Quality inspection method for airborne insar lateral overlapping degree

Abstract

The invention relates to the technical field of remote sensing, and discloses an airborne insar lateral overlap degree quality inspection method, which comprises the following steps: S1, performing range pulse pressure processing on SAR data, including performing range Fourier transform on the SAR data, applying a range pulse pressure function, and performing range inverse Fourier transform, so that echo energy is concentrated on a range dimension; according to the method, under the background of rapid development of aerial photography and diversified and multi-quantization of achievements, the problems that insar data is difficult to read, and the overlapping degree is complex to calculate can be effectively solved, it is ensured that the calculation of the lateral overlapping degree is more accurate, and through fine SAR data processing including technical means of distance pulse pressure, motion compensation, bending correction, azimuth pulse pressure and the like, the calculation accuracy of the lateral overlapping degree is improved. According to the method, the quality inspection of the lateral overlapping degree can be automatically executed, the working efficiency is remarkably improved, and errors caused by manual intervention are reduced.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and particularly to a method for quality inspection of the lateral overlap degree of airborne InSAR. Background Art

[0002] With the rapid development of aerial photography and remote sensing technology, especially in the wide application of airborne synthetic aperture radar technology, how to efficiently and accurately perform data quality inspection, especially the calculation of the lateral overlap degree, has become an urgent problem to be solved. The airborne InSAR technology is widely used in terrain mapping, resource survey, disaster monitoring and other fields. However, with the rapid growth of data volume, the control and evaluation of data quality also face increasingly complex challenges. When conducting flight quality inspection, it is necessary to inspect the lateral overlap degree of airborne synthetic aperture radar images. The lateral overlap degree is an important factor affecting image quality, which is directly related to the coverage range of the image, the accuracy of image stitching, and the reliability of subsequent data processing. Therefore, accurately calculating and evaluating the lateral overlap degree has become an indispensable part of flight quality inspection.

[0003] In practice, the existing InSAR data reading technology and overlap degree calculation method have certain limitations. Especially when dealing with large-scale and various types of SAR data, the calculation process is complex and prone to errors, resulting in inaccurate evaluation results of the lateral overlap degree and unable to meet the requirements of rapid and efficient quality inspection. The existing methods usually rely on manual intervention for overlap degree inspection, which is not only inefficient but also may cause errors in the results due to human factors, and it is impossible to achieve real-time and comprehensive quality assessment in practical applications.

[0004] Therefore, the present invention proposes a new method for quality inspection of the lateral overlap degree of airborne InSAR. By using automated technology to process SAR data, it can accurately and quickly calculate the lateral overlap degree and perform quality assessment according to relevant standards. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method for quality inspection of the lateral overlap degree of airborne InSAR, which solves the problems of complex existing InSAR data processing methods, relying on manual intervention, low efficiency and easy to make mistakes, and difficult to perform real-time quality assessment.

[0006] To achieve the above object, the present invention is realized by the following technical solutions: A method for quality inspection of the lateral overlap degree of airborne InSAR includes the following steps:

[0007] S1: Perform range pulse compression processing on the SAR data, including performing range Fourier transform on the SAR data, applying a range pulse compression function, and performing inverse range Fourier transform, so that the echo energy is concentrated in the range dimension;

[0008] S2: Perform bending correction processing, including calculating the motion compensation amount based on the inertial navigation data, multiplying by the bending correction function in the two-dimensional frequency domain of range and azimuth, thereby removing the coupling between range and azimuth, and obtaining a more accurate target image;

[0009] S3: Perform azimuth pulse compression processing, including multiplying by the azimuth pulse compression function in the azimuth frequency domain to concentrate the energy of the target along the azimuth dimension and obtain a high-resolution SAR image;

[0010] S4: According to the effective range of the SAR image, convert the raster data in tif format into vector surface data in shapefile format, and fill in the corresponding attribute information in the vector surface, including fields such as the shooting area code, flight line number, and flight film number, to ensure the uniqueness of the data;

[0011] S5: Based on the vector surface data, perform spatial topology analysis, calculate the overlapping area between different flight strips, obtain the value of the side overlap degree, and evaluate the side overlap degree according to the limit value required by the technology.

[0012] Preferably, the range pulse compression processing in S1 makes the echo energy concentrate on the range dimension and obtains a clear image by performing Fourier transform on the SAR data, multiplying by the pulse compression function, and performing inverse Fourier transform.

[0013] Preferably, the bending correction processing in S2 removes the coupling between range and azimuth by applying the bending correction function in the two-dimensional frequency domain to ensure the linearity of the image.

[0014] Preferably, the azimuth pulse compression processing in S3 concentrates the energy of the target by applying the azimuth pulse compression function in the azimuth frequency domain to improve the resolution of the image.

[0015] Preferably, the vector surface data in S4 includes fields such as the shooting area code, flight line number, and flight film number, ensures the uniqueness of the vector surface data, and obtains accurate geometric data through data format conversion.

[0016] Preferably, in S5, the formula for calculating the overlap ratio is:

[0017]

[0018] where S1 is the area of the current flight film, S2 is the union area of the overlapping area between the current flight film and the adjacent flight film, and the calculation formula for the overlapping area S2 is:

[0019]

[0020] where A is the current flight film, and C1, C2, C3, C4, C5 are other flight films intersecting with flight film A.

[0021] Preferably, the overlapping ratio is in percentage, with two decimal places taken after the integer and without rounding, and is expressed as: if the calculated overlapping ratio is less than the limit value G required by the technology, it is determined that the lateral overlap of the aerial photograph does not meet the requirements; otherwise, it meets the requirements.

[0022] Preferably, the spatial topology analysis includes precisely matching and calculating the geometric shape, spatial position of the aerial photograph, and the overlapping area of adjacent flight strips, can automatically identify and process data in different regions according to different flight strip numbers and shooting area codes, uses special software for calculation and automatic evaluation, avoids manual intervention, improves work efficiency and accuracy, and finally the calculated lateral overlap value is used for flight quality assessment, data quality detection, and aerial photograph coverage evaluation.

[0023] Beneficial effects

[0024] The present invention provides a quality inspection method for the lateral overlap of airborne InSAR. Compared with the prior art, it has the following beneficial effects:

[0025] 1. In the present invention, under the background of the rapid development of aerial photography and the diversification and quantification of achievements, it can effectively solve problems such as difficult reading of InSAR data and complex calculation of the overlapping ratio, ensure more accurate calculation of the lateral overlap, and improve the reliability and accuracy of quality inspection through fine SAR data processing, including technical means such as range pulse compression, curvature correction, and azimuth pulse compression. This method can automatically perform the quality inspection of the lateral overlap, significantly improve work efficiency and reduce errors caused by manual intervention. Through automatic processing, the present invention can efficiently process large-scale InSAR data, adapt to the increasing demand of aerial photography tasks, and can process different types of airborne InSAR data, such as X-band, C-band, and L-band SAR image data, with wide adaptability and flexible processing parameter adjustment capabilities.

[0026] 2. In the present invention, the quality inspection results are intuitively presented to users through visualization technology, helping users quickly understand and evaluate the lateral overlap information of each aerial photograph, providing a reliable basis for subsequent remote sensing data processing and application. Especially when facing complex aerial photography data, the present invention can effectively evaluate the quality of the overlapping ratio, ensure the integrity and accuracy of aerial photograph coverage. In terms of working principle, the present invention reduces the dependence on manual intervention through automatic tools, not only reduces errors in manual operations, but also improves the work efficiency and safety of pilots and operators. Finally, this method provides strong support for the flight quality inspection of airborne InSAR and provides an important reference basis for the inspection and application of aerial photography achievements, with significant technical and application values. Brief description of the drawings

[0027] Figure 1 Schematic flow chart of a method for quality inspection of the lateral overlap degree of airborne InSAR according to the present invention;

[0028] Figure 2 SAR imaging flow chart of a method for quality inspection of the lateral overlap degree of airborne InSAR according to the present invention;

[0029] Figure 3 Shape diagram of the image vector plane of a method for quality inspection of the lateral overlap degree of airborne InSAR according to the present invention;

[0030] Figure 4 Attribute diagram of the image vector plane of a method for quality inspection of the lateral overlap degree of airborne InSAR according to the present invention;

[0031] Figure 5 SAR image diagram of a method for quality inspection of the lateral overlap degree of airborne InSAR according to the present invention;

[0032] Figure 6 Overlap degree analysis diagram of a method for quality inspection of the lateral overlap degree of airborne InSAR according to the present invention. Specific embodiments

[0033] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0034] Please refer to Figures 1-6 , the present invention provides a technical solution, which specifically includes the following embodiments:

[0035] Embodiment:

[0036] A method for quality inspection of the lateral overlap degree of airborne InSAR includes the following steps:

[0037] S1: Perform range pulse compression processing on the SAR data, including performing range Fourier transform on the SAR data, applying a range pulse compression function, and performing inverse range Fourier transform, so that the echo energy is concentrated in the range dimension;

[0038] S2: Perform bend correction processing, including calculating the motion compensation amount according to the inertial navigation data, multiplying by a bend correction function in the two-dimensional frequency domain of range and azimuth, so as to remove the coupling between range and azimuth and obtain a more accurate target image;

[0039] S3: Perform azimuth pulse compression processing, including multiplying by an azimuth pulse compression function in the azimuth frequency domain to concentrate the energy of the target along the azimuth dimension and obtain a high-resolution SAR image;

[0040] S4: According to the effective range of the SAR image, convert the raster data in tif format into vector surface data in shapefile format, and fill in the corresponding attribute information in the vector surface, including fields such as the shooting area code, flight line number, and flight film number, to ensure the uniqueness of the data;

[0041] S5: Based on the vector surface data, perform spatial topology analysis, calculate the overlapping area between different flight strips, obtain the cross-track overlap degree value, and evaluate the cross-track overlap degree according to the limit value required by the technology.

[0042] Preferably, the range pulse compression processing in S1 is performed by performing a Fourier transform on the SAR data, multiplying by a pulse compression function, and then performing an inverse Fourier transform, so that the echo energy is concentrated in the range dimension to obtain a clear image.

[0043] In the above, during the processing of the SAR image, range pulse compression is a crucial step. Its main purpose is to compress the echo signal to enhance the image resolution and improve the signal quality. In step S1, the SAR data is first transformed into frequency domain data through Fourier transform, thus entering the frequency domain processing stage. In this stage, by applying a range pulse compression function, usually a matched filter, the high-frequency information and low-frequency information in the echo signal are effectively separated, so that the energy of the signal is concentrated in the range dimension. After that, an inverse Fourier transform is performed to convert the frequency domain data back into time domain data, and finally a clear SAR image after range pulse compression processing is obtained. Through this processing method, the energy of the echo signal is concentrated within a specific range of distances, avoiding the blurring effect caused during signal propagation, thereby making the target of the image clearer and the details more distinct. This process not only improves the spatial resolution of the image but also provides an accurate basis for subsequent image analysis, target recognition, and data fusion. Moreover, it has the following beneficial effects:

[0044] Improve image resolution: The range pulse compression processing effectively focuses the energy of the echo signal, resulting in a significant improvement in the resolution of the image in the range dimension. Such a high-resolution image can accurately reflect the details of the ground objects and provide higher-quality data for subsequent analysis and applications.

[0045] Eliminate the blurring effect: Through the application of Fourier transform and matched filter, the energy of the echo signal is concentrated in the correct range dimension, eliminating the blurring effect caused by signal expansion or propagation and ensuring the clear visibility of the target.

[0046] Enhance target detection and recognition capabilities: After the signal can be precisely concentrated, the target features in the image become more prominent, thereby improving the accuracy of target detection and recognition. Especially in complex terrains and environments, different objects or structures can be effectively identified.

[0047] Support efficient data processing: Due to the automated nature of this processing step, it can reduce manual intervention, improve work efficiency, and enable the rapid processing and analysis of a large amount of SAR image data. Especially in the processing of multi-band and multi-data sources, it shows strong adaptability and efficiency.

[0048] Provide basic technical support: Range pulse compression processing provides a high-quality data basis for subsequent image correction, overlap calculation, and spatial analysis, ensuring the smooth progress of other steps such as bend correction and azimuth pulse compression, and guaranteeing the accuracy and consistency of the entire quality inspection process.

[0049] Preferably, the bend correction processing in S2 removes the coupling between range and azimuth by applying a bend correction function in the two-dimensional frequency domain to ensure the linearity of the image.

[0050] In the above, in SAR image processing, bend correction is to eliminate the geometric distortion caused by the imaging characteristics of the SAR system. In step S2, there are certain geometric distortions in the range and azimuth dimensions of the image. Especially when the movement trajectory of the radar system is not completely perpendicular to the target ground, the imaging result often shows a bend effect, resulting in the target in the image presenting a bent or non-linear feature. To eliminate this distortion, bend correction is processed by applying a bend correction function in the two-dimensional frequency domain. Specifically, in the frequency domain, the signals in the range and azimuth dimensions of the image are separated. By applying the bend correction function, the coupling effect between range and azimuth can be effectively removed, thereby restoring the image to a more linear structure that conforms to the real ground objects. This process precisely calculates and adjusts the position information of each pixel point through a mathematical model in the frequency domain, eliminates the geometric errors caused by the changes in the imaging platform and radar viewing angle, and finally ensures that the target in the image presents a linear form with high-precision geometric properties.

[0051] Preferably, the azimuth pulse compression processing in S3 concentrates the energy of the target by applying an azimuth pulse compression function in the azimuth frequency domain to improve the resolution of the image.

[0052] In the above, in SAR image processing, azimuth pulse compression processing is a key step for improving image resolution and enhancing target energy. In step S3, the azimuth pulse compression processing is achieved by applying a pulse compression function in the azimuth frequency domain. Since the SAR imaging system usually has a long pulse width, the echo signal of the target is extended in the azimuth dimension, thus affecting the image resolution. The application of the azimuth pulse compression function aims to compress the echo signal, so that the energy of the target is concentrated in a smaller area, thereby reducing the extension in the azimuth direction and finally improving the image resolution. Specifically, in the azimuth frequency domain, the azimuth pulse compression function processes the target signal, eliminates the ambiguity effect in the azimuth dimension, focuses the signal energy of the target on its actual position, and makes the boundary of each target in the image clearer and the details more prominent. In this way, not only the resolution ability of the target is improved, but also the overall quality of the SAR image is effectively enhanced, providing a clearer and more accurate data basis for subsequent analysis and processing.

[0053] Preferably, the vector surface data in S4 includes fields such as the shooting area code, flight line number, and aerial photo number, ensuring the uniqueness of the vector surface data, and obtaining accurate geometric data through data format conversion.

[0054] In the above, in the process of SAR image processing, the vector surface data is a key intermediate data form, which is used to convert the processed raster data into more structured spatial data. In step S4, by converting the SAR image into vector surface data in the shapefile format, we can more conveniently perform spatial analysis, overlap degree calculation, and quality assessment. During the conversion process, the vector surface data not only contains the geometric information of the pixels, but also includes multiple keyword fields related to the aerial photos, such as the shooting area code, flight line number, aerial photo number, etc. The introduction of these fields ensures the uniqueness of each vector surface data and provides a systematic identification for subsequent data management. These attribute fields are saved together with the spatial information of the image during the data conversion process, enabling each piece of data to be associated with its original image and related aerial photo information, thus avoiding data chaos and loss.

[0055] Preferably, in S5, the formula for calculating the overlap ratio is:

[0056]

[0057] Among them, S1 is the area of the current aerial photo, S2 is the union area of the overlapping area between the current aerial photo and the adjacent aerial photo, and the formula for calculating the overlapping area S2 is:

[0058]

[0059] Among them, A is the current aerial photo, and C1, C2, C3, C4, C5 are other aerial photos intersecting with aerial photo A.

[0060] In the above, by accurately calculating the overlapping area of aerial photos, the overlapping degree between different flight strips can be accurately evaluated. This calculation method eliminates the possible estimation errors in traditional methods, thus ensuring that the calculation result of the overlapping degree is more accurate. This method is not only applicable to the calculation of the overlapping area of multiple flight strips, but also can process various types of aerial photo data. Whether it is horizontal overlap or side overlap, it can be effectively evaluated to meet the requirements of complex remote sensing tasks. By accurately assessing the side overlap degree of each aerial photo, the quality of remote sensing data can be comprehensively evaluated. This process not only helps to verify the data integrity and accuracy, but also supports subsequent multi-source data fusion, mosaicking and application, improving the reliability and usability of the overall data.

[0061] Preferably, the overlapping ratio is in percentage, with two decimal places after the integer and no rounding. It is expressed as: if the calculated overlapping ratio is less than the limit value G of the technical requirement, it is determined that the side overlap degree of this aerial photo does not meet the requirement; otherwise, it meets the requirement.

[0062] In the above, in the calculation of the overlapping degree, to ensure the accuracy and consistency of the calculation result, the overlapping ratio is expressed in percentage. The calculation result of the overlapping ratio is usually a decimal value, indicating the overlapping degree between the current aerial photo and the adjacent aerial photo. To ensure the accuracy, the calculation result is usually retained to two decimal places without rounding. This is to avoid the cumulative error caused by rounding in multiple calculations and ensure the reliability of the result. Specifically, when the calculated overlapping ratio is less than the predetermined technical requirement limit value G, it means that the side overlap degree of this aerial photo does not meet the requirement and its quality is unqualified; while when the overlapping ratio is greater than or equal to the limit value G, it indicates that the side overlap degree of this aerial photo meets the standard and its quality is qualified.

[0063] Preferably, the spatial topology analysis includes accurately matching and calculating the geometric shape, spatial position of the aerial photo and the overlapping area of adjacent flight strips. It can automatically identify and process the data of different regions according to different flight strip numbers and shooting area codes, and uses special software for calculation and automatic evaluation to avoid manual intervention, improving work efficiency and accuracy. The finally calculated side overlap degree value is used for flight quality assessment, data quality detection, and aerial photo coverage evaluation.

[0064] In the above, spatial topology analysis is a key step to ensure the accuracy of overlap degree calculation and data integrity. By precisely matching and calculating the geometric shape, spatial position of aerial photos, and the overlapping areas of adjacent flight strips, we can accurately evaluate the overlap degree of each aerial photo with the surrounding ones. Spatial topology analysis relies on in-depth analysis of the geometric morphology, position of each aerial photo, and the spatial relationship of adjacent flight strips. Based on the flight strip number and shooting area code, the system can automatically identify and process data from different regions. By matching these data, the accuracy of the calculation results is ensured, and the errors that may be brought by manual intervention are avoided. Using dedicated software for automatic calculation and evaluation of data greatly improves work efficiency and ensures the consistency and accuracy of the calculation process. Finally, the calculated cross-overlap degree value is used as the basis for flight quality assessment, data quality detection, and aerial photo coverage evaluation, providing effective support for ensuring the quality of the entire aerial photography process.

[0065] During operation, SAR data is acquired and range pulse compression processing is performed. This processing step makes the energy of the echo signal concentrated in the range dimension by performing range Fourier transform on the SAR data, multiplying by the range pulse compression function, and then performing inverse range Fourier transform, thereby enhancing image clarity and providing reliable data for subsequent precise analysis. Then, bend correction processing is executed. Among them, bend correction processing includes motion compensation: First, information such as the position, speed, and attitude of the aircraft needs to be extracted from the inertial navigation data. Then, the motion parameters such as the position, speed, and attitude of each antenna are calculated using the information such as the position, speed, and attitude of the aircraft and the installation structure relationship between the inertial navigation and the antenna. Finally, the motion compensation amount is calculated using the obtained motion parameters such as the position, speed, and attitude of the antenna, and motion compensation is performed in combination with the echo data of each antenna. Imaging is performed separately for each antenna channel after motion compensation. For motion compensation, please refer to the appendix Figure 2, the purpose of this step is to eliminate the range-azimuth coupling caused by the SAR data itself. During this process, a curvature correction function is applied and multiplication processing is performed in the two-dimensional frequency domain to ensure that the target image is a straight line along the azimuth dimension, thereby optimizing the geometric accuracy of the image. Then, azimuth pulse compression processing is carried out. This process focuses the energy of the target along the azimuth dimension by applying an azimuth pulse compression function in the azimuth frequency domain, so that clearer target details can be obtained in the image, improving the resolution of the image and providing higher-quality image data for subsequent data analysis. The processed data is converted into a SAR image, and through further processing, the raster data in tif format is converted into vector surface data. In step S4, according to the effective range of the SAR image, the data is converted into the shapefile format, and key attribute information such as the shooting area code, flight line number, and aerial photo number is added to each vector surface data, thereby ensuring the uniqueness and correct identification of each data. This step is an important part of data management in the whole method and helps with subsequent data integration and management. Next, spatial topology analysis is carried out to analyze the overlap degree between adjacent aerial photos to ensure that the calculation of the overlap area between different flight strips is accurate. In step S5, using the existing vector surface data, the overlap area between different flight strips is calculated through spatial analysis technology to obtain the value of the side overlap degree. By comparing it with the overlap degree limit required by the technology, the system can determine whether the overlap degree of this aerial photo meets the requirements, thereby providing a basis for further quality assessment. Finally, the calculated side overlap degree and the corresponding quality assessment results will be presented in a visual way to ensure that users can intuitively view the quality information of each aerial photo. Through the whole automated process, manual intervention is reduced, the calculation efficiency is improved, and the accuracy of data inspection is greatly improved. This method not only meets the requirements of complex and variable aerial photography tasks, but also provides comprehensive technical support for the flight quality inspection of airborne InSAR, ensuring the integrity and accuracy of aerial photos and providing strong technical guarantee for the evaluation of relevant aerial photography results.

[0066] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the application shall be included in the protection scope of the present application.

Claims

1. A quality inspection method for the lateral overlap degree of airborne InSAR, characterized in that, Including the following steps: S1: Perform range pulse compression processing on the SAR data, including performing range Fourier transform on the SAR data, applying a range pulse compression function, and performing inverse range Fourier transform, so that the echo energy is concentrated in the range dimension; S2: Perform bend correction processing, including calculating the motion compensation amount according to the inertial navigation data, multiplying by a bend correction function in the two-dimensional frequency domain of range and azimuth, so as to remove the coupling between range and azimuth and obtain a more accurate target image; S3: Perform azimuth pulse compression processing, including multiplying by an azimuth pulse compression function in the azimuth frequency domain to concentrate the energy of the target along the azimuth dimension and obtain a high-resolution SAR image; S4: According to the effective range of the SAR image, convert the raster data in tif format into vector surface data in shapefile format, and fill in the corresponding attribute information in the vector surface, including fields such as the shooting area code, flight line number, and aerial photo number, to ensure the uniqueness of the data; S5: Based on the vector surface data, perform spatial topology analysis, calculate the overlapping area between different flight strips, obtain the cross-overlap degree value, and evaluate the cross-overlap degree according to the limit value required by the technology.

2. The quality inspection method for the cross-track overlap degree of an airborne InSAR according to claim 1, characterized in that The range pulse compression processing in S1 makes the echo energy concentrated in the range dimension and obtains a clear image by performing Fourier transform on the SAR data, multiplying by a pulse compression function, and performing inverse Fourier transform.

3. A quality inspection method for the cross-track overlap degree of an airborne InSAR according to claim 1, characterized in that The bend correction processing in S2 removes the coupling between range and azimuth and ensures the linearity of the image by applying a bend correction function in the two-dimensional frequency domain.

4. A quality inspection method for the cross-track overlap degree of an airborne InSAR according to claim 1, characterized in that, The azimuth pulse compression processing in S3 concentrates the energy of the target and improves the resolution of the image by applying an azimuth pulse compression function in the azimuth frequency domain.

5. The quality inspection method for the cross-track overlap degree of an airborne InSAR according to claim 1, characterized in that, The vector surface data in S4 includes fields such as the shooting area code, flight line number, and aerial photo number, ensures the uniqueness of the vector surface data, and obtains accurate geometric data through data format conversion.

6. The quality inspection method for the cross-track overlap degree of an airborne InSAR according to claim 1, characterized in that In S5, the formula for calculating the overlap ratio is: where S1 is the area of the current aerial photo, S2 is the union area of the overlapping area between the current aerial photo and the adjacent aerial photo, and the formula for calculating the overlapping area S2 is: where A is the current aerial photo, and C1, C2, C3, C4, C5 are other aerial photos intersecting with aerial photo A.

7. A quality inspection method for the lateral overlap degree of an airborne InSAR according to claim 1, characterized in that The overlap ratio is in percentage, with two decimal places after the integer and no rounding. It is expressed as: if the calculated overlap ratio is less than the limit value G required by the technology, it is determined that the cross-overlap degree of this aerial photo does not meet the requirements; otherwise, it meets the requirements.

8. A quality inspection method for the side - overlap degree of an airborne InSAR according to claim 1, characterized in that The spatial topology analysis includes accurately matching and calculating the geometric shape, spatial position of the aerial photos, and the overlapping area between adjacent flight strips, can automatically identify and process data in different regions according to different flight strip numbers and shooting area codes, uses special software for calculation and automatic evaluation, avoids manual intervention, improves work efficiency and accuracy, and the finally calculated cross-overlap degree value is used for flight quality assessment, data quality detection, and aerial photo coverage evaluation.

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