Optical constellation joint calibration method, device and equipment

Abstract

This invention provides a method, apparatus, and device for joint calibration of optical satellite constellations, applicable to the field of satellite data processing technology. The method includes: performing small-side-swing imaging on multiple global geometric calibration fields to establish a rigorous geometric positioning model and generate initial rational polynomial parameters; selecting a reference region for dense matching to obtain dense matching points and calculating the payload's internal azimuth parameters and initial external azimuth elements; performing sparse matching on other regions to obtain sparse matching points; and establishing a joint calibration model based on the dense and sparse matching points, obtaining the optimal external calibration parameters through weighted calculation. This invention, by jointly utilizing data from multiple global calibration fields and uniformly calculating the geometric parameters of satellites within a constellation, effectively overcomes the local overfitting problem caused by a single calibration field, significantly improving the consistency of geometric positioning accuracy of satellites within a constellation globally.

Description

Technical Field

[0001] This invention relates to the field of satellite data processing technology, and more specifically to an optical constellation joint calibration method, apparatus, and equipment. Background Technology

[0002] Optical satellite remote sensing technology has become an important tool in the field of Earth observation, widely used in many civilian sectors such as resource surveys, environmental monitoring, surveying and mapping, agriculture, forestry, and urban planning. With the ever-increasing demands of remote sensing applications, the observation capabilities of a single satellite are no longer sufficient to meet the needs of large-scale, high-frequency, and multi-angle Earth observation. This has led to the emergence of multi-satellite network collaborative observation modes. Multi-satellite, multi-payload collaborative observation technology in constellation networking has become an inevitable trend in the development of remote sensing satellites, and multi-satellite joint observation and integrated data application have entered a routine operational mode.

[0003] In optical constellation remote sensing satellite systems, each satellite is equipped with corresponding geometric positioning design specifications to meet geometric positioning requirements. These specifications mainly include: internal camera geometric parameters (such as principal point, principal distance, lens distortion, etc.), attitude measurement equipment specifications (such as pointing accuracy and measurement accuracy of star sensors), orbit determination accuracy, and time synchronization accuracy between various payloads on the platform. These design specifications are prerequisites for ensuring the geometric positioning accuracy of the satellite. Before satellite launch, the onboard payload parameters are usually rigorously calibrated in the laboratory to obtain initial geometric calibration parameters.

[0004] However, during satellite launch and on-orbit operation, the structure and state of the onboard payload undergo varying degrees of change due to factors such as variations in the space environment, mechanical shocks, thermal vacuum effects, and material aging. These on-orbit state changes result in significant deviations between the geometric parameters obtained through laboratory calibration and the actual geometric parameters of the satellite during on-orbit operation, thus generating geometric systematic errors. These errors directly impact the geometric positioning processing of satellite imagery, leading to a decrease in the geometric positioning accuracy of image products and failing to meet the demands of high-precision applications.

[0005] For the geometric calibration of a single optical satellite, existing technologies mainly employ calibration methods based on geometric calibration fields. This method images a ground-based geometric calibration field from the satellite and uses control point information within the field to calculate the satellite's geometric calibration parameters. However, for optical constellations composed of multiple satellites, traditional calibration methods based on a single geometric calibration field have inherent limitations. Because the orbital characteristics, imaging times, and transit areas of different satellites within the constellation vary, a single geometric calibration field cannot cover the imaging geometry of all satellites in the constellation. This results in the geometric parameters calculated using a single calibration field being insufficient to effectively calibrate the geometric systematic errors of all satellites within the constellation. Consequently, when different satellites within the constellation perform image positioning at different locations globally, their geometric positioning accuracy varies significantly, affecting the collaborative application efficiency of constellation data. Summary of the Invention

[0006] (a) Technical problems to be solved

[0007] To address at least one of the aforementioned problems, this invention provides an optical constellation joint calibration method, apparatus, and device. The method involves imaging multiple global geometric calibration fields with small side-swing angles to establish a rigorous geometric positioning model and generate initial rational polynomial parameters. A reference region is selected for dense matching to calculate the payload's internal azimuth parameters, while other regions are sparsely matched to obtain sparse matching points. Furthermore, a weighted joint calibration model is established to integrate both dense and sparse matching points into the calculation, obtaining optimal external calibration parameters. This achieves joint calculation and global consistency optimization of satellite geometric parameters within the constellation.

[0008] (II) Technical Solution

[0009] To address the aforementioned technical problems, embodiments of the present invention propose an optical constellation joint calibration method, apparatus, and device.

[0010] According to a first aspect of the present invention, a joint calibration method for optical constellations is provided, comprising: performing small-side-swing imaging on geometric calibration fields distributed in multiple regions globally to obtain imaging data for multiple regions; establishing a rigorous geometric positioning model for the payload based on initial satellite payload geometric parameters, satellite auxiliary data, and imaging data, and generating initial rational polynomial parameters; selecting one of the multiple geometric calibration fields as a reference region, and performing dense matching between the initial rational polynomial parameters and the geometric calibration field control image data of the reference region to obtain the pixel coordinates and corresponding geographic coordinates of the densely matched points; establishing a rigorous imaging model using the densely matched points, and solving for the payload interior orientation parameters and initial exterior orientation elements; performing sparse matching between the geometric calibration field imaging data of other regions and the corresponding geometric calibration field control image data based on the initial rational polynomial parameters to obtain the pixel coordinates and corresponding geographic coordinates of the sparsely matched points; and establishing a joint calibration model based on the densely matched points and the sparsely matched points, solving for the optimal exterior calibration parameters, and completing the optical constellation geometric calibration.

[0011] In some exemplary embodiments, small lateral tilt imaging is performed on geometric calibration fields distributed across multiple regions globally. This includes setting imaging constraints to ensure that the imaging data used for calibration meets geometric stability requirements. The imaging constraint is that the imaging cone angle is less than a preset threshold. The preset threshold is determined based on the matching relationship between the internal accuracy of geometric calibration, the resolution of the image to be calibrated, and the accuracy of the digital elevation model data used for calibration. The preset threshold is obtained by dividing the product of the internal accuracy of geometric calibration and the resolution of the image to be calibrated by the accuracy of the digital elevation model data, and then taking the arctangent value.

[0012] In some exemplary embodiments, satellite-aided data includes attitude data, orbit data, and travel time data. The method further includes preprocessing the attitude data, orbit data, and travel time data. The preprocessing includes: deduplicating the attitude data and orbit data, wherein if the quaternion difference between two adjacent packets of attitude data is less than a preset attitude threshold and the time difference is less than a preset time threshold, they are determined to be duplicates and removed; if the position difference between two adjacent packets of orbit data is less than a preset orbit threshold and the time difference is less than a preset time threshold, they are determined to be duplicates and removed; sorting and outlier removal are performed on the deduplicated attitude data, orbit data, and travel time data, wherein outlier data is removed based on the median time and a physical threshold.

[0013] In some exemplary embodiments, dense matching is performed based on the initial rational polynomial parameters and the geometric calibration field control image data of the reference area, including: uniformly dividing the reference image data into grid blocks, predicting the geographic range corresponding to each grid block based on the initial rational polynomial parameters; performing coarse matching of feature points based on the top-level pyramid in parallel blocks, using an adaptive gross error removal method based on error distribution characteristics and probability error to remove gross error points, obtaining coarse matching points and correcting the initial rational polynomial parameters; performing sub-pixel fine matching based on multi-level pyramids in parallel blocks, again using an adaptive gross error removal method to remove gross error points, obtaining accurate corresponding points as dense matching points.

[0014] In some exemplary embodiments, a rigorous imaging model is established using densely matched points to calculate the payload's internal azimuth parameters and initial external azimuth elements. This includes: establishing a rigorous imaging model for each densely matched point, where the rigorous imaging model represents the relationship between the image point's view vector in the camera coordinate system and the coordinates of the ground point, the satellite platform position, the photography center offset, and the transformation matrices between the camera coordinate system and the satellite body coordinate system, the satellite body coordinate system and the inertial coordinate system, and the inertial coordinate system and the Earth-fixed coordinate system; based on the densely matched points and combined with preprocessed satellite auxiliary data, the least squares method is used to iteratively calculate the internal and external azimuth elements until the internal calibration parameter corrections are all less than a preset threshold, thereby obtaining the payload's internal azimuth parameters and initial external azimuth elements.

[0015] In some exemplary embodiments, a joint calibration model is established based on dense and sparse matching points to obtain the optimal external calibration parameters. This includes: establishing a joint calibration model based on dense and sparse matching points; linearizing the joint calibration model to establish a joint error equation, incorporating both dense and sparse matching points into the solution; the joint error equation includes a coefficient matrix, a constant vector, and a residual vector for the external calibration parameter corrections; assigning different weights to dense and sparse matching points, with the weight of sparse matching points being greater than that of dense matching points; iteratively solving for the external calibration parameter corrections using the least squares method, updating the external calibration parameters, until the corrections are less than a preset threshold, thus obtaining the optimal external calibration parameters.

[0016] In some exemplary embodiments, the weight of the sparse matching points is 1.5 to 2 times that of the dense matching points.

[0017] In some exemplary embodiments, the method further includes: evaluating the geometric calibration results based on the interior orientation calibration accuracy and the exterior orientation calibration accuracy.

[0018] According to a second aspect of the present invention, an optical constellation joint calibration device is provided, comprising: a data acquisition module for performing small-side-swing imaging on geometric calibration fields distributed in multiple regions globally to obtain imaging data of multiple regions; a parameter generation module for establishing a rigorous geometric positioning model of the payload based on initial satellite payload geometric parameters, satellite auxiliary data, and imaging data, and generating initial rational polynomial parameters; a first matching module for selecting one of the multiple geometric calibration fields as a reference region, and performing dense matching between the initial rational polynomial parameters and the geometric calibration field control image data of the reference region to obtain the pixel coordinates and corresponding geographic coordinates of the densely matched points; a first calculation module for establishing a rigorous imaging model using the densely matched points, and solving to obtain the payload interior orientation parameters and initial exterior orientation elements; a second matching module for performing sparse matching between the geometric calibration field imaging data of other regions and the corresponding geometric calibration field control image data based on the initial rational polynomial parameters, and obtaining the pixel coordinates and corresponding geographic coordinates of the sparsely matched points; and a second calculation module for establishing a joint calibration model based on the densely matched points and the sparsely matched points, solving to obtain the optimal exterior calibration parameters, and completing the optical constellation geometric calibration. According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0019] (III) Beneficial Effects

[0020] As can be seen from the above technical solutions, the optical constellation joint calibration method, apparatus, and device provided by the embodiments of the present invention have at least the following beneficial effects:

[0021] (1) Significantly improves the consistency of geometric positioning accuracy of satellites within a constellation globally. This invention integrates the geometric calibration problem of multiple satellites within a constellation into a joint solution framework by jointly utilizing geometric calibration field data distributed across multiple regions globally. This effectively overcomes the inherent limitation of traditional single calibration field methods, which can only optimize the geometric accuracy of local areas and cannot take into account global distribution. Experimental data show that after adopting the method of this invention, the geometric positioning error is reduced from 20.05 meters in the prior art to 14.03 meters, and the positioning accuracy is improved by about 30%. The geometric positioning performance of satellites within a constellation at different locations globally is significantly improved.

[0022] (2) The geometric stability of the calibration data is ensured by the small side-swing angle imaging constraint. Under the condition of small side-swing angle imaging, the present invention dynamically determines the imaging cone angle threshold based on the internal accuracy of geometric calibration, image resolution and digital elevation model accuracy, which ensures the geometric stability of the imaging data, provides a reliable data foundation for subsequent high-precision calibration, and avoids the impact of geometric distortion caused by excessive imaging angle on the calibration accuracy.

[0023] (3) Data preprocessing effectively improves the quality and usability of auxiliary data. This invention performs multi-level preprocessing operations such as deduplication, sorting and outlier removal on attitude, orbit and travel time data, effectively eliminating duplicate data, outliers and out-of-order data in the original auxiliary data, improving the quality of input data, providing accurate time and space references for the establishment of a rigorous geometric model, and fundamentally ensuring the reliability of calibration solution.

[0024] (4) A hierarchical matching strategy is adopted to balance solution efficiency and global representativeness. In this invention, dense matching is used in the reference region to obtain high-precision corresponding points for solving the load internal orientation parameters and initial external orientation elements; sparse matching is used in other regions to obtain a sufficient number of control points. This hierarchical matching strategy not only ensures the high accuracy requirement of the internal parameter solution, but also takes into account the information of other calibration field regions, making the joint solution more globally representative and avoiding the local overfitting problem that may be caused by a single calibration field.

[0025] (5) Global geometric consistency is optimized through weighted joint solution. In the joint calibration model, the present invention adopts a weighted solution strategy, giving higher weights to sparse matching points, so that the solution results focus more on maintaining the geometric consistency of different satellites in different regions of the constellation. This weighted joint solution method effectively suppresses over-optimization in local regions and realizes the overall coordination and global optimization of satellite geometric parameters in the constellation.

[0026] (6) The method is highly versatile and has a wide range of applications. The optical constellation joint calibration method proposed in this invention does not depend on a specific satellite platform or sensor type and can be widely applied to various optical remote sensing satellite constellation systems. It is of great significance for improving the geometric quality and application efficiency of multi-satellite collaborative observation data and has broad application prospects in many civilian fields such as resource surveys, environmental monitoring, surveying and mapping, and urban planning. Attached Figure Description

[0027] The above-described features, other objects, and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0028] Figure 1 A flowchart illustrating an optical constellation joint calibration method according to an embodiment of the present invention is shown schematically.

[0029] Figure 2 This schematically illustrates a flowchart of dense matching based on initial rational polynomial parameters and geometric calibration field control image data of a reference region according to an embodiment of the present invention.

[0030] Figure 3This schematically illustrates a flowchart of establishing a rigorous imaging model using densely matched points according to an embodiment of the present invention, and calculating the payload interior orientation parameters and initial exterior orientation elements.

[0031] Figure 4 This schematically illustrates a flowchart of establishing a joint calibration model based on dense and sparse matching points according to an embodiment of the present invention, and solving for the optimal external calibration parameters;

[0032] Figure 5 An optical constellation joint calibration apparatus according to an embodiment of the present invention is illustrated schematically;

[0033] Figure 6 A block diagram of an electronic device for an optical constellation joint calibration method according to an embodiment of the present invention is shown schematically. Detailed Implementation

[0034] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0035] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0036] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0037] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0038] Figure 1 A flowchart illustrating an optical constellation joint calibration method according to an embodiment of the present invention is shown schematically.

[0039] like Figure 1 As shown, an optical constellation joint calibration method according to an embodiment of the present invention includes steps S110 to S160.

[0040] In step S110, small side-swing angle imaging is performed on the geometric calibration fields distributed in multiple regions around the world to obtain imaging data for multiple regions.

[0041] In some exemplary embodiments, small lateral tilt imaging is performed on geometric calibration fields distributed across multiple regions globally. This includes setting imaging constraints to ensure that the imaging data used for calibration meets geometric stability requirements. The imaging constraint is that the imaging cone angle is less than a preset threshold. The preset threshold is determined based on the matching relationship between the internal accuracy of geometric calibration, the resolution of the image to be calibrated, and the accuracy of the digital elevation model data used for calibration. The preset threshold is obtained by dividing the product of the internal accuracy of geometric calibration and the resolution of the image to be calibrated by the accuracy of the digital elevation model data, and then taking the arctangent value.

[0042] For example, imaging constraints are set, and the geometric calibration field distributed across multiple regions globally is defined as follows: n represents the number of geometric calibration fields, which can be flexibly set according to the characteristics of the star cluster orbit and the coverage area; the condition for small side-swing angle imaging is that the imaging cone angle must be satisfied. Less than the set threshold , The determination is mainly based on the internal accuracy of geometric calibration (inerPs), the resolution (gsd) of the image to be calibrated, and the accuracy of the digital elevation model (DEM) data used for calibration. The decision is defined as follows:

[0043]

[0044] That is, a preset threshold. It is equal to the arctangent of the quotient obtained by dividing the product of the geometric calibration internal accuracy and the image resolution by the DEM accuracy.

[0045] For example, the internal precision of geometric calibration is 1 pixel (inerPs), the resolution to be calibrated (gsd) is 0.5 meters, and the precision of DEM data is... If it is 1 meter, then It is approximately 8.5 degrees.

[0046] In step S120, a rigorous geometric positioning model of the payload is established based on the initial satellite payload geometric parameters, satellite auxiliary data, and imaging data, and initial rational polynomial parameters are generated.

[0047] In an embodiment of the present invention, satellite auxiliary data includes attitude data, orbit data, and travel time data. Before step S2, the method further includes preprocessing the attitude data, orbit data, and travel time data. The preprocessing includes: deduplicating the attitude data and orbit data, wherein if the quaternion difference between two adjacent packets of attitude data is less than a preset attitude threshold and the time difference is less than a preset time threshold, they are determined to be duplicates and removed; if the position difference between two adjacent packets of orbit data is less than a preset orbit threshold and the time difference is less than a preset time threshold, they are determined to be duplicates and removed; sorting and outlier removal are performed on the deduplicated attitude data, orbit data, and travel time data, wherein outlier data is removed based on the median time and a physical threshold.

[0048] For example, an initial satellite payload geometric model is established based on measurements from the satellite laboratory, including the camera principal point position, pixel size, focal length, distortion value sampling points, and the camera-satellite mounting matrix, to obtain initial geometric parameters; attitude and orbit data are deduplicated, sorted, and outlier removed. Satellite payload auxiliary data packages are generally generated by... The attitude data is stored in rows, with a frequency of 100%. The orbital data frequency is , , and It is determined by the design of each satellite payload, such as the CCD payload of a certain type of optical remote sensing satellite ("CCD payload" refers to the core imaging sensor used by the satellite to acquire images). =8, 4Hz At 1Hz, this would result in approximately 900 repetitions of the attitude and orbit. For efficient use, deduplication is necessary. Repeated attitudes are removed if the following deduplication criteria are met:

[0049]

[0050] in For the first The time and quaternion values ​​of the pose are included. For the first +1 pack of attitude time and quaternion values.

[0051] Duplicate tracks are removed if the following deduplication criteria are met:

[0052]

[0053] in, For the first Package orbit data including time, satellite orbital positioning values, and velocity values. For the first +1 package containing orbital data including time, satellite orbital positioning values, and velocity values.

[0054] Due to transmission errors and other reasons, the time sequence of attitude and orbit data may be out of order. For subsequent interpolation applications, it is necessary to sort the data according to time.

[0055] Removing outliers from attitude data mainly involves removing data with temporal and attitude anomalies. The removal criteria are as follows:

[0056]

[0057] in, The quaternion error threshold. This is the time error threshold. This represents the temporal median of all attitude data within the imaging time period. and The threshold is determined based on the satellite's attitude control accuracy and continuous imaging time, such as: setting for , The time interval is 3600 seconds. The difference between the sum of squares of the unit quaternions and 1 is used to measure whether the satellite attitude maneuver is a purely rigid rotation without changing its size or shape. The smaller the difference, the more reliable the attitude.

[0058] Removing outliers from orbital data primarily involves removing data with time anomalies and orbital value anomalies. The removal criteria are as follows:

[0059]

[0060] in, The average speed. For satellite velocity error threshold, This is the time error threshold. This represents the median time of all orbital data within the imaging period. The distance from the satellite's designed orbital position to the Earth's center. This is the height threshold. , as well as The threshold is determined based on the satellite's designed orbital altitude, orbital velocity, orbital control accuracy, and continuous imaging time. For example, setting... It is 7.3 km / s. It is 1 km / s. It is 50km. It lasts for 3600 seconds.

[0061] The row time data needs to be sorted and outlier removed. Due to transmission errors and other reasons, row time order often becomes disordered or abnormal. Therefore, it needs to be sorted by time value, and then outlier removal is required. The removal criteria are as follows:

[0062]

[0063] in, When to act This represents the median time of all row-time data within the imaging time period. Here, represents the line frequency, and 'lines' represents the number of lines from the median time interval. The line frequency is determined by the imaging design of the satellite payload, such as... It takes 70 microseconds.

[0064] A rigorous geometric positioning model for the payload is established, generating initial rational polynomial parameters for positioning. The attitude, trajectory, and timing data, after outlier removal, are interpolated, and a rigorous geometric positioning model is constructed based on the interpolated values ​​and camera parameters.

[0065] Return to reference Figure 1 In step S130, a reference region is selected from multiple geometric calibration fields. Based on the initial rational polynomial parameters and the geometric calibration field of the reference region, dense matching is performed on the image data to obtain the pixel coordinates and corresponding geographic coordinates of the densely matched points.

[0066] In embodiments of the present invention, the reference area can be the area with the largest coverage area, rich texture features of ground features, and little variation.

[0067] In some exemplary embodiments, step S130 may specifically include steps S131 to S133, see details below. Figure 2 .

[0068] In step S131, grid blocks are uniformly divided on the reference image data, and the geographical range corresponding to each grid block is predicted based on the initial rational polynomial parameters.

[0069] For example, let the image size be... OK, The column, with a block size of k×k (e.g., k=1024), predicts the geographical extent based on the initial rational polynomial parameters and the corresponding geometric calibration field data.

[0070] In step S132, feature point coarse matching based on the top-level pyramid is performed in parallel in blocks. An adaptive gross error removal method based on error distribution characteristics and probability error is used to remove gross error points, obtain coarse matching points, and correct the initial rational polynomial parameters.

[0071] In step S133, sub-pixel fine matching based on multi-layer pyramid is performed in parallel in blocks, and gross error removal is once again used to remove gross error points and obtain accurate corresponding points as dense matching points.

[0072] In step S140, a rigorous imaging model is established using densely matched points, and the load interior orientation parameters and initial exterior orientation elements are calculated.

[0073] In some exemplary embodiments, step S140 may specifically include steps S141 to S142, see details below. Figure 3 .

[0074] In step S141, a rigorous imaging model is established for each densely matched point. The rigorous imaging model is represented by the relationship between the view vector of the image point in the camera coordinate system and the coordinates of the ground point, the position of the satellite platform, the offset of the photography center, and the transformation matrix between the camera coordinate system and the satellite body coordinate system, the satellite body coordinate system and the inertial coordinate system, and the inertial coordinate system and the ground-fixed coordinate system.

[0075] For example, establishing a rigorous imaging model for each point in the densely matched benchmark data.

[0076]

[0077] in, The first in the reference image The view vector of the CCD probe in the camera coordinate system corresponding to each densely matched point; This represents the total number of densely matched points. This is the transformation matrix from the satellite body coordinate system to the camera coordinate system; The first in the reference image The transformation matrix from the J2000 inertial coordinate system to the satellite body coordinate system corresponding to each densely matched point; The first in the reference image The rotation matrix from the Earth-Centered, Earth-Fixed (ECEF) coordinate system to the inertial coordinate system corresponding to each densely matched point; This is the proportionality coefficient; The first in the reference image The location of the satellite platform at the imaging time corresponding to each densely matched point in the ephemeris interpolation; The first in the reference image The geocentric rectangular coordinates of the intersection of the CCD probe's line of sight and the Earth's ellipsoid corresponding to each densely matched point. Let S be the offset of the photography center relative to the center of the satellite's coordinate system.

[0078] In step S142, based on dense matching points and combined with preprocessed satellite auxiliary data, the least squares method is used to iteratively solve the inner and outer orientation elements until the inner calibration parameter corrections are all less than the preset threshold, thereby obtaining the payload inner orientation parameters and the initial outer orientation elements.

[0079] For example, based on the dense matching points of the reference data, the load interior orientation parameters and initial exterior orientation elements are calculated, let:

[0080]

[0081] Then we have the following formula:

[0082]

[0083] Based on the reference image, the interior and exterior orientation elements are iteratively calculated using the least squares method using densely matched points. The iteration continues until all corrections to the interior calibration parameters are less than a threshold, thus obtaining the load's interior orientation parameters. and initial exterior orientation elements .

[0084] In step S150, for geometric calibration field imaging data of other regions, sparse matching is performed based on the initial rational polynomial parameters and the corresponding geometric calibration field control image data to obtain the pixel coordinates and corresponding geographic coordinates of the sparse matching points.

[0085] In step S160, a joint calibration model is established based on dense and sparse matching points, and the optimal external calibration parameters are calculated to complete the optical constellation geometric calibration.

[0086] In some exemplary embodiments, step S160 may specifically include steps S161 to S164, see details below. Figure 4 .

[0087] In step S161, a joint calibration model is established based on dense matching points and sparse matching points.

[0088] In step S162, the joint calibration model is linearized to establish a joint error equation, which incorporates both dense and sparse matching points into the solution. The joint error equation includes the coefficient matrix, constant vector, and residual vector of the external calibration parameter corrections.

[0089] In step S163, different weights are set for dense matching points and sparse matching points, wherein the weight of sparse matching points is greater than the weight of dense matching points.

[0090] In some exemplary embodiments, the weight of sparse matching points is 1.5 to 2 times that of dense matching points.

[0091] In step S164, the least squares method is used to iteratively solve the correction number of the external calibration parameter, and the external calibration parameter is updated until the correction number is less than the preset threshold, thus obtaining the optimal external calibration parameter.

[0092] For example, a joint calibration model can be established based on dense and sparse matching points.

[0093]

[0094] Solve the calibration model to obtain the optimal external calibration parameters, and assume...

[0095]

[0096] The solution process is as follows:

[0097]

[0098] Linearize the above equation to establish the joint error equation.

[0099]

[0100] in,

[0101]

[0102] In the formula, It utilizes the interior orientation elements generated in step S4. and initial exterior orientation elements Substitute into the formula The constant vector obtained is used in subsequent iterations and in the external scaling parameters generated in each iteration. Substitute the constant vector obtained from the calculation, It is the coefficient matrix of the error equation. External calibration parameter corrections , The weights are assigned to the observations of densely matched points and sparsely matched points. In the joint solution, the weights of sparsely matched points are higher than those of densely matched points, for example: =0.5, =1.0, calculated using least squares. ,

[0103]

[0104] Update the external calibration parameters using the above formula current value

[0105]

[0106] Repeat step S160, iterating the joint solution until the external calibration parameter correction is less than a threshold. For example, the threshold can be set to... .

[0107] exist Figure 1 Based on the method shown, the method according to embodiments of the present invention may further include: evaluating the geometric calibration results based on the internal and external calibration accuracy. The geometric calibration accuracy can be evaluated using two indicators: internal and external calibration accuracy. Internal calibration accuracy is measured by statistically analyzing the residuals of densely matched image points involved in the calculation and the consistency of parameters within multiple images, reflecting the accuracy of the calculation of the payload's internal geometric parameters. External calibration accuracy is quantified through methods such as the positioning deviation of independent ground control points, the registration error of corresponding points in overlapping areas of adjacent images, and cross-validation of multi-region calibration fields, evaluating the accuracy of determining satellite attitude, orbital parameters, and installation relationships. This evaluation step not only achieves comprehensive quality monitoring of the calibration results and provides quantitative feedback for parameter optimization, but also significantly enhances the credibility of the constellation geometric positioning results by objectively verifying the applicability and stability of the joint calibration method in different regions globally, laying a solid foundation for long-term monitoring of constellation geometric performance and collaborative application of multi-satellite data.

[0108] Table 1 schematically illustrates the geometric positioning deviation of products produced using coefficients calibrated with existing methods and the geometric positioning deviation of products produced using coefficients calibrated with the embodiments of the present invention. As shown in Table 1, after geometric calibration using existing technology (calibration method based on a single geometric calibration field), the geometric positioning error of the satellite imagery is 20.05 meters; while after using the optical constellation joint calibration method provided by the embodiments of the present invention, the geometric positioning error is reduced to 14.03 meters, improving the positioning accuracy by approximately 30%. Experimental results show that the embodiments of the present invention, by jointly utilizing data from multiple global geometric calibration fields and uniformly calculating the geometric calibration parameters of satellites within a constellation, effectively compensate for the geometric system errors generated during the on-orbit operation of satellites within a constellation, significantly improving the geometric positioning consistency of satellites in different locations globally, and verifying the superiority and practical value of the method of the present invention compared to existing technologies.

[0109] Table 1. Comparison of geometric positioning deviations of products produced by coefficients generated through calibration in existing solutions and embodiments of the present invention.

[0110]

[0111] Figure 5 An optical constellation joint calibration apparatus according to an embodiment of the present invention is illustrated schematically.

[0112] like Figure 5 As shown, the optical constellation joint calibration device 800 of this embodiment includes a data acquisition module 810, a parameter generation module 820, a first matching module 830, a first calculation module 840, a second matching module 850, and a second calculation module 860.

[0113] The data acquisition module 810 is used to perform small side-swing angle imaging of geometric calibration fields distributed in multiple regions around the world to obtain imaging data of multiple regions. In one embodiment, the data acquisition module 810 can be used to perform the operation S110 described above, which will not be repeated here.

[0114] The parameter generation module 820 is used to establish a rigorous geometric positioning model of the payload based on the initial satellite payload geometric parameters, satellite auxiliary data and imaging data, and generate initial rational polynomial parameters. In one embodiment, the parameter generation module 820 can be used to perform the operation S120 described above, which will not be repeated here.

[0115] The first matching module 830 is used to select one of multiple geometric calibration fields as a reference area, and control the image data to perform dense matching based on the initial rational polynomial parameters and the geometric calibration field of the reference area to obtain the pixel coordinates and corresponding geographic coordinates of the dense matching points. In one embodiment, the first matching module 830 can be used to perform the operation S130 described above, which will not be repeated here.

[0116] The first calculation module 840 is used to establish a rigorous imaging model using dense matching points and calculate the load interior orientation parameters and initial exterior orientation elements. In one embodiment, the first calculation module 840 can be used to perform the operation S140 described above, which will not be repeated here.

[0117] The second matching module 850 is used to perform sparse matching of geometric calibration field imaging data of other regions with the corresponding geometric calibration field control image data based on the initial rational polynomial parameters, to obtain the pixel coordinates and corresponding geographic coordinates of the sparse matching points. In one embodiment, the second matching module 850 can be used to perform the operation S150 described above, which will not be repeated here.

[0118] The second calculation module 860 is used to establish a joint calibration model based on dense matching points and sparse matching points, calculate the optimal external calibration parameters, and complete the optical constellation geometric calibration. In one embodiment, the second calculation module 860 can be used to perform the operation S160 described above, which will not be repeated here.

[0119] According to embodiments of the present invention, any plurality of modules among the data acquisition module 810, parameter generation module 820, first matching module 830, first calculation module 840, second matching module 850, and second calculation module 860 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of the present invention, at least one of the data acquisition module 810, parameter generation module 820, first matching module 830, first calculation module 840, second matching module 850, and second calculation module 860 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in hardware or firmware, or in any one of software, hardware, and firmware implementations, or in a suitable combination of any of these. Alternatively, at least one of the data acquisition module 810, parameter generation module 820, first matching module 830, first calculation module 840, second matching module 850, and second calculation module 860 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0120] Figure 6 A block diagram of an electronic device for an optical constellation joint calibration method according to an embodiment of the present invention is shown schematically.

[0121] like Figure 6 As shown, an electronic device 900 according to an embodiment of the present invention includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0122] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 902 and / or RAM 903. It should be noted that programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in one or more memories.

[0123] According to an embodiment of the present invention, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0124] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0125] According to embodiments of the present invention, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present invention, a computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0126] Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or combinations fall within the scope of the present invention.

Claims

1. A method for joint calibration of optical star clusters, characterized in that, include: Small side-swing angle imaging was performed on geometric calibration fields distributed in multiple regions around the world to obtain imaging data for multiple regions; A rigorous geometric positioning model for the payload is established based on the initial satellite payload geometric parameters, satellite auxiliary data, and the imaging data, and initial rational polynomial parameters are generated. Select one of the multiple geometric calibration fields as a reference region, and perform dense matching of the image data controlled by the geometric calibration field of the reference region based on the initial rational polynomial parameters to obtain the pixel coordinates and corresponding geographic coordinates of the densely matched points. A rigorous imaging model is established using the densely matched points, and the load interior orientation parameters and initial exterior orientation elements are calculated. For geometric calibration field imaging data in other regions, sparse matching is performed based on the initial rational polynomial parameters and the corresponding geometric calibration field control image data to obtain the pixel coordinates and corresponding geographic coordinates of the sparse matching points. as well as A joint calibration model is established based on the dense matching points and the sparse matching points, and the optimal external calibration parameters are calculated to complete the optical constellation geometric calibration.

2. The method according to claim 1, characterized in that, The small-side-swing-angle imaging of geometrically calibrated fields distributed across multiple regions globally includes: An imaging constraint is set to ensure that the imaging data used for calibration meets the geometric stability requirements. The imaging constraint is that the imaging cone angle is less than a preset threshold. The preset threshold is determined based on the matching relationship between the internal accuracy of geometric calibration, the resolution of the image to be calibrated, and the accuracy of the digital elevation model data used for calibration. The preset threshold is obtained by dividing the product of the geometric calibration internal accuracy and the resolution of the image to be calibrated by the accuracy of the digital elevation model data, and then taking the arctangent value.

3. The method according to claim 1, characterized in that, The satellite auxiliary data includes attitude data, orbit data, and travel time data. The method further includes preprocessing the attitude data, orbit data, and travel time data, the preprocessing including: The attitude data and orbit data are deduplicated. Specifically, if the quaternion difference between two adjacent attitude data packets is less than a preset attitude threshold and the time difference is less than a preset time threshold, they are determined to be duplicates and removed. Similarly, if the position difference between two adjacent orbit data packets is less than a preset orbit threshold and the time difference is less than a preset time threshold, they are determined to be duplicates and removed. The deduplicated attitude data, orbit data, and travel time data are sorted and outlier removal is performed respectively, with outlier data being removed based on the median time and physical threshold.

4. The method according to claim 1, characterized in that, The dense matching based on the initial rational polynomial parameters and the geometric calibration field control image data of the reference region includes: The reference image data is uniformly divided into grid blocks, and the geographic range corresponding to each grid block is predicted based on the initial rational polynomial parameters. The feature point coarse matching based on the top-level pyramid is performed in a block-parallel manner. An adaptive gross error removal method based on error distribution characteristics and probability error is used to remove gross error points, obtain coarse matching points, and correct the initial rational polynomial parameters. The sub-pixel fine matching based on a multi-layer pyramid is performed in parallel in blocks. The adaptive gross error removal method is used again to remove gross error points and obtain accurate corresponding points as dense matching points.

5. The method according to claim 1, characterized in that, The process of establishing a rigorous imaging model using the densely matched points and solving for the payload's interior orientation parameters and initial exterior orientation elements includes: A rigorous imaging model is established for each densely matched point. The rigorous imaging model is represented by the relationship between the view vector of the image point in the camera coordinate system and the coordinates of the ground point, the position of the satellite platform, the offset of the photography center, and the transformation matrix between the camera coordinate system and the satellite body coordinate system, the satellite body coordinate system and the inertial coordinate system, and the inertial coordinate system and the ground-fixed coordinate system. Based on the dense matching points and combined with preprocessed satellite auxiliary data, the least squares method is used to iteratively solve the inner and outer orientation elements until the inner calibration parameter corrections are all less than a preset threshold, thereby obtaining the payload inner orientation parameters and the initial outer orientation elements.

6. The method according to claim 1, characterized in that, The step of establishing a joint calibration model based on the dense matching points and the sparse matching points, and solving for the optimal external calibration parameters, includes: A joint calibration model is established based on the dense matching points and the sparse matching points; The joint calibration model is linearized to establish a joint error equation, which incorporates both dense and sparse matching points into the solution. The joint error equation includes the coefficient matrix, constant vector, and residual vector of the external calibration parameter correction. Set different weights for dense and sparse matching points, with the weight of sparse matching points being greater than that of dense matching points; The least squares method is used to iteratively solve for the correction of the external calibration parameters, and the external calibration parameters are updated until the correction is less than a preset threshold, thus obtaining the optimal external calibration parameters.

7. The method according to claim 6, characterized in that, The weight of the sparse matching points is 1.5 to 2 times that of the dense matching points.

8. The method according to claim 1, characterized in that, Also includes: The geometric calibration results are evaluated based on the accuracy of the interior and exterior orientation calibrations.

9. An optical constellation joint calibration device, characterized in that, The device includes: The data acquisition module is used to perform small-side-swing-angle imaging of geometric calibration fields distributed in multiple regions around the world to obtain imaging data of multiple regions; The parameter generation module is used to establish a rigorous geometric positioning model of the payload based on the initial satellite payload geometric parameters, satellite auxiliary data, and the imaging data, and to generate initial rational polynomial parameters. The first matching module is used to select one of the multiple geometric calibration fields as a reference region, and to perform dense matching of the image data controlled by the geometric calibration field of the reference region based on the initial rational polynomial parameters, so as to obtain the pixel coordinates and corresponding geographic coordinates of the dense matching points. The first calculation module is used to establish a rigorous imaging model using the densely matched points and calculate the load interior orientation parameters and the initial exterior orientation elements. The second matching module is used to perform sparse matching between the geometric calibration field imaging data of other regions and the corresponding geometric calibration field control image data based on the initial rational polynomial parameters, to obtain the pixel coordinates and corresponding geographic coordinates of the sparse matching points; and The second calculation module is used to establish a joint calibration model based on the dense matching points and the sparse matching points, solve for the optimal external calibration parameters, and complete the optical constellation geometric calibration.

10. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more computer programs. The one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

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