Satellite radiation product-based remote sensing image geometric calibration method, electronic device and medium

Abstract

The application discloses a kind of satellite radiation product-based remote sensing image geometric calibration method, electronic equipment and medium.The method can include: determining reference image, inputting image to be calibrated;For the same name point matching of image to be calibrated and reference image, obtain the same name point coordinates;The third polynomial distortion model about CCD probe element number is constructed;Based on the same name point coordinates, curve fitting is carried out on the distortion model, and the distortion coefficient is obtained;The resampling of image to be calibrated is carried out by distortion coefficient, and the correction of internal distortion of image is realized.The application can quickly and accurately carry out geometric calibration processing to satellite image, greatly improve the efficiency and quality of image processing.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing image processing technology, and more specifically, to a method, electronic device and medium for geometric verification of remote sensing images based on satellite radiation products. Background Technology

[0002] With the rapid development of satellite remote sensing technology, high spatial resolution, multi-temporal, and multi-payload satellite remote sensing images are constantly emerging, and users' demands for the quality of multi-source remote sensing image products are also increasing. However, after satellite launch, many factors can lead to errors and distortions in the imaging images. The space environment is complex and changeable, with significant differences in temperature, radiation, and other conditions compared to the ground, which can affect the satellite's hardware. At the same time, radial distortion of the camera's optical system is also prominent. There are deviations in the pointing angles of the multi-band detectors on the spaceborne camera payload, and errors are inevitable during assembly. Changes in the external environment, such as temperature and atmospheric conditions, especially distortion of the camera's optical lens and changes in the image controller (CCD) detector itself, such as changes in detector size, CCD linear array bending, rotation, and center shift, all contribute to the internal distortion of the remote sensing images.

[0003] The elimination of internal distortion falls under the category of internal calibration in satellite remote sensing image geometric calibration. Currently, in international research on geometric calibration of high-resolution satellite pushbroom imaging, rigorous geometric modeling and on-orbit geometric calibration are typically considered together. Regarding rigorous geometric model research, numerous publications comprehensively review the latest advancements in geometric modeling for high-resolution pushbroom satellites. Researchers have focused on different aspects of the nonlinear collinearity equations, such as interior orientation, exterior orientation, and additional parameters. By acquiring ground control points or corresponding points between images, they use the least squares adjustment principle to solve for the optimal solution of the collinearity equation parameters. However, during the solution process, the correlation of additional parameters often leads to difficulties in convergence. Other studies have established a general sensor model for high-resolution pushbroom satellite sensor modeling. This model only uses spline functions to interpolate the orbit and attitude parameters at the satellite imaging time to eliminate systematic errors in the attitude and orbit parameters, but it does not consider modeling the sensor's interior orientation. Some researchers have also conducted mathematical modeling studies on the self-calibration of photogrammetric cameras with additional parameters, based on the principle of adjustment using the central projection bundle method for remote sensing images. They have also developed corresponding geometric calibration measurement software for high-resolution remote sensing images, which is used to calculate the geometric errors of the internal and external orientation parameters of the photogrammetric camera and to complete the direct geometric positioning of frame or linear array push-broom imaging.

[0004] In China, Hao Xuetao from the China Center for Resources Satellite Data and Applications and Xu Yuguo from the Institute of Electronics, Chinese Academy of Sciences, proposed an on-orbit geometric distortion calibration method for linear pushbroom CCD cameras based on angle invariance, specifically for on-orbit geometric calibration of spaceborne remote sensing cameras. This method, based on the principle that the camera's exterior elements have a relatively small impact on the angle between the camera's view vectors, uses a least-squares algorithm with reference to ground control points to solve for the angle between the camera's view vectors based on first-level image products, thereby constructing an internal distortion model of the linear array camera and achieving on-orbit geometric calibration of the camera's internal orientation elements. Wei Xinguo et al. from Beijing University of Aeronautics and Astronautics, to improve the geometric positioning accuracy of high-resolution image products, studied an on-orbit calibration method for the cross-linking angle between the principal optical axis of the star sensor and the principal optical axis of the remote sensing camera. Utilizing a rigorous imaging model of high-resolution satellites, they introduced an improved indirect solution model into the calibration of remote sensing camera installation errors, proposing a method for unified calibration of the internal and external orientation elements of the remote sensing camera, achieving accurate compensation for system errors between the onboard attitude determination system and the remote sensing camera. By processing the entire orbital remote sensing image, the correlation matrix of the attitude of the remote sensing camera and star sensor is determined using an optimization model, and then the cross-linking angle is detected to compensate for the geometric positioning error in the rigorous imaging model.

[0005] The construction of these on-orbit geometric calibration models largely relies on building geometric calibration fields in flat, field areas. The construction and maintenance of these fields require significant investment in equipment, such as high-precision measuring instruments; substantial manpower, including professional surveyors and technical engineers; material resources, such as transporting various equipment and materials to the calibration fields; and substantial financial investment, from site rental and construction to equipment purchase and maintenance. Furthermore, geometric calibration demands a high level of expertise and technical skill in both field and office calculations, requiring specialized technicians to utilize complex algorithms and software for data processing and analysis. This not only increases the cost of geometric calibration but also limits its application scope and operability.

[0006] Currently, there is a need to develop a geometric calibration method for remote sensing images based on satellite radiometric products.

[0007] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention, and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] This invention proposes a method, electronic device, and medium for geometric verification of remote sensing images based on satellite radiation products. It can quickly and accurately perform geometric verification processing on satellite images, improving the efficiency and quality of image processing.

[0009] In a first aspect, embodiments of this disclosure provide a method for geometric calibration of remote sensing images based on satellite radiometric products, including:

[0010] Identify the reference image and input the image to be checked;

[0011] The corresponding points are matched between the image to be inspected and the reference image to obtain the coordinates of the corresponding points;

[0012] Construct a cubic polynomial distortion model for the number of CCD detector elements;

[0013] Based on the coordinates of the corresponding points, the distortion model is curve-fitted to obtain the distortion coefficients;

[0014] The distortion coefficient is used to resample the image to be inspected, thereby correcting the distortion within the image.

[0015] Preferably, L1-level high-resolution optical satellite imagery that has undergone internal distortion correction is used as the reference image.

[0016] Preferably, the cubic polynomial distortion model is:

[0017] Δx=a0+a1×x+a2×x 2 +a3×x 3

[0018] Δy=b0+b1×x+b2×x 2 +b3×x 3

[0019] In the formula, a0, a1, a2, a3, b0, b1, b2, b3 are distortion coefficients, and x is the number of CCD detectors in the image to be inspected.

[0020] Preferably, after matching corresponding points, the process further includes:

[0021] The spatial resolution of the matched points is normalized to obtain the coordinates of the matched points.

[0022] Preferably, the distortion model is a cubic polynomial.

[0023] Preferably, curve fitting of the distortion model based on the coordinates of the corresponding points includes:

[0024] The coordinates of the corresponding points are plotted on a coordinate system, and the distortion model is subjected to curve fitting to calculate the distortion coefficients.

[0025] Preferably, after obtaining the fixed distortion of each CCD probe in the CCD line scanning direction and flight direction, the image to be inspected is resampled using the cubic convolution method.

[0026] Secondly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0027] Memory, which stores executable instructions;

[0028] A processor that executes the executable instructions in the memory to implement the aforementioned method for geometric verification of remote sensing images based on satellite radiometric products.

[0029] Thirdly, this disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned remote sensing image geometric calibration method based on satellite radiation products.

[0030] Its beneficial effects are as follows:

[0031] 1. Effective elimination of internal distortion: By constructing a model based on the fixed bias principle of camera CCD and employing a sub-pixel matching method, the fixed bias of each CCD element can be accurately calculated, thereby establishing an accurate internal distortion model. Using this model to resample the image to be inspected can effectively eliminate the internal distortion of the image, making the image more accurately reflect the true shape and location of ground features;

[0032] 2. Improved Geometric Positioning Accuracy of Satellite Imagery: After eliminating internal distortions, the geometric positioning accuracy of satellite imagery is significantly improved. Experimental verification shows that using this method to process satellite imagery requires only 5 control points to achieve a positioning accuracy of approximately 1 pixel. This provides a more accurate data foundation for the application of satellite imagery in geographic information analysis, map production, environmental monitoring, and other fields, helping to improve the accuracy and reliability of related applications.

[0033] 3. Reduced Costs and Technical Difficulty: Unlike traditional on-orbit geometric calibration methods that rely on field calibration sites, this method eliminates the need for costly calibration sites, reducing investment in equipment, manpower, materials, and capital. Furthermore, the method's relatively simple operation reduces the professional and technical requirements for both field and office calculations, improving the operability of geometric calibration and enabling more researchers and application units to easily use this method for satellite image geometric calibration.

[0034] 4. Universality: The internal distortion model proposed in this patent has strong universality and has been successfully incorporated into the operational system for satellite ground preprocessing. This model is also applicable to the geometric internal calibration of other satellite remote sensing images, providing a universal and effective solution for geometric calibration across the entire satellite remote sensing image field, and promoting the development and application of satellite remote sensing image processing technology.

[0035] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0036] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0037] Figure 1 A flowchart illustrating the steps of a remote sensing image geometric calibration method based on satellite radiometric products according to an embodiment of the present invention is shown. Detailed Implementation

[0038] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0039] Figure 1 A flowchart illustrating the steps of a remote sensing image geometric calibration method based on satellite radiometric products according to an embodiment of the present invention is shown.

[0040] like Figure 1 As shown, the geometric calibration method for remote sensing images based on satellite radiometric products includes:

[0041] Step 101: Determine the reference image and input the image to be checked;

[0042] Step 102: Match corresponding points between the image to be checked and the reference image to obtain the coordinates of the corresponding points;

[0043] Step 103: Construct a cubic polynomial distortion model for the number of CCD detector elements;

[0044] Step 104: Perform curve fitting on the distortion model based on the coordinates of corresponding points to obtain the distortion coefficients;

[0045] Step 105: Resample the image to be inspected using the distortion coefficient to correct the distortion within the image.

[0046] In one example, L1-level high-resolution optical satellite imagery with internal distortion correction is used as the reference image.

[0047] In one example, the cubic polynomial distortion model is:

[0048] Δx=a0+a1×x+a2×x 2 +a3×x 3

[0049] Δy=b0+b1×x+b2×x 2 +b3×x 3

[0050] In the formula, a0, a1, a2, a3, b0, b1, b2, b3 are distortion coefficients, and x is the number of CCD detectors in the image to be inspected.

[0051] In one example, matching corresponding points also includes:

[0052] The spatial resolution of the matched points is normalized to obtain their coordinates.

[0053] In one example, the distortion model is a cubic polynomial.

[0054] In one example, curve fitting of the distortion model based on the coordinates of corresponding points includes:

[0055] Plot the coordinates of the corresponding points onto the coordinate system, perform curve fitting on the distortion model, and calculate the distortion coefficients.

[0056] In one example, after obtaining the fixed distortion of each CCD element in the CCD line scanning direction and flight direction, the image to be checked is resampled using cubic convolution.

[0057] Specifically, based on the fixed bias principle of the camera's CCD, a fixed bias model of the linear CCD detector is constructed between the image to be calibrated and the reference image (radioproducts after internal distortion correction) using the sub-pixel matching method. This model utilizes the characteristic that satellite remote sensing camera CCD detectors have fixed biases during the imaging process. By accurately analyzing and calculating these biases, the internal distortion of the image can be described more precisely.

[0058] L1-level very high resolution optical satellite imagery, after internal distortion correction, was selected as the reference image. Simultaneously, satellite imagery with the same temporal phase for the corresponding area was selected as the calibration image. This selection ensures a high degree of similarity between the reference and calibration images in terms of imaging conditions and ground features, providing a reliable foundation for subsequent matching and analysis.

[0059] The image principal point is set in the image plane coordinate system, with the image center point (0, 0) designated as the principal point. Simultaneously, the image resolution is set to unit pixels. This facilitates the normalization of the image plane coordinates, making the subsequent calculation of interior orientation geometric distortion simpler and more accurate, thus improving computational efficiency and precision.

[0060] For the inspection and reference images, a method based on image grayscale or feature correlation is used to perform corresponding point matching, and a matching model is established. For images acquired with different or the same payloads at similar temporal phases, thousands to tens of thousands of points can be uniformly matched with a matching error of less than 1 pixel, or even less than 0.5 pixels, thus effectively ensuring the accuracy of the model and providing high-precision matching point pairs for subsequent analysis.

[0061] When performing internal distortion correction on satellite imagery, a cubic polynomial distortion model with respect to the number of CCD sensors is designed to represent the internal distortion patterns of the imagery:

[0062] Δx=a0+a1×x+a2×x 2 +a3×x 3

[0063] Δy=b0+b1×x+b2×x 2 +b3×x 3

[0064] In the formula, a0, a1, a2, a3, b0, b1, b2, b3 are internal distortion coefficients, and x is the number of CCD detectors in the image to be inspected. This model can accurately describe the distortion of the image in the CCD line scanning direction and flight direction.

[0065] Because the pixel resolutions of the two images are inconsistent, the resolutions of the two images need to be normalized when establishing the coordinate difference Δline,Δsample between the two images:

[0066]

[0067] In the formula, subscript 1 represents the image to be calibrated, and subscript 2 represents the reference image. Resolution normalization can eliminate errors caused by resolution differences, making subsequent calculations more accurate.

[0068] Using Δx and Δy as dependent variables and the x-coordinate (camera sensor number) as the independent variable, a cubic polynomial was used for curve fitting to calculate the polynomial coefficients (distortion coefficients). Curve fitting yields coefficients reflecting image distortion patterns, providing crucial parameters for subsequent interpolation (resampling).

[0069] After obtaining the fixed distortion (deviation) of each CCD element in the CCD line scanning direction and flight direction, the image to be inspected is resampled using cubic convolution, thereby correcting the internal distortion of the image. Cubic convolution can accurately resample the image while ensuring image quality, effectively eliminating the internal distortion of the image.

[0070] This invention uses image correlation to match hundreds or thousands of corresponding control points by referencing radiation products that have undergone internal distortion correction. When calculating the geometric difference between the corresponding control points in the CCD line scan direction (X direction) and flight direction (Y direction), the spatial resolution of the image to be examined needs to be normalized. The least squares curve fitting method is used to generate the deviation of each CCD probe in the two directions (X direction and Y direction). The generated geometric fixed deviation is added to the pixel coordinates of the image to be examined in the pixel coordinate system to obtain new pixel coordinates. Finally, the internal distortion of the image is corrected by indirect resampling.

[0071] The present invention also provides an electronic device, comprising: a memory storing executable instructions; and a processor that executes the executable instructions in the memory to implement the above-described method for geometric verification of remote sensing images based on satellite radiometric products.

[0072] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for geometric verification of remote sensing images based on satellite radiometric products.

[0073] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0074] Example 1

[0075] 1. Experimental Scheme for Reference Satellite Remote Sensing Images Imaging at Different Side-Switch Angles: To thoroughly investigate the impact of the side-switch angle of reference satellite images on the establishment of a distortion model, reference satellite remote sensing images imaging at different side-switch angles are selected to establish a distortion model, which is then used to eliminate the internal distortion of the image to be calibrated. Specifically, high-resolution satellite images can be selected as experimental and reference data in the experiment. By setting different side-switch angles, the distortion of images under different side-switch angle conditions is comprehensively examined, thereby establishing a more targeted and accurate distortion model.

[0076] Experimental and Reference Data Selection: Satellite imagery at different lateral tilt angles was selected as both the imagery to be calibrated and the reference imagery. This selection ensures the data is representative and comprehensively reflects the characteristics of the imagery at different lateral tilt angles.

[0077] Internal distortion model establishment: Using very high resolution satellite imagery as a reference, corresponding points are obtained through image correlation matching. Then, combined with least squares fitting, the fixed deviations of each CCD element in the horizontal scanning direction and flight direction are accurately calculated. These deviations reveal the internal distortion patterns of the satellite imagery, and distortion curves are plotted. The model shows that the distortion is greater for elements farther from the center point, and the maximum distortion in the CCD horizontal scanning direction is greater than that along the orbital flight direction.

[0078] Accuracy testing: First, geometric correction is performed. Five control points are evenly distributed at the four corners and the center point, and an affine transformation model (first-order polynomial model) is used to perform geometric correction on the resampled high-resolution satellite image. Then, accuracy is checked. Using the very high-resolution satellite orthophoto as a reference, 20 check points are evenly selected on both the image to be checked and the reference image, and the corresponding root mean square error is calculated to obtain the geometric positioning error in the plain area.

[0079] Comparative Analysis of Internal Distortion Models at Different Side-Switch Angles: In-depth comparative analysis of the internal distortion curves established at different side-switch angles reveals that the same side-switch angle produces approximately identical distortion curves. The distortion model or curve is closely related to the side-switch angle of the satellite image; the larger the side-switch angle, the more pronounced the distortion becomes at the edges. Furthermore, the distortion curves at larger side-switch angles exhibit a certain slope, and the rotated curves are essentially identical to those at smaller side-switch angles. This suggests that the sloped distortion curves are due to the camera platform and the side-switch angle.

[0080] 2. Experimental Scheme for Reference Satellite Remote Sensing Images with Different Image Rows: To study the correlation between the number of reference satellite image rows and the establishment of the distortion model, a distortion model was established using reference satellite remote sensing images with different image rows, thereby eliminating the internal distortion of the image to be calibrated. Satellite imagery was also selected as experimental and reference data. By changing the number of image rows, the variation pattern of image distortion was observed, providing a basis for establishing a stable and reliable distortion model.

[0081] Experimental and reference data selection: Satellite imagery with the same lateral tilt angle, in the same column, but different rows was selected as both calibration and reference imagery. This data covers different image row situations, which helps to study the impact of the number of image rows on the distortion model.

[0082] Internal distortion model establishment: Using very high resolution satellite imagery as a reference, and based on corresponding points obtained through image correlation matching, combined with least squares fitting, the fixed deviations of each CCD element in the horizontal scanning direction and flight direction are calculated. This allows the acquisition of the internal distortion patterns of the satellite imagery, and distortion curves are plotted. The curves show that the distortion is greater for elements farther from the center point, and the maximum distortion in the CCD horizontal scanning direction is greater than that along the orbital flight direction.

[0083] Accuracy testing: Five control points are evenly distributed at the four corners and the center point. An affine transformation model (first-order polynomial model) is used to perform geometric correction on the resampled high-resolution satellite image. Using a very high-resolution satellite orthophoto as a reference, 20 check points are evenly selected on the image to be calibrated and the reference image. The corresponding root mean square error is calculated to obtain the geometric positioning error in the plain area.

[0084] Comparative Analysis of Distortion Models within Different Row Numbers of Images: Since this experiment targeted image data from the same orbit, theoretically, the models established for images from the same orbit should be consistent. Comparative analysis of the distortion curves of satellite images with different row numbers shows that images from the same orbit have essentially the same distortion model or curve. Furthermore, because the experimental distortion model is based on a fixed bias for each CCD element, changes in the row number are irrelevant to the final establishment of the distortion model, provided the CCD elements remain constant.

[0085] This invention has found practical applications in high-resolution satellite image processing:

[0086] Satellite images are processed by employing a geometric calibration method for remote sensing images based on satellite radiometric products.

[0087] First, following the steps in the above-mentioned experimental process, select appropriate reference images and images to be inspected, and perform operations such as setting image point coordinates and matching corresponding points to establish an accurate internal distortion model.

[0088] Then, by resampling the satellite imagery using this model, internal distortions can be effectively eliminated. The processed high-resolution satellite imagery exhibits significantly improved geometric positioning accuracy, requiring only 5 control points to achieve a positioning accuracy of approximately 1 pixel.

[0089] Currently, this technology has been successfully integrated into the operational ground preprocessing system for high-resolution satellites. In actual operation, the system can quickly and accurately perform geometric verification processing on satellite imagery, greatly improving the efficiency and quality of image processing. Furthermore, the versatility of this technology provides important reference and guidance for the geometric verification of other high-resolution satellite remote sensing imagery, promoting the development of satellite remote sensing image processing technology in practical applications.

[0090] Example 2

[0091] This disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned method for geometric verification of remote sensing images based on satellite radiometric products.

[0092] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0093] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0094] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0095] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0096] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0097] Example 3

[0098] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned remote sensing image geometric calibration method based on satellite radiometric products.

[0099] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0100] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0101] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0102] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for geometric calibration of remote sensing images based on satellite radiometric products, characterized in that, include: Identify the reference image and input the image to be checked; The corresponding points are matched between the image to be inspected and the reference image to obtain the coordinates of the corresponding points; Construct a cubic polynomial distortion model for the number of CCD detector elements; Based on the coordinates of the corresponding points, the distortion model is curve-fitted to obtain the distortion coefficients; The distortion coefficient is used to resample the image to be inspected, thereby correcting the distortion within the image.

2. The method for geometric verification of remote sensing images based on satellite radiometric products according to claim 1, wherein, The L1-level high-resolution optical satellite imagery, after internal distortion correction, is used as the reference image.

3. The method for geometric verification of remote sensing images based on satellite radiometric products according to claim 1, wherein, The cubic polynomial distortion model is as follows: Δx=a0+a1×x+a2×x 2 +a3×x 3 Δy=b0+b1×x+b2×x 2 +b3×x 3 In the formula, a0, a1, a2, a3, b0, b1, b2, b3 are distortion coefficients, and x is the number of CCD detectors in the image to be inspected.

4. The method for geometric verification of remote sensing images based on satellite radiometric products according to claim 1, wherein, Matching with the same name also includes: The spatial resolution of the matched points is normalized to obtain the coordinates of the matched points.

5. The method for geometric verification of remote sensing images based on satellite radiometric products according to claim 1, wherein, The distortion model is a cubic polynomial.

6. The method for geometric verification of remote sensing images based on satellite radiometric products according to claim 1, wherein, Curve fitting of the distortion model based on the coordinates of the corresponding points includes: The coordinates of the corresponding points are plotted on a coordinate system, and the distortion model is subjected to curve fitting to calculate the distortion coefficients.

7. The method for geometric verification of remote sensing images based on satellite radiometric products according to claim 1, wherein, After obtaining the fixed distortion of each CCD probe in the CCD line scanning direction and flight direction, the image to be inspected is resampled using the cubic convolution method.

8. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the remote sensing image geometric calibration method based on satellite radiometric products according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the remote sensing image geometric verification method based on satellite radiometric products as described in any one of claims 1-7.

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