A Method and System for Improving the Geometric Positioning Accuracy of Spaceborne SAR Ocean Images

Abstract

The present invention provides a method and system for improving the geometric positioning accuracy of spaceborne SAR ocean images. The method collects AIS data within the same imaging time and imaging area range as the SAR satellite, corrects the orbital position of the spaceborne SAR satellite according to the actual imaging time of the ship, and at the same time fuses the AIS data and the SAR satellite data to obtain the matching point pairs of the SAR image position and the AIS position of the ship. According to the optimized matching point pair results and the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship, the AIS position is used as a control point to correct the RD model error, and the new geographical coordinate information of each image pixel value is obtained by re-iterative solution. Finally, a system geometric correction image product with improved positioning accuracy is obtained, solving the problem of low positioning accuracy of ships relying solely on SAR images obtained by SAR satellites in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of spaceborne synthetic aperture radar (SAR) image processing, and particularly to a method and system for improving the geometric positioning accuracy of spaceborne SAR ocean images. Background Art

[0002] Spaceborne SAR (Synthetic Aperture Radar) is an active remote sensing device that uses radar imaging technology to monitor the sea surface, capable of obtaining high-resolution images and identifying the position and contour of ships. AIS (Automatic Identification System) is an automatic identification signal automatically sent by ships, containing information such as position and speed, but it depends on whether the ship's equipment is turned on and the signal coverage area. The relationship between the two is as follows:

[0003] (1) Ship Detection and Identity Verification

[0004] Wide-area monitoring by SAR: Spaceborne SAR can scan the sea surface over a large range and detect ships without AIS turned on (such as illegal fishing boats and smuggling boats), and these ships may evade supervision by turning off AIS.

[0005] Identity matching by AIS: Through the ship identity and trajectory data provided by AIS, the targets detected by SAR can be quickly verified whether they are legal ships, reducing misjudgments (such as distinguishing merchant ships from icebergs and sea clutter).

[0006] (2) Identification of Non-cooperative Targets (Dark Targets)

[0007] Discovering "invisible" ships: Some ships deliberately turn off AIS or forge information (such as illegal fishing and pirate activities), and SAR can independently discover these targets through radar echoes, filling the regulatory loopholes of AIS.

[0008] Data fusion to improve accuracy: By overlaying SAR images and AIS data, it can be verified whether the reported trajectory of the ship is true (for example: AIS shows the ship at point A, but SAR detects it at point B).

[0009] The target position accuracy in remote sensing images is one of the basic elements of target information. High-precision positioning information can significantly improve the application efficiency of remote sensing images. The error sources affecting the target positioning accuracy of spaceborne SAR images include orbit error, slant range measurement error, and elevation error. Orbit error mainly depends on the accuracy of the on-board navigation and positioning equipment; slant range measurement error is mainly affected by errors such as atmospheric propagation delay, sampling time error, and channel delay; elevation error is mainly the error of the target height relative to the surface of the earth model, mainly depending on the accuracy of the Digital Elevation Model (DEM) used in the processing system. Commercial spaceborne SAR satellites are restricted by factors such as satellite weight, equipment cost control, and lack of geometric calibration. In the absence of control points, the positioning accuracy of commercial spaceborne SAR images is usually only in the order of hundreds of meters, severely limiting their application efficiency in ocean observation.

[0010] Spaceborne SAR ground processing systems usually perform processing such as imaging processing, radiometric calibration, and system geometric calibration to obtain standardized image products at all levels. Among them, system geometric calibration (used to eliminate the inherent geometric distortion caused by the SAR imaging principle (slant range projection, Doppler frequency shift); SAR is based on slant range projection (non-vertical observation), and there are geometric deformations in the range direction (Range) and azimuth direction (Azimuth) in the original image (such as foreshortening, layover), and geometric calibration converts it to orthographic projection (map coordinate system)) processing usually uses the Range Doppler (RD) model to correct the image into a geocoded image and obtain the system geometric calibration data product. The RD model consists of three non-linear equations: the slant range equation, the Doppler center frequency equation, and the earth model. Using satellite ephemeris parameters and SAR processing parameters (the deviation between the actual orbit and the ideal orbit of the satellite (such as altitude, speed change), the fluctuations of the attitude angles (pitch, roll, yaw) will cause image distortion, and ephemeris and attitude data are needed for correction), the exact solution of the equations is obtained through iterative solution or using Ferrari's method, and the geographic coordinate position information of each pixel in the image is obtained to complete the production of the system geometric calibration product. The equations of the RD model are as follows:

[0011] Doppler equation: The Doppler center frequency between the SAR and the target can be obtained from the relative motion relationship between the SAR and the target. Since the Doppler center frequency used in the imaging processing is the same as that used in the geometric calibration equation, the influence of the Doppler center frequency on the positioning accuracy can be ignored.

[0012] (A)

[0013] Slant Range Equation: SAR has precise ranging capabilities. According to the sampling parameters, the slant range value of each sample can be accurately obtained. Errors such as the atmospheric propagation delay, sampling time error, and channel delay during the imaging task of spaceborne SAR are ultimately reflected in the slant range error.

[0014] (B)

[0015] Earth Model: The Earth can usually be approximated as an ellipsoid, and the target is a point on this ellipsoid. Based on this condition, the constraint equations for the three-dimensional position of the target can be obtained.

[0016] (C)

[0017] Among them, represents the Doppler center frequency, represents the microwave wavelength of spaceborne SAR, is the distance between the SAR and the ground target, and represent the position and velocity vectors of the SAR satellite, using the WGS84 geodetic coordinate system of GPS itself; and represent the position and velocity vectors of the ground target, usually also using the WGS84 geodetic coordinate system. and represent the semi-major axis and semi-minor axis of the ideal Earth ellipsoid model.

[0018] To improve the positioning accuracy of spaceborne SAR images, two methods, namely geometric calibration and orthorectification, are usually adopted. Geometric calibration is the process where the SAR satellite observes and images a ground calibration field multiple times. By using the data of high-precision control points (Ground Control Point, GCP) corresponding to accurately measured ground corner reflectors, geometric calibration coefficients are obtained to correct the systematic errors of the spaceborne SAR positioning model, thereby improving the uncontrolled positioning accuracy of the geometric calibration products of the spaceborne SAR system. Spaceborne SAR orthorectification, on the other hand, uses geometric models, DEM, and GCP data to correct various geometric errors and distortions during the imaging process, obtaining a more advanced orthorectified image product and further improving the geometric quality of the spaceborne SAR image. During the on-orbit operation of the spaceborne SAR satellite, each time an imaging mission is executed, it is inevitably affected by factors such as the space environment, equipment temperature, platform stability, and orbit control, resulting in the inability of the ground calibration coefficients to fully compensate for the positioning errors. In addition, the correction of atmospheric delay errors depends on atmospheric parameter products (European Centre for Medium-Range Weather Forecasts ECMWF and National Centers for Environmental Prediction NCEP in the United States). These products have time delays and uncertainties, and also have relatively large time delays themselves. Moreover, the measurement of actual atmospheric parameters requires a large amount of manpower and material resources, which is almost impossible for most commercial satellite companies to implement. Although the orthorectification method can correct geometric errors, it depends on high-precision GCP points and has strict requirements on the quantity and distribution of GCP points. In ocean scenes, it is difficult to obtain GCP points, making it difficult for the orthorectification method to meet the real-time processing requirements of the spaceborne SAR ground system. Summary of the Invention

[0019] The purpose of the present invention is to overcome the above technical deficiencies and provide a method and system for improving the geometric positioning accuracy of spaceborne SAR ocean images, so as to solve the problem of low positioning accuracy of vessels relying solely on SAR images obtained by SAR satellites in related technologies.

[0020] To achieve the above technical objectives, the present invention adopts the following technical solutions:

[0021] According to the first aspect of the present invention, a method for improving the geometric positioning accuracy of spaceborne SAR ocean images is provided, including:

[0022] Step S11: Formulate the future imaging mission plan and data transmission and reception tasks of the spaceborne SAR satellite system. According to the imaging mission plan, determine the satellite imaging area range and specific imaging time. After the SAR satellite completes imaging according to the imaging mission plan, collect AIS data corresponding to the specific time and area range through an AIS data service provider.

[0023] Step S12: According to the data transmission reception task, preprocess the original bitstream data received by the on-orbit SAR satellite ground processing system from the data transmission station to obtain a segmented on-orbit SAR ocean image;

[0024] Step S13: Detect vessels in the on-orbit SAR ocean image and calculate the actual imaging time of each detected vessel for time synchronization when fusing with the AIS data;

[0025] Step S14: According to the GPS data and the actual imaging time of each vessel, combined with the dynamic model, calculate the orbital position of the on-orbit SAR satellite corresponding to the actual imaging time of each vessel;

[0026] Step S15: According to the actual imaging time of each vessel, pre-match the vessels in the on-orbit SAR ocean image and the AIS data to obtain a combination of the SAR image position and the AIS position of the vessels;

[0027] Step S16: Based on the global minimum mean square error, optimize the matching of the combination of the SAR image position and the AIS position to obtain optimized matching point pairs;

[0028] Step S17: Based on the optimized matching point pairs and the orbital position of the on-orbit SAR satellite corresponding to the actual imaging time of each vessel, use the AIS position as a control point to correct the RD model error, and re-solve to obtain new geographic coordinate information of each SAR image pixel value, and obtain a geometric correction image product with improved positioning accuracy.

[0029] According to the second aspect of the present invention, there is provided an on-orbit SAR ocean image geometric positioning accuracy improvement system, including:

[0030] A data acquisition and processing module, configured to formulate future imaging task plans and data transmission reception tasks for the on-orbit SAR satellite system, determine the satellite imaging area range and specific imaging time according to the imaging task plan, and after the SAR satellite completes imaging according to the imaging task plan, collect AIS data corresponding to the corresponding time and area range through an AIS data service provider;

[0031] It is also configured to preprocess the original bitstream data received by the on-orbit SAR satellite ground processing system from the data transmission station according to the data transmission reception task to obtain a segmented on-orbit SAR ocean image;

[0032] A calculation module, configured to detect vessels in the on-orbit SAR ocean image and calculate the actual imaging time of each detected vessel for time synchronization when fusing with the AIS data;

[0033] It is also used to calculate the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each vessel according to the GPS data and the actual imaging time of each vessel, in combination with the dynamic model;

[0034] A matching module, configured to pre-match the vessels in the spaceborne SAR ocean image and the AIS data according to the actual imaging time of each vessel, to obtain a combination of the SAR image position and the AIS position of the vessel;

[0035] An optimization module, configured to perform matching optimization on the combination of the SAR image position and the AIS position based on the global minimum mean square error, to obtain an optimized matching point pair;

[0036] A correction module, configured to correct the RD model error by using the AIS position as a control point based on the optimized matching point pair and the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each vessel, and re-solve to obtain new geographic coordinate information of each SAR image pixel value, so as to obtain a geometric correction image product with improved positioning accuracy.

[0037] The technical solution provided by the embodiment of the present invention may include the following beneficial effects:

[0038] By collecting AIS data within the same imaging time and imaging area range as the SAR satellite, and correcting the orbital position of the spaceborne SAR satellite according to the actual imaging time of the vessel, and at the same time fusing the AIS data and the SAR satellite data, a matching point pair of the SAR image position and the AIS position of the vessel is obtained. According to the optimized matching point pair result and the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each vessel, the AIS position is used as a control point to correct the RD model error, and re-iteratively solve to obtain new geographic coordinate information of each image pixel value, and finally obtain a system geometric correction image product with improved positioning accuracy, which solves the problem of low positioning accuracy of vessels relying solely on the SAR images obtained by the SAR satellite in the prior art. Description of the Drawings

[0039] Figure 1 is a flowchart of a method for improving the geometric positioning accuracy of a spaceborne SAR ocean image shown according to an exemplary embodiment;

[0040] Figure 2 is a schematic block diagram of a system for improving the geometric positioning accuracy of a spaceborne SAR ocean image shown according to an exemplary embodiment. Detailed Embodiments

[0041] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solution in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0042] Embodiment 1

[0043] Figure 1 is a flowchart of a method for improving the geometric positioning accuracy of spaceborne SAR ocean images shown according to an exemplary embodiment. Refer to Figure 1 , the method includes:

[0044] Step S11: Formulate the future imaging task plan and data transmission and reception tasks of the spaceborne SAR satellite system. Determine the satellite imaging area range and specific imaging time according to the imaging task plan. After the SAR satellite completes imaging according to the imaging task plan, collect AIS data corresponding to the time and area range through an AIS data service provider;

[0045] Step S12: According to the data transmission and reception tasks, preprocess the original code stream data received by the spaceborne SAR satellite ground processing system from the data transmission station to obtain segmented spaceborne SAR ocean images;

[0046] Step S13: Detect ships in the spaceborne SAR ocean images and calculate the actual imaging time of each detected ship to synchronize time when fusing with the AIS data;

[0047] Step S14: According to the GPS data and the actual imaging time of each ship, combine with the dynamic model to calculate the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship;

[0048] Step S15: According to the actual imaging time of each ship, pre-match the ships in the spaceborne SAR ocean images and the AIS data to obtain a combination of the SAR image position and the AIS position of the ships;

[0049] Step S16: Based on the global minimum mean square error, optimize the matching of the combination of the SAR image position and the AIS position to obtain optimized matching point pairs;

[0050] Step S17: Based on the optimized matching point pairs and the orbital positions of the spaceborne SAR satellites corresponding to the actual imaging times of each vessel, use the AIS positions as control points to correct the errors of the RD model, and re-solve to obtain the new geographic coordinate information of each SAR image pixel value, thereby obtaining a geometric correction image product with improved positioning accuracy.

[0051] It should be noted that the technical solution provided in this embodiment runs on an electronic device when implemented in practice. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the embodiment. The electronic device is generally set up at the ground receiving center of the SAR satellite.

[0052] It can be understood that the technical solution provided in this embodiment collects AIS data within the same imaging time and imaging area range as the SAR satellite, corrects the orbital position of the spaceborne SAR satellite according to the actual imaging time of the vessel, and at the same time fuses the AIS data and the SAR satellite data to obtain the matching point pairs of the SAR image position and the AIS position of the vessel. Based on the optimized matching point pair results and the orbital positions of the spaceborne SAR satellites corresponding to the actual imaging times of each vessel, use the AIS positions as control points to correct the errors of the RD model, and re-iterate to solve to obtain the new geographic coordinate information of each image pixel value, and finally obtain a system geometric correction image product with improved positioning accuracy, solving the problem of low positioning accuracy of vessels relying solely on SAR images obtained by SAR satellites in the prior art.

[0053] It should be noted that in step S11, according to the orbit of the SAR satellite and the planned imaging ocean area position, formulate the future imaging task plan and data transmission reception task of the spaceborne SAR satellite system. Collect AIS data within the range of this time and location through the AIS data service provider. Usually, the AIS data time covers a range of half an hour before and after the spaceborne SAR imaging time.

[0054] To facilitate the understanding of the specific implementation of preprocessing the original code stream data received by the data transmission receiving station of the spaceborne SAR satellite ground processing system according to the data transmission reception task in step S12 to obtain the segmented spaceborne SAR ocean image, the following explains the relevant technical terms:

[0055] The area covered by a single satellite scan is called a "scene," similar to a photograph taken by a camera. The size of a scene depends on the satellite's imaging mode and typically covers tens to hundreds of kilometers (for example, a Sentinel-1 scene is approximately 250 km x 250 km). SAR satellite imaging requires segmentation based on satellite orbital parameters (such as latitude / longitude) or mission requirements to ensure seamless stitching between adjacent scenes.

[0056] Spaceborne SAR data undergoes multiple levels of processing from raw signals to usable images, resulting in standardized products of different levels:

[0057] Level 0 (raw data): Unprocessed radar echo signals (binary data packets) that cannot be used directly by users.

[0058] Level 1 (Basic Product):

[0059] Single-look complex image (SLC): Contains phase and amplitude information for specialized analysis (such as interferometry).

[0060] Ground Range Image (GRD): After geometric correction, it can be directly used for target detection or mapping.

[0061] Level 2+ (Advanced Product): Further processed into thematic data, such as ocean wind fields, surface deformation maps, etc.

[0062] The "single scene product" downloaded by the user is an image that can be used directly (such as Level 1 GRD) without having to process the original signal. Each scene product contains:

[0063] Image data: A grayscale image reflecting the backscattering intensity of the surface.

[0064] Metadata: shooting time, satellite parameters (incident angle, polarization mode), geographical range, etc.

[0065] Specifically, image products at all levels are obtained through processing steps such as data analysis, data segmentation, and standard scene product production. The system geometric correction product utilizes the RD model, which iteratively solves to obtain the geographic coordinates of each image pixel. Satellite orbit data for at least four seconds during the imaging period is obtained from auxiliary data, typically in second intervals.

[0066] It should be noted that in step S13, a constant false-alarm rate (CFAR) or deep learning method is used to detect ships from the spaceborne SAR ocean image. Since the ship detection method is an existing technology, it will not be described in detail in this embodiment.

[0067] In specific practice, calculating the actual imaging time of each detected vessel in step S13 includes:

[0068] According to the vessel detection result, obtain the row and column coordinates of all vessels on the spaceborne SAR ocean image , where and correspond to the pixel positions in the azimuth direction and range direction respectively, indicating the number of detected vessels;

[0069] Obtain the longitude and latitude position values of all vessels from the spaceborne SAR ocean image , where represents longitude, represents latitude, represents elevation;

[0070] Obtain the central imaging time of this scene image product, the pulse repetition frequency prf of the echo signal, and the starting time of the azimuth direction in the imaging process from the metadata of the current scene image product ; ;

[0071] Calculate the actual imaging time of each vessel , .

[0072] It can be understood that the SAR imaging process involves signal time delay and Doppler frequency shift. Since the satellite is in motion, the echo received at different time points corresponds to different position information. For stationary targets, their imaging positions can be determined through the geometric relationship between the satellite's position and the beam. However, for moving vessels, their motion may affect the time delay and Doppler parameters of the echo, resulting in position offset or blurring in the image.

[0073] Therefore, calculating the actual imaging time of each vessel is to more accurately locate their positions in the image. For example, if a vessel is moving, its speed will cause a deviation between its position in the SAR image and its actual position. At this time, knowing the specific imaging time point and combining the satellite's orbital parameters can correct this deviation or inversely deduce the vessel's speed and heading.

[0074] In addition, the actual position corresponding to each pixel in the SAR image is calculated through the propagation time of the radar signal and the satellite's position. For stationary targets, this process is relatively straightforward, but moving targets introduce additional time-varying factors. Therefore, determining the actual imaging time of the vessel helps to accurately register its position, especially when it is necessary to fuse with other data sources (such as AIS data), and time synchronization is very important.

[0075] In specific practice, in step S14, according to the GPS data and the actual imaging time of each vessel, combined with the dynamic model, the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each vessel is calculated, including:

[0076] According to the GPS data in the spaceborne SAR auxiliary data, parameter fitting is performed using the third-order polynomial method, including:

[0077] (1)

[0078] Wherein, , represents the orbital position of the SAR satellite at time t0 in the GPS data; represents the orbital position of the SAR satellite at time t1 in the GPS data; represents the orbital position of the SAR satellite at time t2 in the GPS data; represents the orbital position of the SAR satellite at time t3 in the GPS data; the first column in matrix T is the number 1;

[0079] According to the least squares method, the specific values of the parameters in matrix A are calculated:

[0080] (2)

[0081] According to the actual imaging time of each vessel, the orbital position of the spaceborne SAR satellite at its corresponding time is calculated through third-order fitting:

[0082] (3).

[0083] It can be understood that in a spaceborne SAR (Synthetic Aperture Radar) system, using GPS data for orbital data fitting is a core technical means to ensure the high-precision acquisition of satellite position, speed, and attitude parameters. This process directly affects the geometric positioning accuracy of SAR images, the interferometric measurement ability, and the reliability of moving target detection.

[0084] In a spaceborne SAR (Synthetic Aperture Radar) system, the auxiliary data usually includes various parameters required for satellite operation, and the GPS data is an important part of the auxiliary data. The GPS receiver records the three-dimensional position (longitude, latitude, altitude) and speed of the satellite in real time for subsequent imaging geometric correction and geolocation.

[0085] In spaceborne synthetic aperture radar (SAR) systems, orbital data fitting using GPS data is a core technical approach to ensure high-precision acquisition of satellite position, velocity, and attitude parameters. This process directly impacts the geometric positioning accuracy of SAR images, interferometric measurement capabilities, and the reliability of moving target detection. SAR uses the motion of the satellite platform to synthesize a virtual long-aperture antenna, and its imaging and data processing are highly dependent on the spatiotemporal parameters of the satellite orbit.

[0086] Geometric positioning: The geographic coordinates of each pixel in a SAR image are determined by the radar wave's travel time and the satellite's position. If the orbital error exceeds 1 meter, the image positioning error can exceed 10 meters (especially at low angles of incidence).

[0087] GPS provides a real-time, high-frequency, and high-precision orbit measurement method for spaceborne SAR. Its advantages are irreplaceable: 3D position (X / Y / Z) and velocity (Vx / Vy / Vz) directly cover all the degrees of freedom required for orbit fitting. Typical GPS receiver data output frequencies range from 1 Hz to 10 Hz, far exceeding the minute-level frequency of ground-based measurement and control systems, enabling the capture of minute-by-minute details of satellite motion.

[0088] In specific practice, in step S15, the ship in the spaceborne SAR ocean image and the AIS data are pre-matched according to the actual imaging time of each ship to obtain a combination of the SAR image position and the AIS position of the ship, including:

[0089] The AIS data is classified according to the vessel identification code and timestamp, and the data with heading changes in the vessel's AIS dynamic information is cleared, and the data whose AIS data time does not cover the spaceborne SAR imaging time is cleared;

[0090] According to the timestamp and position of the ship AIS data, the second-order fitting method is used to calculate the imaging time of the ship in the spaceborne SAR image. AIS latitude and longitude position values ,in, Indicates longitude, Indicates latitude, Indicates the number of ships that meet the AIS criteria;

[0091] The latitude and longitude position value P of the AIS i And the latitude and longitude position values R of all ships detected on the SAR image i Perform spatial correlation to obtain the combination of the ship's SAR image position and AIS position ,in Indicates the number of ships in the SAR image that can be matched with AIS signals according to spatial association rules, Indicates the number of AIS signals that can be matched and associated with vessel j according to the spatial association rule, where L is a positive integer greater than 2; among them, if a vessel in the SAR image fails to match AIS data, the detection result of that vessel is deleted.

[0092] It should be noted that according to the spatial rule with a tolerance of 1.5 times, each vessel may be associated with multiple AIS signals. Here, L represents the number of AIS signals associated with vessel j.

[0093] It can be understood that the inventive concept of the present invention is: when processing satellite SAR image data, AIS data is associated to verify or analyze the activities of vessels, whether the position of AIS data at a specific time point (the actual imaging time of the vessel) matches the detection result of the vessel in the SAR image.

[0094] In this embodiment, according to the longitude and latitude coordinates of the detected vessels on the spaceborne SAR image , a spatial region with a tolerance range is set to perform spatial association on the vessels in the image and the corresponding AIS longitude and latitude position value data, and the tolerance is set to 1.5 times the positioning accuracy index of the spaceborne SAR image.

[0095] According to the AIS information standard, the reporting interval of static information such as its information identification code and MMSI number is 6 minutes, while the reporting interval of dynamic information such as the ground speed, vessel position longitude and latitude, and course varies from 2 seconds to 3 minutes.

[0096] Cleaning the AIS data is to improve the accuracy of subsequent analysis for spatio-temporal matching analysis. Clean AIS data can help exclude false alarms, such as position deviations caused by changes in course or data unavailability due to time mismatches, ensuring that the AIS data exactly coincides with the time window of the SAR image, thereby accurately associating the data of both and enhancing the reliability of detection or verification. For example, if the SAR image is taken at a specific moment and there is no record of the AIS data at that moment and the moments before and after, then these data need to be excluded to avoid introducing noise.

[0097] In specific practice, a threshold needs to be set for the judgment of course changes. For example, a course change exceeding a certain angle is regarded as an effective change to avoid misjudgment caused by data noise. At the same time, a time interval needs to be defined for time coverage, such as a few minutes before and after the SAR imaging moment, to accommodate the time deviation of the AIS data because the AIS data may have problems with time delay or update frequency.

[0098] In specific practice, in step S16, based on the global minimum mean square error, the combination of the SAR image position and the AIS position is matched and optimized to obtain the optimized matching point pairs, including:

[0099] Calculate the combination of the SAR image position and the AIS position of a ship The distance in the longitude direction between the SAR image position and the AIS position of each ship in ;

[0100] According to the said distance average value, use the global least mean square error correlation algorithm to optimize the matching of the combination of the SAR image position and the AIS position.

[0101] In spaceborne SAR imaging processing, when the ship target has motion in the direction perpendicular to the satellite orbit, it will cause the imaging position of the ship in the spaceborne SAR image to shift along the satellite orbit direction. The shift amount is related to the ship's motion speed, motion direction, radar incident angle, etc.

[0102] Preferably, the combination of calculating the SAR image position and the AIS position of the ship The distance in the longitude direction between the SAR image position and the AIS position of each ship in , specifically:

[0103] (4)

[0104] S jk = COS(R j -Lon k )(5)

[0105] Wherein, represents the distance in the longitude direction between the jth ship that can be associated with the AIS in the SAR image and the kth ship that can be associated with the SAR image in the AIS position; R j represents the longitude value of the jth ship that can be associated with the AIS in the SAR image, and Lon k represents the longitude value of the kth ship that can be associated with the SAR image in the AIS position.

[0106] Preferably, the said according to the said distance average value, use the global least mean square error correlation algorithm to optimize the matching of the combination of the SAR image position and the AIS position, including:

[0107] Use the global least mean square error correlation algorithm to calculate the minimum value of the mean square value of the distance deviation of all ships in the longitude direction. The designed objective function is as follows:

[0108] (6)

[0109] Wherein, represents the average value of all in formula (4); is a binary variable, where 0 means not associated and 1 means associated. In a set of roughly matched there is at most 1 associated value, and when exceeds the number of matching pairs in this group at that time the value is 0; represents the maximum value when all vessels are roughly matched ;

[0110] The Munkres algorithm is used to calculate the point track matching of the global minimum mean square error, and new matching point pairs of the SAR image position and the AIS position are obtained , where , represents the number of vessels for which matching is achieved;

[0111] Calculate the Euclidean distance between the SAR image position and the AIS position of the vessels in :

[0112] (7)

[0113] Calculate the mean value of and the standard deviation of all matching point pairs, and determine whether the associated matching point pairs exceed the threshold. If they exceed the threshold, it indicates that the associated matching is invalid, and the matching points are deleted. The threshold range is , and the matching point pairs optimized by spatio-temporal association are obtained .

[0114] It can be understood that the optimization method based on the global minimum mean square error (MMSE, Minimum Mean Square Error) applied to the matching of SAR images and AIS (Automatic Identification System) positions can significantly improve the accuracy and reliability of multi-source data fusion.

[0115] MMSE finds the optimal matching combination between the positions of vessels detected in the SAR image and the dynamic positions reported by AIS by minimizing the mean square value of the global position error. This method is unbiased and effective statistically and can eliminate the influence of random noise (such as SAR imaging errors, AIS transmission delays) on the matching results. For example, if the detected coordinates of a vessel in the SAR image are A, and AIS reports multiple candidate positions B1, B2, B3,........Bn during the same period, MMSE selects the AIS record with the minimum error as the matching object by calculating the global mean square error.

[0116] In addition, in dense sea areas, SAR may simultaneously detect multiple vessels (such as in ports or waterways), and AIS data may contain dynamic information of hundreds of vessels. Through global optimization, MMSE can avoid local optimal traps (such as only matching the nearest neighbor AIS targets) and achieve the global optimal solution for many-to-many matching. For example, if the SAR image detects 5 vessels while AIS reports 10 vessels in the same area, MMSE can avoid mis-matching a SAR vessel to multiple AIS targets or missing low-speed vessels by jointly optimizing all possible matching combinations.

[0117] In specific practice, in step S17, based on the optimized matching point pairs and the orbital positions of the spaceborne SAR satellites corresponding to the actual imaging times of each vessel, the AIS positions are used as control points to correct the RD model error, and new geographic coordinate information of each SAR image pixel value is obtained by re-solving, resulting in a geometric correction image product with improved positioning accuracy, including:

[0118] The positioning error of the spaceborne SAR image can be divided into range-direction positioning error and azimuth error according to the satellite imaging direction. According to the RD model, the errors in these two dimensions can be expressed by the range-direction initial slant range error and the azimuth starting time error.

[0119] The slant range equation of each pixel point in the RD model is expressed as the following formula (8):

[0120]

[0121] where represents the initial slant range used in the slant range calculation, the near-range value in the imaging process, which is obtained from the metadata of the current scene image product; is the speed of light constant, is the number of range-direction sampling points; represents the sampling frequency of the echo signal, which is obtained from the metadata of the current scene image product; represents the range-direction error, including the delay error introduced by atmospheric propagation, the system sampling time error, and the channel delay error. In a single imaging mission, this error value takes a fixed value;

[0122] The formula expression of the azimuth starting time error is as follows:

[0123] (9)

[0124] where represents the azimuth starting moment in the imaging process, which is obtained from the metadata of the current scene image product; is the azimuth pulse count; represents the azimuth time error;

[0125] According to Equation (3), the orbital position error of the spaceborne SAR satellite is expressed as a first-order function of the azimuth time error:

[0126] (10)

[0127] Combining Equation (8) and Equation (10), the equation (11) containing the initial slant range error in the range direction and the starting time error in the azimuth direction is obtained:

[0128]

[0129] According to Equation (11), calculate and values to correct the RD model.

[0130] In specific practice, the calculating and values according to Equation (11) to correct the RD model includes:

[0131] Step S171, obtain calculation parameters, where the calculation parameters include: the longitude and latitude coordinates of the AIS expressed as the coordinate position of the vessel in the SAR image , the column coordinate of the corresponding vessel on the spaceborne SAR image , and the parameters and in the metadata, and the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of the vessel;

[0132] Step S172, use the matched point pairs optimized by spatio-temporal correlation to calculate and values to correct the RD model, where represents the number of matches in

[0133] When , use Equation (8), substitute , , , and to calculate , and take the average value as the slant range error in the range direction , and default the azimuth time error to 0;

[0134] When , use Equation (10) to obtain a system of equations, set initial and both to be 0, and use the Newton - Raphson method to calculate and by using the first - order derivative; judge whether the error is less than a pre - set threshold when is satisfied, the iteration terminates, otherwise go to step S171 to continue the iterative calculation;

[0135] Step S173, according to and results, update the RD model system of equations, and the corrected system geometric correction algorithm equation includes:

[0136] (12)

[0137] (13)

[0138] (14)

[0139] Re - obtain the geographical coordinate position information of each pixel in the image by iterative solution or using Ferrari's method, and finally obtain a system geometric correction image product with improved positioning accuracy.

[0140] Through the description of the above technical solutions, it can be seen that the technical solution provided in this embodiment improves the quality of ocean images in space - borne SAR ground processing, and solves the problem of insufficient calibration parameters and control points of commercial space - borne SAR satellites. According to the dynamic and static information in AIS and the ship detection information in space - borne SAR images, a high - precision association of AIS signals and ship SAR images is realized through a multi - strategy spatio - temporal association matching method. At the same time, the RD correction model in the space - borne SAR ground processing system is corrected using the AIS position information, two - dimensional error parameters in the azimuth and range directions are obtained, and the RD model equation is reconstructed, realizing the timeliness and reliability of improving the accuracy of the ground processing system geometric correction product.

[0141] First, the spatio - temporal association matching processing of ships in space - borne SAR images and AIS data is improved. Due to the movement of ship targets, the position of ship images appears shifted, which is due to the imaging mechanism of space - borne SAR. Existing methods basically use the calculation of motion parameters to compensate and correct the ship position. By designing a preliminary matching strategy based on the distance in the longitude direction and combining with the global least mean square error optimization algorithm, the matching efficiency and accuracy of AIS data and SAR image ships are significantly improved, and the matching problem in multi - target scenarios is solved.

[0142] Secondly, for the geometric correction RD model algorithm of the modified spaceborne SAR system, aiming at the random and systematic biases that cannot be eliminated during the spaceborne SAR imaging process, the position information with meter-level accuracy of AIS is used to calibrate the initial slant range error in the range direction and the starting time error in the azimuth direction in the RD model, and the RD model equation is reconstructed, effectively improving the geometric correction accuracy of the spaceborne SAR ground processing system.

[0143] Compared with the prior art, the technical solution provided in this embodiment has at least the following beneficial effects:

[0144] (1) By introducing AIS data as control points, the geometric positioning accuracy of spaceborne SAR ocean images is significantly improved, and the problems of lack of control points and insufficient update of calibration parameters are solved.

[0145] (2) On the basis of the existing business process of the spaceborne SAR ground processing system, by introducing AIS data and optimizing the geometric correction algorithm, without adding hardware facilities, the geometric correction algorithm of the spaceborne SAR ground processing system is optimized, and the processing efficiency and reliability of ocean images are improved.

[0146] (3) This method provides technical support for the application of high-resolution spaceborne SAR data in the fields of ocean monitoring, target recognition, etc., and has a broad application prospect.

[0147] Embodiment 2

[0148] Figure 2 It is a schematic block diagram of a system 100 for improving the geometric positioning accuracy of spaceborne SAR ocean images shown according to an exemplary embodiment. Refer to Figure 2 , the system 100 includes:

[0149] A data acquisition and processing module 101, configured to formulate the future imaging task plan and data transmission reception task of the spaceborne SAR satellite system, determine the satellite imaging area range and specific imaging time according to the imaging task plan, and after the SAR satellite completes imaging according to the imaging task plan, collect the AIS data corresponding to the time and area range through an AIS data service provider;

[0150] It is also configured to preprocess the original code stream data received by the spaceborne SAR satellite ground processing system from the data transmission receiving station according to the data transmission reception task, and obtain the segmented spaceborne SAR ocean images;

[0151] A calculation module 102, configured to detect ships in the spaceborne SAR ocean images, and calculate the actual imaging time of each detected ship for time synchronization when fusing with the AIS data;

[0152] It is also used to calculate the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship according to the GPS data and the actual imaging time of each ship, in combination with the dynamic model;

[0153] The matching module 103 is used to pre-match the ships in the spaceborne SAR ocean image and the AIS data according to the actual imaging time of each ship, so as to obtain the combination of the SAR image position and the AIS position of the ship;

[0154] The optimization module 104 is used to perform matching optimization on the combination of the SAR image position and the AIS position based on the global minimum mean square error, so as to obtain the optimized matching point pairs;

[0155] The correction module 105 is used to correct the RD model error with the AIS position as the control point based on the optimized matching point pairs and the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship, and re-solve to obtain the new geographic coordinate information of each SAR image pixel value, so as to obtain a geometric correction image product with improved positioning accuracy.

[0156] It should be noted that the technical solution provided in this embodiment is loaded and run in an electronic device in specific practice. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the embodiment. The electronic device is generally set at the ground receiving center of the SAR satellite.

[0157] For the implementation manners of the above modules, refer to the implementation manners of the corresponding steps in Embodiment 1, which will not be elaborated in this embodiment.

[0158] It can be understood that the technical solution provided in this embodiment collects AIS data within the same imaging time and imaging area range as the SAR satellite, corrects the orbital position of the spaceborne SAR satellite according to the actual imaging time of the ship, and at the same time fuses the AIS data and the SAR satellite data to obtain the matching point pairs of the SAR image position and the AIS position of the ship. According to the optimized matching point pair results and the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship, the AIS position is used as the control point to correct the RD model error, and re-iteratively solve to obtain the new geographic coordinate information of each image pixel value, and finally obtain a system geometric correction image product with improved positioning accuracy, solving the problem of low positioning accuracy of ships relying solely on the SAR images obtained by the SAR satellite in the prior art.

[0159] The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages and disadvantages of the embodiments.

[0160] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage media. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing one or more computer devices (which can be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in various embodiments of this application.

[0161] In the above embodiments of this application, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0162] In the several embodiments provided by this application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in an electrical or other form.

[0163] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0165] The above are only the preferred embodiments of this application. It should be noted that for those of ordinary skill in the art, without departing from the principle of this application, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as the protection scope of this application.

Claims

1. A method for improving the geometric positioning accuracy of spaceborne SAR ocean images, characterized in that, Including: Step S11: Formulate the future imaging mission plan and data transmission reception mission of the spaceborne SAR satellite system. Determine the satellite imaging area range and specific imaging time according to the imaging mission plan. After the SAR satellite completes imaging according to the imaging mission plan, collect AIS data corresponding to the specific time and within the area range through an AIS data service provider. Step S12: According to the data transmission reception mission, preprocess the original code stream data received by the ground processing system of the spaceborne SAR satellite from the data transmission station to obtain segmented spaceborne SAR ocean images. Step S13: Detect ships in the spaceborne SAR ocean images and calculate the actual imaging time of each detected ship for time synchronization when fusing with the AIS data. Step S14: According to the GPS data and the actual imaging time of each ship, combined with the dynamic model, calculate the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship. Step S15: According to the actual imaging time of each ship, pre-match the ships in the spaceborne SAR ocean images and the AIS data to obtain a combination of the SAR image position and the AIS position of the ships. Step S16: Based on the global minimum mean square error, optimize the matching of the combination of the SAR image position and the AIS position to obtain optimized matching point pairs. Step S17: Based on the optimized matching point pairs and the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship, use the AIS position as a control point to correct the RD model error, and re-solve to obtain new geographic coordinate information of each SAR image pixel value, and obtain a geometric correction image product with improved positioning accuracy. Among them, in step S15, according to the actual imaging time of each ship, pre-match the ships in the spaceborne SAR ocean images and the AIS data to obtain a combination of the SAR image position and the AIS position of the ships, including: Classify the AIS data according to the ship identification code and time stamp, clear the data with changed course in the AIS dynamic information of the ships, and clear the data whose AIS data time cannot cover the spaceborne SAR imaging time. According to the timestamp and location of the vessel's AIS data, the imaging time of the vessel in the spaceborne SAR image is calculated by the method of second-order fitting of the longitude and latitude position values of the AIS , where represents longitude represents latitude represents the number of vessels with AIS meeting the conditions; The longitude and latitude position value P of the AIS i and the longitude and latitude position values R of all vessels detected on the SAR image i are spatially associated to obtain a combination of the SAR image position and the AIS position of the vessels , where R j ∈R i , R j represents the longitude and latitude position value of the j-th vessel detected on the SAR image; P jk ∈P i , P jk represents the longitude and latitude position value of the k-th vessel in the AIS that can match R j ; represents the number of vessels in the SAR image that can be matched with the AIS signal according to the spatial association rule, represents the number of AIS signals that can be matched and associated with vessel j according to the spatial association rule, and L is a positive integer greater than 2; among them, if a certain vessel in the SAR image fails to match the AIS data, the detection result of that vessel is deleted; Among them, in step S16, based on the global minimum mean square error, optimize the matching of the combination of the SAR image position and the AIS position to obtain optimized matching point pairs, including: Calculate the combination of the SAR image position and the AIS position of a vessel The distance in the longitude direction between the SAR image position and the AIS position of each vessel in ; According to the average value of the said distance the combination of the SAR image position and the AIS position is matched and optimized by using the correlation algorithm of global least mean square error.

2. The method according to claim 1, wherein In step S13, calculating the actual imaging time of each detected ship includes: According to the ship detection results, obtain the row and column coordinates of all ships on the spaceborne SAR ocean image in the spaceborne SAR image , where and correspond to the pixel positions in the azimuth direction and the range direction respectively, indicating the number of detected ships; Obtain the longitude and latitude position values of all vessels from the spaceborne SAR ocean images , where represents longitude represents latitude represents elevation, and the superscript letter T represents the inversion operation of the matrix, indicating that R i is 's inverted matrix; Obtain the central imaging time of the scene image product from the metadata of the scene image product , the pulse repetition frequency prf of the echo signal, and the starting time in the azimuth direction during imaging processing ; Calculate the actual imaging time of each vessel , .

3. The method according to claim 2, characterized in that, In step S14, according to the GPS data and the actual imaging time of each ship, combined with the dynamic model, calculating the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each ship includes: According to the GPS data in the spaceborne SAR auxiliary data, use the third-order polynomial method for parameter fitting, including: （1） Among them, , the first column in matrix T is the number 1; Represents the orbital position of the SAR satellite at time t0 in the GPS data, Represents the orbital position of the SAR satellite at time t1 in the GPS data, Represents the orbital position of the SAR satellite at time t2 in the GPS data, Represents the orbital position of the SAR satellite at time t3 in the GPS data, , , , all include three values: longitude, latitude, and elevation. Among them, the relationship between longitude and the imaging time satisfies a third-order polynomial, where a0, a1, a2, and a3 are the coefficients of the corresponding third-order polynomial; the relationship between latitude and the imaging time satisfies a third-order polynomial, where b0, b1, b2, and b3 are the coefficients of the corresponding third-order polynomial; the relationship between elevation and the imaging time satisfies a third-order polynomial, where c0, c1, c2, and c3 are the coefficients of the corresponding third-order polynomial; ia = 0, 1, 2, 3; According to the least squares method, calculate the specific values of each parameter in matrix A: （2） According to the actual imaging time of each vessel , the orbital position of the spaceborne SAR satellite at the corresponding time is calculated by third-order fitting : （3）。 4. The method according to claim 1, wherein The combination of the SAR image position and the AIS position of the calculated vessel The distance in the longitude direction between the SAR image position and the AIS position of each vessel , specifically: （4） S jk = COS(R j -Lon k )(5) Among them, represents the distance in the longitude direction between the j-th vessel in the SAR image that can be associated with AIS and the k-th vessel in the AIS position that can be associated with the SAR image; R j represents the longitude value of the j-th vessel in the SAR image that can be associated with AIS, Lon k represents the longitude value of the k-th vessel in the AIS position that can be associated with the SAR image.

5. The method according to claim 4, characterized in that According to the average value of the distance , an association algorithm of global least mean square error is used to optimize the matching of the combination of the SAR image position and the AIS position, including: Adopt the correlation algorithm of global minimum mean square error to calculate the minimum value of the mean square value of the distance deviation of all ships in the longitude direction. The designed objective function is as follows: （6） Among them, represents the average value of all in formula (4); is a binary variable, 0 means not associated, 1 means associated, and there is at most 1 associated value in a group of rough matches , and when exceeds the number of matching pairs in this group at that time the value is 0; represents the maximum value of all vessels during rough matching ; The Munkres algorithm is used to calculate the point - track matching of the global minimum mean square error, and new matching point pairs of the SAR image position and the AIS position are obtained , where , represents the number of vessels for which matching is achieved; Calculation Euclidean distance between the SAR image position and the AIS position of the vessel in : （7） Among them, Lon j represents longitude, and Lat j represents latitude; Calculate the mean value of all matching point pairs and standard deviation , and determine whether the associated matching point pairs exceed the threshold. If they exceed the threshold, it indicates that the associated match is invalid, and the matching points are deleted. The threshold range is , and obtain the matching point pairs optimized by spatio-temporal association .

6. The method according to claim 5, wherein In step S17, based on the optimized matching point pairs and the orbital positions of the spaceborne SAR satellites corresponding to the actual imaging time of each vessel, the AIS position is used as a control point to correct the RD model error, and the new geographic coordinate information of each SAR image pixel value is obtained by re-solving, resulting in a geometric correction image product with improved positioning accuracy, including: The slant range equation of each pixel point in the RD model is expressed as the following formula (8): Among them, R tx represents the longitude position of the vessel in the AIS at time t; R ty represents the latitude position of the vessel in the AIS at time t; R tz represents the elevation of the vessel in the AIS at time t; Among them, represents the initial slant range adopted in slant range calculation, the near-range value in imaging processing, and is obtained from the metadata of the current scene image product; is the speed of light constant, is the number of sampling points in the range direction; represents the sampling frequency of the echo signal and is obtained from the metadata of the current scene image product; represents the range direction error, including the delay error introduced by atmospheric propagation, the system sampling time error, and the channel time delay error. This error value takes a fixed value in a single imaging task; The formula expression of the azimuth starting time error is as follows: （9） Among them, represents the starting time in the azimuth direction in imaging processing and is obtained from the metadata of the current scene image product; is the azimuth pulse count; represents the azimuth time error; According to formula (3), the orbital position error of the spaceborne SAR satellite is expressed as a first-order function of the azimuth time error: （10） Combining formula (8) and formula (10), an equation (11) containing the range initial slant range error and the azimuth starting time error is obtained: According to Equation (11), calculate and the values, and perform a correction process on the RD model.

7. The method according to claim 6, characterized in that According to Equation (11), calculate and the values, and perform correction processing on the RD model, including: Step S171, obtain calculation parameters, where the calculation parameters include: the longitude and latitude coordinates of the AIS expressed as the coordinate position of the vessel in the SAR image , the column coordinate of the corresponding vessel on the spaceborne SAR image , and the parameters in the metadata and , and the actual imaging time of the vessel corresponding to the orbital position of the spaceborne SAR satellite ; Step S172: Using the matching point pairs optimized by spatio-temporal correlation , calculate and values to correct the RD model, represents the number of matches in, including: When , using formula (8), substitute , , , and to calculate , and take the average value as the range slant range error , and default the azimuth time error to be 0; When , using formula (10), obtain systems of equations, set the initial and both to 0, and use the Newton iteration method to calculate and by using the first-order derivative; judge whether the error is less than the pre-set threshold through iterative calculation. When , the iteration terminates; otherwise, go to step S171 to continue the iterative calculation; Step S173. According to and the results, update the RD model equations. The corrected system geometric correction algorithm equations include: （12） （13） （14） The geographic coordinate position information of each pixel in the image is obtained again through iterative solution or by using the Ferrari method, and finally a system geometric correction image product with improved positioning accuracy is obtained.

8. An on-orbit SAR ocean image geometric positioning accuracy improvement system, characterized in that, Including: A data acquisition and processing module, which is used to formulate the future imaging task plan and data transmission and reception tasks of the spaceborne SAR satellite system, determine the satellite imaging area range and specific imaging time according to the imaging task plan, and after the SAR satellite completes imaging according to the imaging task plan, collect the AIS data corresponding to the time and area range through an AIS data service provider; It is also used to preprocess the original code stream data received by the ground processing system of the spaceborne SAR satellite from the data transmission station according to the data transmission and reception task, and obtain a segmented spaceborne SAR ocean image; A calculation module, which is used to detect vessels in the spaceborne SAR ocean image and calculate the actual imaging time of each detected vessel for time synchronization when fusing with the AIS data; It is also used to calculate the orbital position of the spaceborne SAR satellite corresponding to the actual imaging time of each vessel according to the GPS data and the actual imaging time of each vessel, in combination with the dynamic model; A matching module, which is used to pre-match the vessels in the spaceborne SAR ocean image and the AIS data according to the actual imaging time of each vessel to obtain a combination of the SAR image position and the AIS position of the vessel; An optimization module, which is used to optimize the matching of the combination of the SAR image position and the AIS position based on the global minimum mean square error to obtain optimized matching point pairs; A correction module, which is used to correct the RD model error with the AIS position as a control point based on the optimized matching point pairs and the orbital positions of the spaceborne SAR satellites corresponding to the actual imaging time of each vessel, and re-solve to obtain the new geographic coordinate information of each SAR image pixel value, resulting in a geometric correction image product with improved positioning accuracy; Among them, the pre-matching of the vessels in the spaceborne SAR ocean image and the AIS data according to the actual imaging time of each vessel to obtain a combination of the SAR image position and the AIS position of the vessel includes: Classify the AIS data according to the vessel identification code and time stamp, clear the data with a change in course in the AIS dynamic information of the vessel, and clear the data whose AIS data time cannot cover the spaceborne SAR imaging time; According to the timestamp and location of the vessel's AIS data, the imaging time of the vessel in the spaceborne SAR image is calculated using the second-order fitting method of the longitude and latitude position values of the AIS , where represents longitude represents latitude represents the number of vessels of AIS that meet the conditions; The longitude and latitude position value P of the AIS i and the longitude and latitude position values R of all vessels detected on the SAR image i are spatially associated to obtain the combination of the SAR image positions and AIS positions of the vessels , where R j ∈R i , R j represents the longitude and latitude position value of the j-th vessel detected on the SAR image; P jk ∈P i , P jk represents the longitude and latitude position value of the k-th vessel in the AIS that can match R j ; represents the number of vessels in the SAR image that can match the AIS signal according to the spatial association rule, represents the number of AIS signals that can be matched and associated with vessel j according to the spatial association rule, and L is a positive integer greater than 2; among them, if a certain vessel in the SAR image fails to match the AIS data, the detection result of that vessel is deleted; Among them, the optimization of the matching of the combination of the SAR image position and the AIS position based on the global minimum mean square error to obtain optimized matching point pairs includes: Calculate the combination of the SAR image position and the AIS position of a vessel The distance in the longitude direction between the SAR image position and the AIS position of each vessel in ; According to the average value of the said distance the combination of the SAR image position and the AIS position is optimized by using the global least mean square error correlation algorithm.

Images