibrated spaceborne SAR imagery data and uncalibrated spaceborne SAR imagery data come from different SAR satellites operating in the same band, such as the C-band Sentinel-1 and Gaofen-3. The calibrated spaceborne SAR imagery data must undergo high-precision radiometric calibration to reduce subsequent errors introduced by errors in the standard data observations.

[0059] In this embodiment, the calibrated and uncalibrated historical spaceborne SAR imagery data cover the same area and contain uniformly distributed targets, such as urban areas, bare land, deserts, or rainforests. They have the same ascending and descending orbits and are imaged with an interval of less than 60 days. If the intervals are very close, within a week, and the selected target features are relatively stable, such as saline-alkali land or forests, they can also be used as a spaceborne SAR recalibration data pair.

[0060] After collecting calibrated and uncalibrated historical spaceborne SAR imagery data, the computer can extract data in the same polarization mode for SAR radiometric cross-recalibration. The calibrated and uncalibrated historical spaceborne SAR imagery data must be in the same polarization mode to reduce errors in subsequent radiometric cross-calibration due to factors such as different polarization channels and polarization modes, ensuring that the recalibration results are closer to the true values ​​of the ground objects.

[0061] For example, in this embodiment, the main data used by the computer device is the historical image data obtained by the C-band satellite-borne SAR satellites Sentinel-1A and GF-3A. Both satellites are in-orbit satellites with high load performance and imaging quality, and have similar sensor parameters, are comparable, and have certain intercommunication capability on data. During the work around the earth, both satellites irradiate most of the earth's objects and regions, and the detection areas of the two satellites have many overlapping parts. At the same time, the revisit period of both satellites is less than one month, and the data acquisition time is also feasible, so that multiple sets of historical cross-orbit data pairs with small time difference, same irradiation area and same polarization mode can be selected. On historical data, both satellites have good data conditions. Sentinel-1A will perform radiation performance monitoring every year, and regularly issue quality reports, with an absolute radiation accuracy of 1 dB. GF-3A, as China's first C-band multi-polarization and multi-mode SAR satellite, also has high radiation performance, and the China Resource Satellite Center will perform field calibration experiments every year, with an absolute radiation accuracy of 1.5 dB. The specific parameters of the sensors of the two satellites are shown in the following table:

[0062]

[0063] Among them, in data selection, data with the same polarization mode needs to be selected, and since the dual polarization modes of Sentinel-1A and GF-3A are completely different, the data selected are Sentinel-1A dual polarization GRD data (GRD-VV+VH) and GF-3A L1A full polarization (QPSI mode) data (SLC-HH+VV+VH+HV), and the computer device can extract the data corresponding to the same polarization (VV) mode collected by both, for subsequent radiation re-calibration processing. Among them, the GRD data of Sentinel does not contain phase information, so the L1A SLC data of GF-3A also needs to be pre-processed to obtain intensity values, such as Figure 3 The specific parameters of the selected experimental area are shown in the following table:

[0064]

[0065] Step 204, pre-processing the satellite-borne SAR re-calibration data pair, and terrain correction of the pre-processed satellite-borne SAR re-calibration data pair, after the terrain correction of the calibrated satellite-borne SAR historical image data and the to-be-calibrated satellite-borne SAR historical image data, the image registration is performed using the SAR image registration method, and the re-calibration historical data pair is generated.

[0066] The computer device can preprocess the spaceborne SAR recalibration data pairs to achieve registration and alignment of SAR data from different satellites. Specifically, the computer device performs the process of obtaining historical data radiometric recalibration data regions for the target area selected in step 202. This preprocessing is necessary for the subsequent production of recalibration cross data pairs, achieving registration and cropping of historical image data acquired from two spaceborne SAR satellites with the same wavelength band but different payloads.

[0067] Specifically, in one embodiment, a SAR historical data recalibration method based on heterosatellite crossover may further include a data preprocessing process, which specifically includes: performing the same preprocessing operation on the data in the spaceborne SAR recalibration data pair; wherein the preprocessing operation includes thermal noise removal, multi-look processing, speckle filtering, and radiation correction.

[0068] In this embodiment, the computer device pre-processes the calibrated spaceborne SAR historical image data and the to-be-calibrated spaceborne SAR historical image data to eliminate differences between different satellites.

[0069] The computer equipment can perform the same preprocessing on the selected historical image data pairs that are uniform and stable, have the same ascending and descending orbits, similar imaging times, and the same polarization mode, thereby obtaining the backscatter coefficient value σ of the calibrated spaceborne SAR historical image data and the image digital quantization value DN of the uncalibrated spaceborne SAR historical image data. The preprocessing operations mainly include: thermal noise removal, multi-look processing, speckle filtering, and radiation correction, among which:

[0070] Thermal noise removal is to remove the noise caused by the sensor system to improve data accuracy;

[0071] Multi-look processing is to average the azimuth and range directions of the single-look complex (SLC) image, and the result is the intensity data after multi-look;

[0072] Speckle filtering is to eliminate the influence of speckle in image data on subsequent processing of SAR images;

[0073] Radiometric correction is the process of converting the digital quantization values ​​of calibrated spaceborne SAR historical image data into corresponding backscatter coefficient values ​​to facilitate subsequent calibration processing.

[0074] The computer equipment performs multi-look processing and speckle filtering, primarily preprocessing the two sets of acquired historical spaceborne SAR satellite data. To ensure uniform preprocessing, the preprocessing process uses a unified process and parameters: filtering preprocessing is performed on Sentinel-1A GRD data, and multi-look processing and filtering preprocessing are performed on GF-3A SLC data.

[0075] In one embodiment, a provided SAR historical data recalibration method based on hetero-satellite crossover may further include a terrain correction process, specifically comprising: performing terrain correction processing on the pre-processed satellite-borne SAR recalibration data using a geocoding method in the same coordinate system, centering the pre-processed satellite-borne SAR recalibration data, and ensuring that image data from different payloads have the same geographic information at corresponding pixel points.

[0076] The computer can use geocoding in the same coordinate system to perform terrain correction on the calibrated historical data and the uncalibrated historical data from the spaceborne SAR recalibration data pair. This ensures that the image data from different payloads have the same geographic information at corresponding pixels, eliminating the range deformation caused by the side-view imaging of the spaceborne SAR satellite. Specifically, the computer can use the digital elevation model (DEM) data downloaded from the USGS to perform unified terrain correction on the image data obtained by preprocessing the historical data from the two satellites, ensuring that the geographic data used is the same.

[0077] In one embodiment, a method for recalibrating SAR historical data based on alien satellite crossover is provided, which may also include the process of longitude and latitude clipping and calibration data pair registration. Specifically, the computer equipment performs longitude and latitude clipping on the calibrated and uncalibrated historical data after terrain correction to ensure the mapping accuracy of the data pair. The cropping area should be selected from areas in the image that are uniform, stable, and have a large backscatter coefficient, mainly including: bare land, Gobi, rainforest, saline-alkali land or forest, etc. Subsequently, the SAR image registration method is used to perform image registration to achieve matching of the calibrated historical data and the uncalibrated historical data, and generate a recalibrated cross data pair containing multiple land feature type data slices.

[0078] Specifically, the computer equipment can perform longitude and latitude cropping on the image data obtained after preprocessing and geocoding. After the two sets of calibration data are cropped with the same longitude and latitude, the SAR scale-invariant feature transformation method is used to obtain the image pair matching feature points and perform recalibration data pair registration. Two pairs of data slices of different ground object types are extracted from the overlapping area of ​​the two sets of images. The detailed parameters of each set of calibration data pairs are shown in the following table:

[0079]

[0080] In this embodiment, the selected recalibration historical data pair region diagram after clipping is as follows: Figure 3 As shown. The recalibrated data pairs 1 and 2 correspond to Figure 3 The two regions in (b) correspond to the recalibrated data pairs 3 and 4 respectively. Figure 3 In the two regions of (d), the cropped area is not obvious because the Sentinel-1A dual-polarization imaging area is large. Figure 3The corresponding data blocks are not marked in the schematic diagram.

[0081] Step 206 : performing resolution resampling processing on the recalibrated historical data pairs, and performing incident angle correction on the recalibrated historical data pairs with incident angle differences, to construct recalibrated cross data pairs.

[0082] Computer equipment can resample the resolution of recalibrated historical data pairs to eliminate the effects of resolution differences. Specifically, the computer equipment uses the preprocessed and registered historical image data output to resample the resolution according to the imaging resolutions of the two satellites. It then corrects the incidence angles of data pairs with incident angle differences, reconstructing one or more sets of cross-data pairs that meet the requirements for recalibration as input for recalibration coefficients using a Gaussian mixture model. Finally, a two-dimensional Gaussian mixture model is used to fit the calibration coefficients for radiometric recalibration of the historical spaceborne SAR data to be calibrated. These coefficients are then compared with standard values ​​to verify the accuracy of the recalibration results.

[0083] Specifically, in one embodiment, a SAR historical data recalibration method based on alien satellite crossing is provided, which may also include a resolution resampling process, wherein the specific process includes: using the lower resolution of the recalibrated historical data pair as a resampling benchmark; based on the resampling benchmark, performing weighted calculation on each pixel point of the image in the recalibrated historical data pair through a downsampling function to obtain a new pixel value.

[0084] The computer can resample the resolution of each pair of calibrated and uncalibrated historical imagery based on the results obtained after longitude and latitude cropping and registration. This ensures that the resolution differences of historical data from different satellites do not directly affect the calibration results during the historical data radiometric cross-calibration process, thereby improving the accuracy of the calibration results. The computer resamples each set of recalibrated data pairs to maintain consistent resolution among the historical SAR data from different payloads, eliminating resolution effects for the next step of data pair construction. For example, each set of GF-3A data can be downsampled to 15m resolution, resulting in imagery with the same resolution as Sentinel-1A.

[0085] During the resampling process, a downsampling process is adopted, and the lower resolution of the two is used as the resampling benchmark to avoid introducing new values ​​and retain the original scattering characteristics of the ground object. For example, the resolution of the spaceborne SAR satellite 1 image is P1, and the corresponding pixel value is I(x,y). The resolution of the spaceborne SAR satellite 2 image is P2, and the corresponding pixel value is I1(x,y). Assuming that the P2 value is less than the P1 value (that is, P2 corresponds to high-resolution image data), the high-resolution image data P1 is downsampled to P2 using the downsampling interpolation method, and the downsampling is obtained by I′(x′,y′). The downsampling kernel function formula is expressed as:

[0086] For each pixel in the image, the computer device can use the downsampling kernel function to weight the surrounding pixels and calculate the new pixel value. The specific steps include: positioning calculation, determining the position of the pixel in the target image in the source image. For scaling operations, this involves calculating the scaling factor; weight calculation, calculating the weight of the target pixel and the neighboring pixels in the source image based on the downsampling kernel function; weighted summation: The values ​​of the neighboring pixels in the source image are weighted and summed to obtain the new value of the target pixel. The calculation formula is expressed as: Wherein, I(x, y) is the pixel value of the source image, I′(x′, y′) is the pixel value of the target image, L(x) is the downsampling kernel function, and a is the window size. In this embodiment, the value of a can be 3.

[0087] In this embodiment, the computer device makes the historical image data from different satellites in the same band have the same resolution through the resampling process, so as to facilitate the construction of recalibration cross data pairs and eliminate the calibration error introduced by inconsistent imaging resolution.

[0088] In one embodiment, a provided SAR historical data recalibration method based on alien satellite crossing may further include a process of performing an incident angle correction, specifically comprising: calculating an incident angle corresponding to a pair of recalibrated historical data; if the difference between the incident angle and a reference incident angle is within a threshold range, then no incident angle difference exists; if the difference between the incident angle and the reference incident angle exceeds the threshold range, then determining that an incident angle difference exists, and performing correction using an incident angle correction model.

[0089] Among them, the computer equipment can determine whether there is an incident angle difference in the historical data and recalibrated data. If there is an incident angle difference, it is handled in two cases: if the incident angle difference in the selected area is small, for example, less than 1 degree, the incident angle correction step is directly ignored; if it is large, an incident angle correction model is required for correction, wherein the incident angle correction model can be an Oh model, a Lambertian model, a cosine model, etc.

[0090] In this embodiment, the selection of the incident angle model should comply with the data characteristic requirements, accuracy requirements, and precision and computational complexity requirements, wherein the data characteristic requirements are: different surface types (such as water bodies, vegetation, cities, and bare soil) have different response characteristics to the incident angle, and the selected correction model should be able to accurately describe the reflection characteristics of the target surface; precision requirements: select an appropriate model according to the application requirements. If high-precision surface information is required (such as topographic measurement, fine classification), a complex physical model (such as the Oh model) can be used. If only a rough correction is required for easy observation, a simple empirical model (such as the cosine model) can be used; precision and computational complexity requirements: for real-time processing requirements (such as real-time monitoring), the cosine model with simple calculation and fast speed is more suitable.

[0091] In this embodiment, the computer equipment can perform incident angle correction based on the incident angle difference of each group of recalibrated cross pairs. During the experiment, the incident angle deviation of the four image pairs that can be selected does not exceed 0.2°, and the impact of the incident angle on the historical data recalibration process can be ignored; if a large incident angle difference value occurs, it is necessary to perform incident angle correction on the selection rules of the incident angle model according to the specific situation.

[0092] In one embodiment, a provided SAR historical data recalibration method based on alien satellite crossover may further include a process of constructing a recalibrated crossover data pair, the specific process including: extracting a first pixel value corresponding to the uncalibrated historical data in the recalibrated historical data pair, and using the first pixel value as an independent variable of the recalibrated crossover data pair; extracting a second pixel value corresponding to the downsampled calibrated standard historical data in the recalibrated historical data pair, and using the second pixel value as a dependent variable of the recalibrated crossover data pair; combining the independent variable and the dependent variable to form a two-dimensional data pair, and using the two-dimensional data pair as data corresponding to the recalibrated crossover data pair to construct the recalibrated crossover data pair.

[0093] Among them, by resampling and correcting the incident angle of the recalibrated cross data pair after registration, we get a recalibrated historical data pair that is almost identical except for the load effect. Each data pair contains a backscatter coefficient data σ of a calibrated image. cal and the image digital quantization value data DN of an image to be calibrated uncal During the data pair construction process, the computer device can construct the obtained pair of image data into (DN uncal ,σ cal ) in the form of , so that the subsequent Gaussian mixture model can solve the calibration coefficient k of the historical image to be calibrated.

[0094] Specifically, in this embodiment, the recalibration cross data pair construction process may include:

[0095] A pair of n×n uncalibrated historical data and calibrated historical data with the same resolution and pixel size is regarded as a cross-calibration data pair (referred to as cross-data pair);

[0096] Extract the historical data to be calibrated I1(x i ,y i ) corresponds to the first pixel value (DN uncal ) as the independent variable x of the recalibrated cross-data pair;

[0097] Extract the calibrated standard historical data I′(x i ′,y i ′) corresponds to the second pixel value (σ cal ) as the dependent variable y of the recalibrated cross data pair;

[0098] The extracted first pixel value and second pixel value are combined to construct a (x, y) two-dimensional array as the corresponding data at (i, i) of the recalibrated cross data pair, which serves as the input for the Gaussian mixture model to calculate the calibration coefficient of the historical data image to be calibrated in the next step.

[0099] In this embodiment, the computer device can extract the backscatter coefficient (σ) of the calibrated Sentinel-1A historical data from each data pair after registration and resampling. cal ) image as the dependent variable, and extract the digital quantization value (DN) of the GF-3A historical data to be calibrated. uncal ) as the independent variable, construct the two-dimensional cross data (DN uncal , σ cal ), forming the standard data input for solving the radiation recalibration coefficients using the subsequent Gaussian mixture model.

[0100] In step 208 , the recalibrated cross data pairs are input into a Gaussian mixture model, and the recalibrated cross data pairs are fitted by the Gaussian mixture model to calculate the calibration coefficients of the SAR historical image.

[0101] Computer equipment can use the Gaussian mixture model clustering algorithm instead of the traditional least squares method to fit the cross-data pairs, extract the scattering characteristics of the high-frequency area, eliminate the influence of other types of ground objects and noise, and finally calculate the calibration coefficients of historical SAR images.

[0102] In one embodiment, a provided SAR historical data recalibration method based on alien satellite crossing may further include a process of calculating a calibration coefficient of the SAR historical image. The specific process includes: inputting the recalibrated cross data pairs into a Gaussian mixture model, extracting the high-frequency region in each two-dimensional data pair, and calculating the interquartile range of the two-dimensional data pair; based on the high-frequency region, calculating the bin width using the Freedman-Diaconis rule, and obtaining the high-frequency region data by using the joint distribution of two-dimensional histogram statistical variables; if the recalibrated cross data pairs are data pairs of different types of regions, fitting the Gaussian mixture model of each two-dimensional Gaussian distribution using the high-frequency region data, and initializing the initial parameters of the Gaussian mixture model using the K-means method; fitting the Gaussian mixture model to the recalibrated cross data pairs to calculate the calibration coefficient of the SAR historical image.

[0103] In this embodiment, if Figure 4 As shown in FIG, the process of calculating the calibration coefficient of the SAR historical image may specifically include:

[0104] The obtained two-dimensional recalibrated cross data pairs are used as input to extract the high-frequency area in each two-dimensional cross data pair; extracting the high-frequency area means extracting the corresponding (DN uncal , σ cal ) value, retaining the scattering characteristics of the main uniform and stable objects as much as possible and excluding the influence of other types of objects and noise;

[0105] Calculate the interquartile range (IQR) of a two-dimensional data pair. The calculation formula is: σ cal,IQR =IQR(σ cal );DN uncal,IQR =IQR(DN uncal ); IQR = Q3 - Q1; where Q3 is the third quartile and Q1 is the first quartile;

[0106] Use the Freedman-Diaconis rule to calculate the bin width: the calculation formula can be expressed as: cal,binwidth =2·σ cal,IQR ·n -1 / 3 DN uncal,binwidth =2·DN uncal,IQR ·n -1 / 3 ; where n is the number of data points; the number of bins is calculated based on the bin width:

[0107] Use a two-dimensional histogram to count the joint distribution of two variables and find the highest-frequency region and its index in the group. If the amount of data in the highest-frequency region obtained is small, its boundary can be calculated based on the index of the high-frequency region, and the high-frequency region can be expanded by an expansion factor to the corresponding area located in the middle 50% of the data volume.

[0108] When multiple data pairs of different types of regions are input, using these high-frequency region data, it is necessary to fit multiple two-dimensional Gaussian mixture models (GMM): Among them, π i is the weight of the i-th Gaussian distribution, μ i is the mean, Σ i is the covariance matrix;

[0109] In one embodiment, the process of calculating the calibration coefficient of the SAR historical image may further include: fitting the recalibrated cross data pair with a Gaussian mixture model to extract the corresponding expectation and Gaussian weight of the Gaussian distribution in the high-frequency region; and multiplying the Gaussian weight by the expectation ratio corresponding to the Gaussian weight to obtain the calibration coefficient of the SAR historical image.

[0110] Specifically, the computer device can use the K-means method to initialize the initial parameters of the Gaussian mixture model, and then fit the Gaussian mixture model to the cross data pair to extract multiple corresponding expectations and weights of the Gaussian distribution in the high-frequency area used; the K-means initialization parameter process can be expressed as: mean μ K : The initialization mean of GMM is the Kmeans cluster center; μ k =K-means cluster center (k=1,2,...,K); covariance matrix Σ k : For each cluster, calculate the covariance matrix of the data points and use it as the initial covariance matrix of the cluster: Weight π k : The initial weight of GMM is the ratio of the number of data points in each cluster to the total number of data points Among them, N k is the number of data points in the kth cluster, and N is the total number of data points.

[0111] Then, the computer device can fit a two-dimensional Gaussian mixture model to each two-dimensional data pair input, extracting the mean and weight corresponding to each Gaussian distribution. The corresponding Gaussian weight is used to calculate the expected backscattering coefficient μ of the corresponding two-dimensional Gaussian distribution. i (σ cal ) and the digital quantization value expected DN uncal The historical SAR image calibration coefficients calculated using the two-dimensional Gaussian mixture model are multiplied by the ratio of

[0112]

[0113] Specifically, in this embodiment, the computer device can use the obtained multiple pairs of two-dimensional arrays as data input to recalibrate the historical spaceborne SAR data to be calibrated; the two pairs of recalibrated image pairs corresponding to the two sets of image data are used as inputs of the two-dimensional Gaussian mixture model, and the model is used to fit the Gaussian mixture model to all the input data. The fitting results are shown in the figure. Figure 5 (c) As shown; then according to the four-bin criterion, the high-frequency data area is extracted, and then the extracted high-frequency data area (accounting for about 50% of the total data volume) is fitted with a two-dimensional Gaussian mixture model, as shown in Figure 5 (d) and solve the weight value and mean of the corresponding data pair, combining the weight and backscattering coefficient mean μ obtained by combining the two pairs of fitting data used in each group i (σ cal ) and the digital quantization value mean μ i (DN uncal ) ratio to obtain the radiometric recalibration coefficient of the alien SAR historical data based on the Gaussian mixture model. The detailed solution results of each historical data pair based on the Gaussian mixture model are shown in the following table:

[0114]

[0115]

[0116] Step 210: Verify the calibration coefficients of the historical SAR image and output the historical data recalibration results.

[0117] The computer equipment can use the historical SAR image calibration coefficient k GMM高频区域 and the digital quantization value μ of the historical spaceborne SAR image data to be calibrated i (DN uncal ) are multiplied to obtain the result value of the recalibrated backscatter coefficient; then the reference backscatter coefficient value is calculated using the existing or officially nominal calibration coefficients of the satellite to be recalibrated; finally, the root mean square error (RMSE) is solved for each pixel. If the calculated RMSE meets the design accuracy of the satellite's on-orbit radiometric calibration, the historical data recalibration result is output: Among them, σ i is the backscatter coefficient value calculated based on the existing or official nominal calibration coefficients of the satellite to be calibrated, The backscatter coefficient value is recalibrated based on the historical data calculated by the present invention.

[0118] If they do not meet the requirements, the computer equipment can compare the average backscatter coefficient of the same type of ground object with the calibrated satellites in the similar time. If the error between the calculated average backscatter coefficient and the average of the same type of ground objects is less than the design accuracy of the satellite's on-orbit radiation calibration, the historical data recalibration result is output. Otherwise, the recalibration result is rejected, and the data pair selection, preprocessing and recalibration process are repeated.

[0119] Specifically, in this embodiment, the computer device can use the obtained calibration coefficient to recalibrate the historical image data to be calibrated, and obtain recalibrated backscatter coefficient image data based on the historical image data; use the recalibrated data and; compare the standard backscatter coefficient image data directly obtained by the provided calibration coefficient to calculate the root mean square error between the theoretical backscatter coefficient and the recalibrated backscatter coefficient result; then use the classical least squares method to solve and compare with the results in this embodiment.

[0120] The results show that this embodiment establishes a recalibration process for historical spaceborne SAR data with different payloads in the same band to address differences in resolution, incident angle, etc. between satellite data with different payloads. In the final solution of the calibration coefficient, a clustering method based on a mixed Gaussian model is used to perform weighted fitting on multiple recalibrated data pairs to obtain the recalibration coefficient. The recalibration accuracy meets the GF-3A radiometric calibration accuracy requirement (<1.5dB) and is significantly better than the current mainstream least squares solution method in terms of effect, as shown in the following table:

[0121]

[0122] In one embodiment, Figure 6 As shown in the figure, a SAR historical data recalibration method based on heterogeneous satellite crossover is provided, which mainly includes three parts: historical data acquisition, historical data recalibration data preprocessing, and historical data crossover recalibration.

[0123] Historical data acquisition: used to select imaging areas and cross-image data pairs that meet the requirements of two satellites with the same band but different payloads;

[0124] Historical data recalibration data pair preprocessing: Provides the process of obtaining historical data radiation recalibration data area for the target area selected in S1, performs necessary preprocessing for the subsequent production of recalibration cross data pairs, and realizes the registration and cropping of historical image data obtained from two spaceborne SAR satellites with the same band but different payloads;

[0125] Historical data cross-recalibration part: The output pre-processed and aligned historical image data is used to resample the resolution according to the imaging resolution of the two satellites, and the incident angle is corrected for the data pairs with different incident angles. One or more sets of recalibrated cross-data pairs that meet the requirements are reconstructed as input for solving the recalibration coefficients based on the Gaussian mixture model method. Finally, the two-dimensional Gaussian mixture model is used to fit the calibration coefficients for the radiation recalibration of the historical satellite-borne SAR data to be calibrated, and the coefficients are compared with the standard values ​​to verify the accuracy of the recalibration results.

[0126] It should be understood that, although the various steps in the above-mentioned flowcharts are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the above-mentioned flowcharts may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0127] In one embodiment, Figure 7 As shown, a SAR historical data recalibration system based on alien satellite crossover is provided, comprising: a data acquisition module 710, a data preprocessing module 720, a data pair construction module 730, a calibration coefficient calculation module 740 and a result verification module 750, wherein:

[0128] The data acquisition module 710 is configured to acquire calibrated historical spaceborne SAR image data and uncalibrated historical spaceborne SAR image data from different satellites in the same band, and extract data using the same polarization mode from the calibrated historical spaceborne SAR image data and uncalibrated historical spaceborne SAR image data as spaceborne SAR recalibration data pairs.

[0129] The data preprocessing module 720 is used to preprocess the spaceborne SAR recalibration data pair, perform terrain correction on the preprocessed spaceborne SAR recalibration data pair, perform longitude and latitude clipping on the terrain-corrected calibrated spaceborne SAR historical image data and the to-be-calibrated spaceborne SAR historical image data, and then perform image registration using a SAR image registration method to generate a recalibration historical data pair.

[0130] A data pair construction module 730 is configured to perform resolution resampling processing on the recalibrated historical data pairs, perform incident angle correction on the recalibrated historical data pairs with incident angle differences, and construct recalibrated cross data pairs;

[0131] The calibration coefficient calculation module 740 is used to input the recalibrated cross data pairs into the Gaussian mixture model, fit the recalibrated cross data pairs through the Gaussian mixture model, and calculate the calibration coefficients of the SAR historical image;

[0132] The result verification module 750 is used to verify the calibration coefficients of the SAR historical images and output the historical data recalibration results.

[0133] In one embodiment, the data acquisition module 710 is also used to collect calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data with the same coverage area, the same imaging ascending and descending orbits, and imaging time intervals within a threshold range under different satellites in the same band.

[0134] In one embodiment, the data preprocessing module 720 is further configured to perform the same preprocessing operation on the data in the spaceborne SAR recalibration data pair; wherein the preprocessing operation includes thermal noise removal, multi-look processing, speckle filtering, and radiometric correction.

[0135] In one embodiment, the data preprocessing module 720 is further used to perform terrain correction processing on the preprocessed spaceborne SAR recalibration data using a geocoding method under the same coordinate system, centering the preprocessed spaceborne SAR recalibration data so that the image data of different payloads have the same geographic information at corresponding pixel points.

[0136] In one embodiment, the data pair construction module 730 is further configured to use the lower resolution of the recalibrated historical data pair as a resampling benchmark; based on the resampling benchmark, a downsampling function is used to perform weighted calculation on each pixel point of the image in the recalibrated historical data pair to obtain a new pixel value.

[0137] In one embodiment, the data pair construction module 730 is further used to calculate the incident angle corresponding to the recalibrated historical data pair. If the difference between the incident angle and the reference incident angle is within a threshold range, then there is no incident angle difference; if the difference between the incident angle and the reference incident angle exceeds the threshold range, then it is determined that there is an incident angle difference, and correction is performed using the incident angle correction model.

[0138] In one embodiment, the data pair construction module 730 is further used to extract the first pixel value corresponding to the historical data to be calibrated in the recalibrated historical data pair, and use the first pixel value as the independent variable of the recalibrated cross data pair; extract the second pixel value corresponding to the calibrated standard historical data after downsampling in the recalibrated historical data pair, and use the second pixel value as the dependent variable of the recalibrated cross data pair; combine the independent variable and the dependent variable to form a two-dimensional data pair, and use the two-dimensional data pair as the data corresponding to the recalibrated cross data pair to construct the recalibrated cross data pair.

[0139] In one embodiment, the calibration coefficient calculation module 740 is further used to input the recalibrated cross data pairs into a Gaussian mixture model, extract the high-frequency area in each two-dimensional data pair, and calculate the interquartile range of the two-dimensional data pair; based on the high-frequency area, the Freedman-Diaconis rule is used to calculate the bin width, and the joint distribution of the two-dimensional histogram statistical variables is used to obtain the high-frequency area data; if the recalibrated cross data pairs are data pairs of different types of areas, the high-frequency area data is used to fit the Gaussian mixture model of each two-dimensional Gaussian distribution, and the K-means method is used to initialize the initial parameters of the Gaussian mixture model; the Gaussian mixture model is fitted to the recalibrated cross data pairs to calculate the calibration coefficient of the SAR historical image.

[0140] In one embodiment, the calibration coefficient calculation module 740 is further used to fit a Gaussian mixture model to the recalibrated cross data pair, extract the corresponding expectation and Gaussian weight of the Gaussian distribution in the high-frequency area; and multiply the Gaussian weight and the expected ratio corresponding to the Gaussian weight to obtain the calibration coefficient of the SAR historical image.

[0141] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a SAR historical data recalibration method based on alien satellite intersection is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0142] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0143] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of a SAR historical data recalibration method based on alien satellite crossing when executing the computer program.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of a SAR historical data recalibration method based on alien satellite crossing are implemented.

[0145] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0146] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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.

[0147] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A SAR historical data recalibration method based on heterogeneous satellite crossover, characterized in that: The method comprises: Collecting calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data from different satellites in the same band, and extracting data using the same polarization mode from the calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data as spaceborne SAR recalibration data pairs; Preprocessing the spaceborne SAR recalibration data pair, performing terrain correction on the preprocessed spaceborne SAR recalibration data pair, performing longitude and latitude clipping on the terrain-corrected calibrated spaceborne SAR historical image data and the to-be-calibrated spaceborne SAR historical image data, and then performing image registration using a SAR image registration method to generate a recalibrated historical data pair; performing resolution resampling processing on the recalibrated historical data pairs, and performing incident angle correction on the recalibrated historical data pairs having incident angle differences, to construct recalibrated cross data pairs; Inputting the recalibrated cross data pairs into a Gaussian mixture model, fitting the recalibrated cross data pairs through the Gaussian mixture model, and calculating calibration coefficients of SAR historical images; The calibration coefficients of the SAR historical images are verified, and the historical data recalibration results are output.

2. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 1 is characterized in that: Collect calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data from different satellites in the same band, including: The calibrated spaceborne SAR historical image data and the spaceborne SAR historical image data to be calibrated are collected from different satellites in the same band with the same coverage area, the same imaging ascending and descending orbits, and imaging time intervals within the threshold range.

3. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 1 is characterized in that: Preprocessing the spaceborne SAR recalibration data includes: performing the same preprocessing operation on the data in the spaceborne SAR recalibration data pair; The preprocessing operations include thermal noise removal, multi-view processing, speckle filtering, and radiation correction.

4. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 1 is characterized in that: Perform terrain correction on the pre-processed spaceborne SAR recalibration data, including: The pre-processed spaceborne SAR recalibration data are subjected to terrain correction processing using a geocoding method under the same coordinate system, and the pre-processed spaceborne SAR recalibration data are aligned so that the image data of different payloads have the same geographic information at corresponding pixel points.

5. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 1 is characterized in that: Performing resolution resampling processing on the recalibrated historical data pair includes: using a lower resolution of the recalibrated historical data pair as a resampling benchmark; Based on the resampling benchmark, a weighted calculation is performed on each pixel point of the image in the recalibrated historical data pair through a downsampling function to obtain a new pixel value.

6. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 1 is characterized in that: Performing incident angle correction on the recalibrated historical data pair having incident angle differences, comprising: calculating an incident angle corresponding to the recalibration historical data pair, and if a difference between the incident angle and a reference incident angle is within a threshold range, then no incident angle difference exists; If the difference between the incident angle and the reference incident angle exceeds the threshold range, it is determined that there is an incident angle difference, and an incident angle correction model is used for correction.

7. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 1 is characterized in that: Construct recalibration cross-data pairs, including: Extracting a first pixel value corresponding to the historical data to be calibrated in the recalibrated historical data pair, and using the first pixel value as an independent variable of the recalibrated cross data pair; Extracting a second pixel value corresponding to the downsampled calibrated standard historical data in the recalibrated historical data pair, and using the second pixel value as a response variable of the recalibrated cross data pair; The independent variables and the dependent variables are combined to form a two-dimensional data pair, and the two-dimensional data pair is used as data corresponding to the recalibrated cross data pair to construct the recalibrated cross data pair.

8. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 7 is characterized in that: Inputting the recalibrated cross data pair into a Gaussian mixture model, fitting the recalibrated cross data pair using the Gaussian mixture model, and calculating the calibration coefficient of the SAR historical image, including: Inputting the recalibrated cross-data pairs into a Gaussian mixture model, extracting the high-frequency region in each of the two-dimensional data pairs, and calculating the interquartile range of the two-dimensional data pairs; Based on the high-frequency area, the Freedman-Diaconis rule is used to calculate the bin width, and the joint distribution of the two-dimensional histogram statistics variables is used to obtain the high-frequency area data; If the recalibrated cross-data pairs are data pairs of different types of regions, then the high-frequency region data is used to fit the Gaussian mixture model of each two-dimensional Gaussian distribution, and the K-means method is used to initialize the initial parameters of the Gaussian mixture model; Gaussian mixture model fitting is performed on the recalibrated cross data pairs to calculate the calibration coefficients of the SAR historical images.

9. The SAR historical data recalibration method based on heterogeneous satellite crossover according to claim 8, characterized in that: The recalibrated cross data pairs are fitted with a Gaussian mixture model to calculate the calibration coefficients of the SAR historical image, including: Fitting the recalibrated cross-data pair with a Gaussian mixture model to extract the corresponding expectation and Gaussian weight of the Gaussian distribution in the high-frequency region; The Gaussian weight and the expected ratio corresponding to the Gaussian weight are multiplied to obtain a calibration coefficient of the SAR historical image.

10. A SAR historical data recalibration system based on heterogeneous satellite crossover, characterized in that: The system comprises: a data acquisition module for collecting calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data from different satellites in the same band, and extracting data using the same polarization mode from the calibrated spaceborne SAR historical image data and uncalibrated spaceborne SAR historical image data as spaceborne SAR recalibration data pairs; a data preprocessing module, configured to preprocess the spaceborne SAR recalibration data pair, perform terrain correction on the preprocessed spaceborne SAR recalibration data pair, perform longitude and latitude clipping on the terrain-corrected calibrated spaceborne SAR historical image data and the to-be-calibrated spaceborne SAR historical image data, and then perform image registration using a SAR image registration method to generate a recalibration historical data pair; a data pair construction module, configured to perform resolution resampling processing on the recalibrated historical data pairs, and perform incident angle correction on the recalibrated historical data pairs having incident angle differences, to construct recalibrated cross data pairs; a calibration coefficient calculation module, configured to input the recalibrated cross data pairs into a Gaussian mixture model, fit the recalibrated cross data pairs through the Gaussian mixture model, and calculate the calibration coefficients of the SAR historical image; The result verification module is used to verify the calibration coefficients of the SAR historical images and output the historical data recalibration results.

Images