An error compensation method for realizing high-precision deformation inversion

Abstract

The present invention relates to the field of error compensation technology, and more particularly to an error compensation method for achieving high-precision deformation inversion. The present invention determines point gradient differences, screens satellite images and their unique textures, analyzes the terrain texture representation characteristics of the unique textures, determines deformation interference representation parameters corresponding to the target area, and determines the deformation tendency of the satellite image corresponding to the target area. Based on the deformation tendency of the target area, the collected satellite image corresponding to the target area is processed, including adjusting the number of compensations for the satellite image based on the deformation interference representation parameters, extracting adjacent satellite images obtained by compensation for superposition and comparison, determining the texture feature compensation change ratio based on the superposition and comparison results, and determining whether the compensation is abnormal, or maintaining the number of compensations for the satellite image. The present invention improves the efficiency and accuracy of error compensation by accurately determining the unique texture, specifically adjusting the number of compensations for the satellite image, and verifying it.

Description

Technical Field

[0001] The present invention relates to the technical field of error compensation, and particularly to an error compensation method for achieving high-precision deformation inversion. Background Art

[0002] In recent years, significant progress has been made in the error compensation technology for high-precision deformation inversion in aspects such as error source identification, multi-source data fusion, improvement of time-series InSAR technology, and expansion of practical applications. Researchers have effectively reduced the impact of major error sources on deformation inversion through methods such as atmospheric delay correction, orbit error compensation, and topographic phase removal. At the same time, the fusion technology combining InSAR with multi-source data such as GNSS and GRACE has further improved the accuracy and reliability of deformation monitoring. The improvement of time-series InSAR technology enables deformation monitoring to more comprehensively reflect the dynamic changes of surface deformation and has been widely applied in fields such as surface movement, urban subsidence, complex geology, and infrastructure monitoring. In the future, with the development of intelligent processing technology and the application of higher-resolution data, deformation inversion technology is expected to achieve more efficient and accurate monitoring in more fields.

[0003] For example, Chinese Patent Publication No.: CN115963493A discloses a multi-dimensional deformation and differential tropospheric delay inversion method for distributed spaceborne D-InSAR. This method adaptively realizes the joint inversion of high precision and high preservation resolution according to the spatial characteristics of multi-dimensional deformation and differential atmospheric delay, thereby improving the fineness of multi-dimensional deformation inversion. It includes: performing D-InSAR phase unwrapping processing on the repeat-pass SAR interferometric data obtained by distributed spaceborne SAR satellites; estimating the ionospheric phase error in each satellite differential interferogram using the optimal sub-spectrum method, and compensating the ionospheric phase error for each satellite differential interferogram after phase unwrapping; constructing a multi-channel adaptive minimum mean square error inversion matrix in the wavenumber domain according to the wavenumber domain power spectrum of each parameter to be estimated, the wavenumber domain power spectrum of the phase error component, and the observation equation of the distributed spaceborne SAR system; and jointly inverting the multi-dimensional deformation and differential tropospheric delay using the established minimum mean square error inversion matrix based on the processed differential interferogram.

[0004] For example, Chinese Patent Publication No.: CN116699610A discloses a Beidou InSAR atmospheric error compensation method based on regional division. This method divides the propagation path from the receiver to the PS point target into a propagation area and an estimation area. For the atmospheric error phase in the propagation area, it is regarded as a constant; while for the estimation area, the imaging, phase results of different satellites and the PS point selection results are combined to estimate its atmospheric refractive index. Each PS point in the scene is traversed, and the atmospheric refractive index of each point is estimated separately. The median absolute deviation method is used to judge the connectivity of the atmospheric refractive index of the scene, and for the points that do not meet the connectivity, that is, the outlier points, the atmospheric refractive index is corrected. Finally, the phases in the propagation area and the estimation area are added together to obtain the atmospheric phase of each target, realizing atmospheric phase compensation, and further improving the accuracy of deformation inversion and the effective early warning of disasters.

[0005] However, the following problems still exist in the prior art.

[0006] In the prior art, due to the differences in terrain and landform in different regions, the errors generated by satellite images are different. Usually, the satellite images to be processed are massive, and using the same standard for error compensation of satellite images has low efficiency and consumes computing power. Summary of the Invention

[0007] Therefore, the present invention provides an error compensation method for realizing high-precision deformation inversion to solve the problem that in the prior art, due to the differences in terrain and landform in different regions, the errors generated by satellite images are different. Usually, the satellite images to be processed are massive, and using the same standard for error compensation of satellite images has low efficiency and consumes computing power.

[0008] To achieve the above object, the present invention provides an error compensation method for realizing high-precision deformation inversion, which includes:

[0009] Obtain the elevation geographic information of several target points in each target area to be monitored to determine the point position gradient difference;

[0010] In response to the monitoring satellite obtaining the satellite image of the target area, randomly select a predetermined number of satellite images, mark the texture features in the satellite images to screen out the specific textures, and analyze the terrain texture characterization features of the specific textures;

[0011] Based on the point position gradient difference and the terrain texture characterization features, determine the deformation interference characterization parameters corresponding to the target area to determine the deformation tendency of the satellite image corresponding to the target area;

[0012] Based on the deformation tendency of the satellite image corresponding to the target area, process the collected satellite image corresponding to the target area, including,

[0013] Adjust the compensation times for satellite images based on the deformation interference characterization parameters, extract the satellite images obtained from the adjacent secondary compensation for superposition and comparison, determine the compensation change ratio of texture features based on the superposition and comparison results, and determine whether the compensation is abnormal;

[0014] Or, maintain the compensation times for satellite images;

[0015] Among them, the terrain texture characterization features include the number of specific textures and the chromaticity difference on the texture side. The superposition and comparison include placing the satellite images in the same coordinate system and comparing the coincidence degrees of each texture feature.

[0016] Further, the process of determining the point position gradient difference includes,

[0017] Determine the elevation of each of the target point positions;

[0018] Calculate the elevation difference corresponding to each of the target point positions and the adjacent target point positions;

[0019] Solve the average value of each of the elevation differences to obtain the point position gradient difference.

[0020] Further, the process of screening specific textures includes,

[0021] Mark several texture features in the satellite image;

[0022] Determine the length of each texture feature;

[0023] Determine the chromaticity difference between each of the texture features and the adjacent non-texture feature regions;

[0024] If the texture feature meets the screening conditions, then screen the texture feature as a specific texture;

[0025] The screening conditions are that the chromaticity difference is greater than the preset chromaticity difference threshold and the length is greater than the preset length.

[0026] Further, the process of analyzing the terrain texture characterization features of specific textures includes,

[0027] Determine the average value of the chromaticity differences between the adjacent region features on both sides of each of the texture features as the chromaticity difference on the texture side;

[0028] Statistically calculate the average value of the number of specific textures screened in each satellite image within the target region as the number of specific textures.

[0029] Further, the process of determining the deformation interference characterization parameters corresponding to the target region includes,

[0030] Determine the ratio of the point position gradient difference to the reference point position gradient difference as the gradient difference influence factor;

[0031] Determine the ratio of the number of specific textures to the number of reference specific textures as the texture influence factor;

[0032] Determine the ratio of the chromaticity difference on the texture side to the chromaticity difference on the reference texture side as the chromaticity difference influence factor;

[0033] Determine the weighted sum value of the gradient difference influence factor, the texture influence factor, and the chromaticity difference influence factor as the deformation interference characterization parameter.

[0034] Further, determine the deformation tendency of the satellite image corresponding to the target area, where

[0035] If the deformation interference characterization parameter is greater than the deformation interference characterization parameter threshold, determine that the deformation tendency is a strong deformation tendency;

[0036] If the deformation interference characterization parameter is less than or equal to the deformation interference characterization parameter threshold, determine that the deformation tendency is a weak deformation tendency.

[0037] Further, based on the deformation tendency of the satellite image corresponding to the target area, process the collected satellite image corresponding to the target area, where

[0038] If the satellite image corresponding to the target area has a strong deformation tendency, adjust the compensation times for the satellite image based on the deformation interference characterization parameter, extract the satellite images obtained by adjacent sub-compensations for superposition comparison, determine the texture feature compensation change ratio based on the superposition comparison result, and determine whether the compensation is abnormal;

[0039] If the satellite image corresponding to the target area has a weak deformation tendency, maintain the compensation times for the satellite image.

[0040] Further, the deformation interference characterization parameter is positively correlated with the error compensation times for the satellite image.

[0041] Further, the process of determining the texture feature compensation change ratio based on the superposition comparison result includes

[0042] Place the satellite images obtained by adjacent sub-compensations in the same coordinate system and compare the coincidence degrees of each texture feature;

[0043] Determine the changing texture features based on the coincidence degree;

[0044] Determine the ratio of the area of the changing texture features to the area of all texture features as the texture feature compensation change ratio;

[0045] Among them, if the coincidence degree of the texture features is less than the predetermined coincidence degree threshold, the texture features are changing texture features.

[0046] Further, determine whether the compensation is abnormal, where

[0047] If the compensation change ratio is greater than a preset compensation change ratio, it is determined that the compensation is abnormal, and the error compensation continues;

[0048] If the compensation change ratio is less than or equal to the preset compensation change ratio, it is determined that the compensation is normal.

[0049] Compared with the prior art, the present invention obtains the elevation geographic information of several target points in each target area to be monitored, determines the point position gradient difference, randomly selects a predetermined number of satellite images, marks the texture features in the satellite images to screen out specific textures, analyzes the topographic texture characterization features of the specific textures, determines the deformation interference characterization parameters corresponding to the target area, so as to determine the deformation tendency of the satellite images corresponding to the target area. Based on the deformation tendency of the target area, the satellite images corresponding to the target area collected are processed, including adjusting the compensation times for the satellite images based on the deformation interference characterization parameters, extracting and superposing and comparing the satellite images obtained by adjacent secondary compensations, determining the texture feature compensation change ratio based on the superposition and comparison results, determining whether the compensation is abnormal, or maintaining the compensation times for the satellite images. The present invention improves the efficiency and accuracy of error compensation by accurately determining specific textures, determining specific textures with different deformation tendencies and the texture feature compensation change ratio, and determining the abnormal state of the compensation.

[0050] In particular, marking the texture features of the satellite images and screening out specific textures, and analyzing the topographic texture characterization features of the specific textures provide a data basis for calculating the deformation interference characterization parameters corresponding to the target area. In the process of inverting the satellite images, if the model and calculation method are continuously used to determine the specific features of the images, not only will a huge amount of computing power be consumed, but also errors will occur in the error compensation of the images. Based on this, the present invention marks the texture features of the images and further determines specific textures through brief identification and analysis of the images, determines the topographic texture characterization features by analyzing the specific textures, consumes less computing power, enables subsequent calculation of the deformation interference characterization parameters, divides the deformation tendency of the images, determines the error compensation times, and improves the efficiency and accuracy of the error compensation of the satellite images.

[0051] In particular, the deformation tendency of the satellite image corresponding to the target area is determined by calculating the deformation interference characterization parameter corresponding to the target area. In actual situations, error compensation is required for satellite images. For some satellite images with large errors, multiple compensations may be needed. Inappropriate compensation may affect the accuracy of satellite images and reduce the accuracy rate. Based on this, before performing error compensation on satellite images, the present invention calculates the deformation interference characterization parameter of the satellite image in advance and determines the deformation tendency, so as to adaptively determine the number of error compensations based on satellite images with different deformation tendencies, improve the accuracy of satellite images, and improve the efficiency and accuracy of error compensation for satellite images.

[0052] In particular, the abnormal state of error compensation is determined by calculating the change ratio of texture feature compensation. In actual situations, some satellite images may be accurate after one compensation, or there may still be errors after multiple compensations. If the abnormal state of satellite images is not determined, compensation waste or insufficient compensation accuracy of satellite images may occur. Based on this, the present invention considers that after performing error compensation on satellite images, the satellite images obtained by adjacent secondary compensation are extracted and superimposed for comparison to calculate the change ratio of texture feature compensation, distinguish whether the error compensation is in an abnormal state, and then determine whether it is necessary to continue compensating the satellite images, improve the accuracy of satellite images, and improve the efficiency and accuracy of error compensation for satellite images. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the steps of the error compensation method for realizing high-precision deformation inversion in the embodiment of the invention;

[0054] Figure 2 It is a logic block diagram for determining the deformation tendency of the satellite image corresponding to the target area in the embodiment of the invention;

[0055] Figure 3 It is a logic block diagram for processing the satellite image corresponding to the target area collected based on the deformation tendency of the satellite image corresponding to the target area in the embodiment of the invention;

[0056] Figure 4 It is a logic block diagram for determining whether the compensation is abnormal in the embodiment of the invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0058] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0059] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the steps of the error compensation method for realizing high-precision deformation inversion in an embodiment of the invention. The error compensation method for realizing high-precision deformation inversion of the present invention includes:

[0060] Step S1, obtaining the elevation geographic information of several target points in each target area to be monitored to determine the point gradient difference;

[0061] Step S2, in response to the monitoring satellite obtaining satellite images of the target area, randomly screening a predetermined number of satellite images, marking the texture features in the satellite images to screen out specific textures, analyzing the topographic texture characterization features of the specific textures. In this embodiment, the predetermined number can be determined based on the proportion of all images required to be taken in the area, usually set between 5% and 10% of all images, which will not be elaborated here;

[0062] Step S3, based on the point gradient difference and the topographic texture characterization features, determining the deformation interference characterization parameters corresponding to the target area to determine the deformation tendency of the satellite images corresponding to the target area;

[0063] Step S4, based on the deformation tendency of the satellite images corresponding to the target area, processing the collected satellite images corresponding to the target area, including,

[0064] adjusting the compensation times for the satellite images based on the deformation interference characterization parameters, extracting the satellite images obtained by adjacent compensations for superposition and comparison, determining the texture feature compensation change ratio based on the superposition and comparison results, and determining whether the compensation is abnormal;

[0065] or, maintaining the compensation times for the satellite images;

[0066] wherein, the topographic texture characterization features include the number of specific textures and the chromaticity difference on the texture side, and the superposition and comparison include placing the satellite images in the same coordinate system and comparing the coincidence degrees of each texture feature.

[0067] Specifically, there is no limitation on the method for obtaining the elevation geographic information. For example, it can be obtained through existing geographic data or through on-site inspections to obtain the elevation geographic information. Those skilled in the art can select according to the actual situation, which will not be elaborated here.

[0068] Specifically, the process of determining the point gradient difference includes,

[0069] determining the elevation of each of the target points;

[0070] Calculate the elevation difference between each of the target points and the elevation of the adjacent target points;

[0071] Solve the average value of each of the elevation differences to obtain the point gradient difference;

[0072] It can be understood that there is at least one adjacent target point. For two adjacent target points, the elevation difference is the difference between the elevation of the target point and the average elevation of the two adjacent target points.

[0073] Specifically, the process of screening specific textures includes,

[0074] Mark a number of texture features in the satellite image;

[0075] Determine the length of each texture feature;

[0076] Determine the chromaticity difference between each of the texture features and the adjacent non-texture feature regions;

[0077] If the texture feature meets the screening conditions, then screen the texture feature as a specific texture;

[0078] The screening conditions are that the chromaticity difference is greater than a preset chromaticity difference threshold and the length is greater than a preset length.

[0079] Specifically, the preset length is calculated in advance. The purpose of setting the preset length is to screen out overly short texture features. Such texture features have poor data representativeness and avoid introducing such texture features to cause errors in subsequent judgments. The preset length is determined based on the diagonal length of the satellite image and is set to be between 0.05 and 0.1 times the diagonal length.

[0080] Specifically, there is no limitation on the method of identifying chromaticity. For the chromaticity of a region or a texture feature, multiple acquisition points can be used to collect the chromaticity, and the chromaticity mean value is used to represent the chromaticity of the corresponding region or texture feature. Of course, those skilled in the art can also make selections according to actual situations, as long as they can identify the chromaticity of the texture feature, which will not be elaborated here.

[0081] Specifically, the chromaticity of the non-texture feature region is the average value of the chromaticity within the region, which will not be elaborated here.

[0082] Specifically, the preset chromaticity difference threshold is calculated in advance. First, obtain the chromaticity values of a number of texture features and the chromaticity values of a number of non-texture feature regions, and determine 0.82 times the average value of the differences between the chromaticity values of the number of texture features and the chromaticity values of the number of non-texture feature regions as the chromaticity difference threshold.

[0083] Specifically, the process of analyzing the topographic texture characterization features of specific textures includes,

[0084] Determine the mean chromaticity difference between the adjacent region features on both sides of each of the texture features as the chromaticity difference on the texture side;

[0085] Statistically calculate the mean value of the number of specific textures selected in each satellite image within the target region as the number of specific textures.

[0086] Specifically, perform texture feature marking and specific texture screening on the satellite image, analyze the topographic texture characterization features of the specific texture, and provide a data basis for calculating the deformation interference characterization parameters corresponding to the target region. During the process of inverting the satellite image, if the model and calculation method are continuously used to determine the specific features of the image, not only will it consume huge computing power, but also errors in image error compensation will occur. Based on this, the present invention marks the texture features of the image and further determines the specific texture through brief identification and analysis of the image, and determines the topographic texture characterization features by analyzing the specific texture, consuming less computing power, enabling subsequent calculation of the deformation interference characterization parameters, dividing the deformation tendency of the image, determining the number of error compensation times, and improving the efficiency and accuracy of satellite image error compensation.

[0087] Specifically, the process of determining the deformation interference characterization parameters corresponding to the target region includes,

[0088] Determine the ratio of the point position gradient difference to the reference point position gradient difference as the gradient difference influence factor;

[0089] Determine the ratio of the number of specific textures to the reference number of specific textures as the texture influence factor;

[0090] Determine the ratio of the chromaticity difference on the texture side to the reference chromaticity difference on the texture side as the chromaticity difference influence factor;

[0091] Determine the weighted sum value of the gradient difference influence factor, the texture influence factor, and the chromaticity difference influence factor as the deformation interference characterization parameter.

[0092] Specifically, the reference point position gradient difference is pre-calculated. Obtain several original satellite images corresponding to the satellite images after compensation in advance, and determine the average value of the point position gradient differences in the several original satellite images as the reference point position gradient difference.

[0093] Specifically, the reference number of specific textures is pre-calculated. Obtain several original satellite images corresponding to the satellite images after compensation in advance, and determine the average value of the number of specific textures in the several original satellite images as the reference number of specific textures.

[0094] Specifically, the reference chromaticity difference on the texture side is pre-calculated. Obtain several original satellite images corresponding to the satellite images after compensation in advance, and determine the average value of the chromaticity differences on the texture side in the several original satellite images as the reference chromaticity difference on the texture side.

[0095] Specifically, the sum of the weight coefficients of the gradient difference influence factor, the texture influence factor, and the chromaticity difference influence factor is 1. The weight coefficient of the gradient difference influence factor is 0.3, the weight coefficient of the texture influence factor is 0.34, and the weight coefficient of the chromaticity difference influence factor is 0.36.

[0096] Please refer to Figure 2 , Figure 2 which is a logic block diagram for determining the deformation tendency of the satellite image corresponding to the target area in the invention embodiment. Specifically, determine the deformation tendency of the satellite image corresponding to the target area, where

[0097] if the deformation interference characterization parameter is greater than the deformation interference characterization parameter threshold, it is determined that the deformation tendency is a strong deformation tendency;

[0098] if the deformation interference characterization parameter is less than or equal to the deformation interference characterization parameter threshold, it is determined that the deformation tendency is a weak deformation tendency.

[0099] Specifically, the deformation interference characterization parameter threshold characterizes the complexity of the texture features of the satellite image and affects the number of times of error compensation. Therefore, the deformation interference characterization parameter threshold is set to be selected within the interval [0.75, 0.85].

[0100] Specifically, the deformation tendency of the satellite image corresponding to the target area is determined by calculating the deformation interference characterization parameter corresponding to the target area. In actual situations, compensation is required for satellite images. For some satellite images with large errors, multiple compensations may be required. Improper compensation may affect the accuracy of the satellite image and reduce the accuracy rate. Based on this, before compensating for the error of the satellite image, the present invention calculates the deformation interference characterization parameter of the satellite image in advance and determines the deformation tendency, so as to adaptively determine the number of error compensations based on satellite images with different deformation tendencies, improve the accuracy of the satellite image, and improve the efficiency and accuracy rate of error compensation for satellite images.

[0101] Please refer to Figure 3 , Figure 3 which is a logic block diagram for processing the satellite image corresponding to the target area collected based on the deformation tendency of the satellite image corresponding to the target area in the invention embodiment. Specifically, based on the deformation tendency of the satellite image corresponding to the target area, the satellite image corresponding to the target area collected is processed, where

[0102] if the satellite image corresponding to the target area has a strong deformation tendency, the number of compensations for the satellite image is adjusted based on the deformation interference characterization parameter, the satellite image obtained by adjacent secondary compensation is extracted for superposition and comparison, the texture feature compensation change ratio is determined based on the superposition and comparison result, and it is determined whether the compensation is abnormal;

[0103] If the satellite image corresponding to the target area has a weak deformation tendency, the compensation times for the satellite image are maintained.

[0104] Specifically, the deformation interference characterization parameter is positively correlated with the error compensation times for the satellite image.

[0105] In some possible implementations,

[0106] If the deformation interference characterization parameter is greater than or equal to the second deformation interference characterization parameter comparison threshold, the error compensation times are set to 4;

[0107] If the deformation interference characterization parameter is greater than the first deformation interference characterization parameter comparison threshold and less than the second deformation interference characterization parameter comparison threshold, the error compensation times are set to 3;

[0108] If the deformation interference characterization parameter is less than or equal to the first deformation interference characterization parameter comparison threshold, the error compensation times are set to 2;

[0109] The second deformation interference characterization parameter comparison threshold is 1.5 times the deformation interference characterization parameter threshold, and the first deformation interference characterization parameter comparison threshold is 1.25 times the deformation interference characterization parameter threshold.

[0110] Specifically, the process of determining the texture feature compensation change ratio based on the superposition comparison result includes,

[0111] Placing the satellite images obtained by adjacent sub-compensations in the same coordinate system and comparing the coincidence degrees of each texture feature;

[0112] Determining the changing texture features based on the coincidence degrees;

[0113] Determining the ratio of the area of the changing texture features to the area of all texture features as the texture feature compensation change ratio;

[0114] Among them, if the coincidence degree of the texture feature is less than the predetermined coincidence degree threshold, the texture feature is a changing texture feature.

[0115] Specifically, the method for determining the coincidence degree is not limited. For example, the coincidence degree of the texture feature can be determined by comparing pixel points;

[0116] Obtaining the coordinates of each pixel point of two corresponding texture features, and determining the set of coincidence coordinates and the set of non-coincidence coordinates;

[0117] Determining the number of pixel points in the set of coincidence coordinates, and solving the ratio of the number to the average value of the number of pixel points of the two texture features to obtain the coincidence degree.

[0118] Of course, those skilled in the art can also select other methods or a combination of multiple methods, as long as they can ensure the successful determination of the coincidence degree, which will not be elaborated here.

[0119] Specifically, the coincidence degree threshold is determined in advance. Among them, during the process of obtaining multiple compensations for several satellite images, the coincidence degree of the texture features in the satellite images obtained by adjacent compensations is obtained, the average value of the coincidence degree is solved, and 0.75 times of the average value of the coincidence degree is set as the coincidence degree threshold.

[0120] Please refer to Figure 4 , Figure 4 which is a logic block diagram for determining whether the compensation is abnormal in the embodiment of the invention. Specifically, it is determined whether the compensation is abnormal, where

[0121] if the compensation change ratio is greater than the preset compensation change ratio, it is determined that the compensation is abnormal, and error compensation continues;

[0122] if the compensation change ratio is less than or equal to the preset compensation change ratio, it is determined that the compensation is normal.

[0123] Specifically, the preset compensation change ratio represents that the error compensation accuracy of the satellite image has reached the highest at this time, and the satellite image does not need to continue error compensation to improve the accuracy of the satellite image. Therefore, the preset compensation change ratio is set to be selected within the interval [0.05, 0.12].

[0124] Specifically, the abnormal state of the error compensation is determined by calculating the compensation change ratio of the texture features. In actual situations, some satellite images may be able to ensure the accuracy after one compensation, and there may still be errors after multiple compensations. If the abnormal state of the satellite image is not determined, it may cause waste of compensation or insufficient accuracy of satellite image compensation. Based on this, the present invention considers that after error compensation of the satellite image, by extracting and superposing and comparing the satellite images obtained by adjacent compensations, the compensation change ratio of the texture features is calculated to distinguish whether the error compensation is in an abnormal state, and then determine whether it is necessary to continue compensating the satellite image, improving the accuracy of the satellite image, and improving the efficiency and accuracy of the error compensation of the satellite image.

[0125] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0126] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention; for those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An error compensation method for realizing high-precision deformation inversion, characterized in that Including: Obtain the elevation geographic information of several target points in each target area to be monitored, so as to determine the point gradient difference; In response to the monitoring satellite obtaining satellite images of the target area, randomly select a predetermined number of satellite images, mark the texture features in the satellite images to screen out specific textures, and analyze the terrain texture characterization features of the specific textures; Based on the point gradient difference and the terrain texture characterization features, determine the deformation interference characterization parameters corresponding to the target area, so as to determine the deformation tendency of the satellite images corresponding to the target area; Based on the deformation tendency of the satellite images corresponding to the target area, process the collected satellite images corresponding to the target area, including, Adjust the compensation times for the satellite images based on the deformation interference characterization parameters, extract the satellite images obtained by adjacent secondary compensation for superposition comparison, determine the texture feature compensation change ratio based on the superposition comparison result, and determine whether the compensation is abnormal; Or, maintain the compensation times for the satellite images; Wherein, the terrain texture characterization features include the number of specific textures and the chromaticity difference on the texture side, and the superposition comparison includes placing the satellite images in the same coordinate system and comparing the coincidence degrees of each texture feature.

2. The error compensation method for realizing high-precision deformation inversion according to claim 1, wherein, The process of determining the point gradient difference includes, Determine the elevation of each of the target points; Calculate the elevation difference corresponding to each target point and its adjacent target point; Solve the average value of each elevation difference to obtain the point gradient difference.

3. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that The process of screening specific textures includes, Mark several texture features in the satellite images; Determine the length of each texture feature; Determine the chromaticity difference between each texture feature and the adjacent non-texture feature area; If the texture feature meets the screening conditions, then screen the texture feature as a specific texture; The screening conditions are that the chromaticity difference is greater than the preset chromaticity difference threshold and the length is greater than the preset length.

4. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that The process of analyzing the terrain texture characterization features of specific textures includes, Determine the average chromaticity difference between the adjacent area features on both sides of each texture feature as the chromaticity difference on the texture side; Count the average number of specific textures screened out in each satellite image in the target area as the number of specific textures.

5. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that The process of determining the deformation interference characterization parameters corresponding to the target area includes, Determine the ratio of the point gradient difference to the reference point gradient difference as the gradient difference influence factor; Determine the ratio of the number of specific textures to the reference number of specific textures as the texture influence factor; Determine the ratio of the chromaticity difference on the texture side to the reference chromaticity difference on the texture side as the chromaticity difference influence factor; Determine the weighted sum value of the gradient difference influence factor, the texture influence factor and the chromaticity difference influence factor as the deformation interference characterization parameter.

6. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that Determine the deformation tendency of the satellite images corresponding to the target area, wherein, If the deformation interference characterization parameter is greater than the deformation interference characterization parameter threshold, then determine the deformation tendency as a strong deformation tendency; If the deformation interference characterization parameter is less than or equal to the deformation interference characterization parameter threshold, then determine the deformation tendency as a weak deformation tendency.

7. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that Based on the deformation tendency of the satellite images corresponding to the target area, process the collected satellite images corresponding to the target area, wherein, If the satellite image corresponding to the target area has a strong deformation tendency, the compensation times for the satellite image are adjusted based on the deformation interference characterization parameter, the satellite images obtained by adjacent secondary compensation are extracted for superposition comparison, the texture feature compensation change ratio is determined based on the superposition comparison result, and whether the compensation is abnormal is judged; If the satellite image corresponding to the target area has a weak deformation tendency, the compensation times for the satellite image are maintained.

8. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that, The deformation interference characterization parameter is positively correlated with the error compensation times for the satellite image.

9. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that The process of determining the texture feature compensation change ratio based on the superposition comparison result includes placing the satellite images obtained by adjacent secondary compensation in the same coordinate system and comparing the coincidence degrees of each texture feature; determining the changing texture features based on the coincidence degree; determining the ratio of the area of the changing texture features to the area of all texture features as the texture feature compensation change ratio; wherein, if the coincidence degree of the texture features is less than a predetermined coincidence degree threshold, the texture features are changing texture features.

10. The error compensation method for realizing high-precision deformation inversion according to claim 1, characterized in that The judgment of whether the compensation is abnormal, wherein if the compensation change ratio is greater than a preset compensation change ratio, it is determined that the compensation is abnormal and error compensation continues; if the compensation change ratio is less than or equal to the preset compensation change ratio, it is determined that the compensation is not abnormal.

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