Ground elevation model generation method based on common-orbit satellites

Abstract

The invention discloses a ground elevation model generation method based on a common-orbit satellite, and relates to the technical field of the Internet. Multi-period elevation data is collected and preprocessed to form an elevation data set D, time sequence consistency detection and correction are carried out on the elevation data set D based on a consistency factor C, a corrected elevation data set Dc is obtained, and the corrected elevation data set Dc is calculated. Calculating a short-term elevation change index H to mark and eliminate dynamic interference points, obtaining an elevation data set Ds after elimination, integrating multi-period data based on calculation of an elevation fusion evaluation index Hf, forming a ground elevation data set DEMb, and comparing with a preset precision allowable error threshold Tth to obtain an available state result of the ground elevation data set DEMb. Construction of a high-precision ground elevation model is completed through iterative optimization, efficient fusion of multi-period elevation data after dynamic elimination of interference points is realized, and the stable ground elevation model capable of reflecting dynamic change characteristics is generated.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a method for generating a ground elevation model based on co-orbit satellites. Background Art

[0002] The mid-ground elevation model is one of the core research directions in the field of geographic information science, and it has important application value in disaster prediction, urban planning, resource management, etc. As a specific research direction in this field, the use of satellite remote sensing technology to construct elevation models has become one of the important means of high-precision terrain mapping due to its wide coverage and high observation efficiency. In satellite remote sensing technology, the application of co-orbit satellites is particularly unique. It refers to satellites with the same orbital parameters that can repeatedly observe the same surface area in a periodic manner. This feature makes it possible to obtain high-precision terrain data in multiple time series.

[0003] In actual application scenarios, such as the assessment of terrain changes in flood monitoring, rapid recovery modeling of terrain in disaster areas after an earthquake, and micro-topography analysis in crop-growing areas, multiple observations by satellites in the same orbit can provide key data for accurate and efficient terrain modeling.

[0004] Although satellites in the same orbit have significant advantages in generating ground elevation models, existing processing methods have obvious limitations in multi-source data fusion. Specifically, because the data generated by multiple flights are affected by observation time, weather conditions, and dynamic changes in the surface, such as rainfall and seasonal vegetation growth, the quality and consistency of different data sets vary greatly, resulting in accuracy deviations in the generated elevation models. In addition, current data processing algorithms usually rely on simple averaging or interpolation methods, which cannot effectively eliminate the interference of temporary surface changes on the model. This deficiency not only limits the application potential of elevation models in dynamic and complex terrain, but also reduces the reliability of rapid response in emergency scenarios such as natural disasters. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for generating a ground elevation model based on co-orbital satellites, which solves the problems mentioned in the background technology.

[0006] To achieve the above purpose, the present invention is implemented by the following technical scheme: a method for generating a ground elevation model based on co-orbital satellites, comprising the following steps:

[0007] S1, collect multi-period elevation data of satellites in the same orbit, obtain relevant observation data, and pre-process them to form an elevation data set D;

[0008] S2, extract features from the elevation dataset D, and then perform time series consistency detection to determine the stability of the elevation dataset D. By quantifying the consistency of multi-period data, obtain the consistency factor C, and make corrections based on the consistency factor C to obtain the corrected elevation dataset Dc;

[0009] S3, analyzing the short-term surface changes of the corrected elevation data set Dc, calculating the short-term elevation change index △H to dynamically remove interference points, and marking the interference points to obtain the removed elevation data set Ds after dynamic removal;

[0010] S4, by fusing the removed elevation data sets Ds of different time series, obtaining the elevation fusion evaluation index Hf, and integrating them to form the ground elevation dataset DEMb, and comparing them with the preset accuracy allowable error threshold Tthe, obtaining the available status result of the ground elevation dataset DEMb;

[0011] S5. Iterative optimization is performed according to the available status results of the ground elevation dataset DEMb, and a ground elevation model is constructed.

[0012] Preferably, said S1 includes S11 and S12;

[0013] S11, collect multi-period elevation data of the same-orbit satellite, record the elevation data Hij of each observation and related observation conditions, including sampling time Ti and illumination conditions Li, to form an initial elevation data set Dr = {Hij, Ti, Li};

[0014] Among them, the elevation data Hij is specifically represented by the elevation value of the j-th ground sampling point of the i-th observation; the sampling time T specifically represents the observation timestamp of the i-th sampling, which is used to identify the time sequence of the observation; the lighting condition Li represents the ambient lighting condition of the i-th sampling.

[0015] Preferably, S12, preprocessing the initial elevation data set Dr, including noise removal, outlier correction and data standardization, to generate an elevation data set D = {Hij (std), Ti, Li (std)}, wherein Hij (std) and Li (std) represent the standardized elevation data and standardized lighting conditions after data standardization preprocessing, respectively;

[0016] Among them, noise removal is carried out by using the upper and lower quartile method to detect the initial elevation data set Dr and remove the noise points in the observation data; outlier correction is carried out by using the linear interpolation method to correct the outliers of the initial elevation data set Dr after noise removal; data standardization is carried out by using the minimum-maximum normalization method to normalize the initial elevation data set Dr after the outlier correction to the range of 0 to 1.

[0017] Preferably, S2 includes S21 and S22;

[0018] S21, extract features from the elevation dataset D, obtain standardized elevation data Hij(std) and sampling time Ti, perform time series consistency detection on multi-period data of the same ground sampling point j, and form a time series feature set Hj={H1j(std), H2j(std), H3j(std)..., Hnj(std)} to judge the stability of the elevation dataset D, and obtain the consistency factor C by quantifying the consistency of multi-period data;

[0019] Where n represents the total number of sampling observations, Hj represents the elevation time series characteristics of ground sampling point j;

[0020] The consistency factor C is obtained by the following calculation formula:

[0021]

[0022] In the formula, Cj represents the consistency factor of the ground sampling point j, σ(Hj) represents the standard deviation of the time series feature set Hj, which is specifically used to measure the degree of data fluctuation, Hmax and Hmin represent the upper and lower elevation values ​​at the ground sampling point j, respectively;

[0023] Among them, the standard deviation σ(Hj) is obtained by The calculation formula is obtained, where: Represents the mean of the standardized elevation data Hij(std) in the time series feature set.

[0024] Preferably, S22, comparing the obtained consistency factor C with a preset consistency state threshold Cthe, obtaining a data set correction execution state result, and correcting the elevation data set D according to the data set correction execution state result to obtain a corrected elevation data set Dc;

[0025] The dataset modification execution status result is obtained by the following comparison method:

[0026] When the consistency factor C is less than the consistency state threshold Cthe, the result of the dataset correction execution state is obtained as executing correction, including correcting the standardized elevation data Hij(std) of the ground sampling point j to the mean value Hj, replacing the data of the ground sampling point j and adding it to the corrected elevation dataset Dc;

[0027] When the consistency factor C≥consistency state threshold Cthe, the dataset correction execution state result is obtained as no correction is performed, and the standardized elevation data Hij(std) of the ground sampling point j is directly added to the corrected elevation dataset Dc.

[0028] Preferably, said S3 includes S31 and S32;

[0029] S31, analyzing the short-term surface changes of the corrected elevation data set Dc, including calculating the short-term fluctuations of the same ground sampling point j between adjacent time points, obtaining the short-term elevation change index △H, and dynamically removing interference points;

[0030] The short-term elevation change index △H is obtained by the following calculation formula:

[0031]

[0032] Where △Hij represents the short-term elevation change index of the j-th ground sampling point in the i-th observation, Hij(std) represents the standardized elevation data of the j-th ground sampling point in the corrected elevation dataset Dc, H(i+1)j(std) represents the standardized elevation data of the j-th ground sampling point in the (i+1)-th observation, Ti and Tk represent adjacent sampling times, specifically representing the sampling time Ti and the sampling time T(i+1).

[0033] Preferably, S32, according to the comparison between the short-term elevation change index △Hij and the preset short-term change assessment threshold Hthe, the interference state result Mij of the ground sampling point j position is determined, and the interference point is marked at the ground sampling point j position according to the interference state result Mij, and the eliminated elevation data set Ds is obtained after the corrected elevation data set Dc is dynamically eliminated;

[0034] The interference state result Mij is obtained by the following comparison method:

[0035]

[0036] Among them, when the short-term elevation change index △Hij> the short-term change assessment threshold Hthe, the interference state result Mij=1, which specifically indicates that the position of the ground sampling point j needs to be marked as an interference point;

[0037] When the short-term elevation change index △Hij≤the short-term change assessment threshold Hthe, the interference state result Mij=0, which specifically indicates that the position of the ground sampling point j does not need to be marked as an interference point.

[0038] Preferably, said S4 includes S41 and S42;

[0039] S41, by fusing the removed elevation data sets Ds of different time series, obtaining the elevation fusion evaluation index Hf, and integrating them to form a ground elevation data set DEMb = {Hf, j, Xj, Yj}, where j represents a ground sampling point, and Xj and Yj represent the horizontal axis coordinate and the vertical axis coordinate of the ground sampling point;

[0040] The elevation fusion evaluation index Hf is obtained by the following calculation formula:

[0041]

[0042] Where Hij(std) represents the standardized elevation data of the jth ground sampling point after eliminating the i-th observation in the elevation data set Ds, and n represents the total number of sampling observations.

[0043] Preferably, S42, by extracting from the ground elevation dataset DEMb a comparison state with a preset accuracy allowable error threshold Tthe, obtaining a usable state result of the ground elevation dataset DEMb;

[0044] The available status results of the ground elevation dataset DEMb are obtained by the following comparison method:

[0045]

[0046] in, Indicates the presence of logical symbols, Represents all logical symbols;

[0047] When Mdem=1, the available status of the ground elevation dataset DEMb is obtained as available;

[0048] When Mdem=0, the available status of the ground elevation dataset DEMb is obtained as unavailable.

[0049] Preferably, the S5 includes S51;

[0050] S51. Trigger the execution of an iterative optimization mechanism according to the available status result of the ground elevation dataset DEMb. When the available status result of the ground elevation dataset DEMb is available, the execution of the iterative optimization mechanism is not triggered, and a prompt is given to build a ground elevation model. When the available status result of the ground elevation dataset DEMb is unavailable, the execution of the iterative optimization mechanism is triggered, including adjusting the accuracy allowable error threshold Tthe, the short-term change assessment threshold Hthe and the consistency status threshold Cthe.

[0051] The present invention provides a method for generating a ground elevation model based on co-orbital satellites, which has the following beneficial effects:

[0052] (1) The elevation dataset D is formed by collecting multi-period elevation data and preprocessing them; the elevation dataset D is tested and corrected based on the consistency factor C to obtain the corrected elevation dataset Dc; the short-term elevation change index △H is calculated to mark and remove dynamic interference points, and the elevation data set Ds after removal is obtained; the multi-period data is integrated based on the calculation of the elevation fusion evaluation index Hf to form the ground elevation dataset DEMb, and the available state result of the ground elevation dataset DEMb is obtained by comparing it with the preset accuracy allowable error threshold Tthe; finally, the construction of a high-precision ground elevation model is completed through iterative optimization, and the efficient fusion of multi-period elevation data after dynamic removal of interference points is realized, and a stable, high-precision ground elevation model that can reflect dynamic change characteristics is generated. Compared with the traditional method, this scheme not only improves the accuracy and consistency of the ground elevation model, but also has strong robustness and adaptability, providing a scientific and efficient solution for high-precision terrain modeling under complex terrain conditions.

[0053] (2) By quantifying the short-term elevation fluctuation of ground sampling point j between adjacent time points, it effectively captures abnormal changes caused by temporary factors such as vegetation cover and rainwater accumulation in a short period of time.

[0054] By marking interference points and dynamically removing them, the removed elevation data set Ds is generated, thereby eliminating the impact of dynamic interference points on elevation data to the greatest extent, accurately capturing the elevation fluctuations of ground sampling points between adjacent time points, and realizing accurate dynamic removal based on the short-term change assessment threshold Hthe. Compared with the traditional static removal method, it can dynamically mark and remove short-term abnormal points while retaining stable terrain change information, laying a more reliable foundation for dynamic change processing for the subsequent construction of ground elevation models.

[0055] (3) By fusing and eliminating the elevation data set Ds, calculating the elevation fusion evaluation index Hf, and integrating to generate the ground elevation dataset DEMb, the efficient fusion and spatial matching of multi-period data is completed, and the availability of the ground elevation dataset DEMb is accurately judged. Through the trigger mechanism of the available status results, the data fusion quality is iteratively optimized, which can not only efficiently quantify the accuracy status of the ground elevation data, but also intelligently adjust the unavailable data through the iterative optimization mechanism, realizing the closed-loop management of ground elevation data from quality assessment to model construction. Compared with the traditional single error evaluation method, by adjusting multiple key thresholds, the accuracy and stability of data fusion are continuously optimized, ensuring that the final constructed ground elevation model has high accuracy and strong robustness under different environmental conditions, providing reliable support for large-scale and multi-period terrain mapping. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The present invention is a schematic diagram of the steps of a method for generating a ground elevation model based on co-orbital satellites. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] The present invention provides a method for generating a ground elevation model based on co-orbital satellites. Figure 1 , including the following steps:

[0060] S1, collect multi-period elevation data of satellites in the same orbit, obtain relevant observation data, and pre-process them to form an elevation data set D;

[0061] S2, extract features from the elevation dataset D, and then perform time series consistency detection to determine the stability of the elevation dataset D. By quantifying the consistency of multi-period data, obtain the consistency factor C, and make corrections based on the consistency factor C to obtain the corrected elevation dataset Dc;

[0062] S3, analyzing the short-term surface changes of the corrected elevation data set Dc, calculating the short-term elevation change index △H to dynamically remove interference points, and marking the interference points to obtain the removed elevation data set Ds after dynamic removal;

[0063] S4, by fusing the removed elevation data sets Ds of different time series, obtaining the elevation fusion evaluation index Hf, and integrating them to form the ground elevation dataset DEMb, and comparing them with the preset accuracy allowable error threshold Tthe, obtaining the available status result of the ground elevation dataset DEMb;

[0064] S5. Iterative optimization is performed according to the available status results of the ground elevation dataset DEMb, and a ground elevation model is constructed.

[0065] In this embodiment, the elevation data set D is formed by collecting multi-period elevation data and preprocessing it; the elevation data set D is tested and corrected for time series consistency based on the consistency factor C to obtain the corrected elevation data set Dc; the short-term elevation change index △H is calculated to mark and remove dynamic interference points to obtain the removed elevation data set Ds; the multi-period data is integrated based on the calculation of the elevation fusion evaluation index Hf to form the ground elevation data set DEMb, and the available state result of the ground elevation data set DEMb is obtained by comparing it with the preset accuracy allowable error threshold Tthe; finally, the construction of a high-precision ground elevation model is completed through iterative optimization, and the efficient fusion of multi-period elevation data after dynamic removal of interference points is achieved, generating a stable, high-precision ground elevation model that can reflect dynamic change characteristics. Compared with traditional methods, this solution not only improves the accuracy and consistency of the ground elevation model, but also has strong robustness and adaptability, providing a scientific and efficient solution for high-precision terrain modeling under complex terrain conditions.

[0066] Example 2

[0067] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11 and S12;

[0068] S11, collect multi-period elevation data of the same-orbit satellite, record the elevation data Hij of each observation and related observation conditions, including sampling time Ti and illumination conditions Li, to form an initial elevation data set Dr = {Hij, Ti, Li};

[0069] Among them, the elevation data Hij is specifically represented by the elevation value of the j-th ground sampling point of the i-th observation; the sampling time T specifically represents the observation timestamp of the i-th sampling, which is used to identify the time sequence of the observation; the lighting condition Li represents the ambient lighting condition of the i-th sampling.

[0070] S12, preprocessing the initial elevation data set Dr, including noise removal, outlier correction and data standardization, to generate an elevation data set D = {Hij(std), Ti, Li(std)}, where Hij(std) and Li(std) represent the standardized elevation data and standardized illumination conditions after data standardization preprocessing, respectively;

[0071] Among them, noise removal is carried out by using the upper and lower quartile method to detect the initial elevation data set Dr and remove the noise points in the observation data; outlier correction is carried out by using the linear interpolation method to correct the outliers of the initial elevation data set Dr after noise removal; data standardization is carried out by using the minimum-maximum normalization method to normalize the initial elevation data set Dr after the outlier correction to the range of 0 to 1.

[0072] In this embodiment, an initial elevation data set Dr is formed through the acquisition process, which provides comprehensive basic information support for the accuracy of subsequent data and multi-time period fusion. Furthermore, through noise removal, outlier correction and data standardization processing, the noise points in the data are effectively removed, the abnormal data are corrected, and the elevation data set D is generated, which significantly improves the integrity and quality of the elevation data; ensuring the consistency of multi-time period data in subsequent fusion calculations. Compared with traditional methods, the present invention can more accurately remove observation noise and correct outliers. At the same time, through the comprehensive recording of multi-dimensional observation parameters, the environmental adaptability of elevation data is enhanced, laying a solid foundation for the construction of a high-precision ground elevation model.

[0073] Example 3

[0074] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0075] S21, extract features from the elevation dataset D, obtain standardized elevation data Hij(std) and sampling time Ti, perform time series consistency detection on multi-period data of the same ground sampling point j, and form a time series feature set Hj={H1j(std), H2j(std), H3j(std)..., Hnj(std)} to judge the stability of the elevation dataset D, and obtain the consistency factor C by quantifying the consistency of multi-period data;

[0076] Where n represents the total number of sampling observations, Hj represents the elevation time series characteristics of ground sampling point j;

[0077] The consistency factor C is obtained by the following calculation formula:

[0078]

[0079] In the formula, Cj represents the consistency factor of the ground sampling point j, σ(Hj) represents the standard deviation of the time series feature set Hj, which is specifically used to measure the degree of data fluctuation, Hmax and Hmin represent the upper and lower elevation values ​​at the ground sampling point j, respectively;

[0080] Among them, the standard deviation σ(Hj) is obtained by The calculation formula is obtained, where: Represents the mean of the standardized elevation data Hij(std) in the time series feature set.

[0081] S22, comparing the obtained consistency factor C with a preset consistency state threshold Cthe, obtaining a dataset correction execution state result, and correcting the elevation dataset D according to the dataset correction execution state result to obtain a corrected elevation dataset Dc;

[0082] The dataset modification execution status result is obtained by the following comparison method:

[0083] When the consistency factor C is less than the consistency state threshold Cthe, the result of the dataset correction execution state is obtained as executing correction, including correcting the standardized elevation data Hij(std) of the ground sampling point j to the mean value Hj, replacing the data of the ground sampling point j and adding it to the corrected elevation dataset Dc;

[0084] When the consistency factor C≥consistency state threshold Cthe, the dataset correction execution state result is obtained as no correction is performed, and the standardized elevation data Hij(std) of the ground sampling point j is directly added to the corrected elevation dataset Dc.

[0085] In this embodiment, the time series feature set Hj is obtained by extracting features from the elevation data set D, and the consistency factor C is calculated based on the fluctuation of the time series data of the ground sampling point j. By quantifying the consistency factor C, the stability of the time series feature set is accurately evaluated, providing a scientific basis for subsequent correction processing. According to the comparison results of the consistency factor C and the preset consistency state threshold Cthe, the data set correction execution status result is generated, which solves the problem of judgment error caused by the difference in fluctuation amplitude of data at different sampling locations and times. At the same time, according to the strict comparison mechanism of the consistency factor C and the consistency state threshold Cthe, the stable data and the data to be corrected are effectively distinguished, and the targeted correction of the data is accurately realized. Compared with the traditional one-size-fits-all correction method, only the data with large fluctuations are processed through mean correction, which not only improves the overall consistency of the elevation data, but also retains the spatiotemporal characteristics of the original data to the maximum extent, thereby providing high-quality basic data support for the construction of a high-precision ground elevation model.

[0086] Example 4

[0087] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0088] S31, analyzing the short-term surface changes of the corrected elevation data set Dc, including calculating the short-term fluctuations of the same ground sampling point j between adjacent time points, obtaining the short-term elevation change index △H, and dynamically removing interference points;

[0089] The short-term elevation change index △H is obtained by the following calculation formula:

[0090]

[0091] Where △Hij represents the short-term elevation change index of the j-th ground sampling point in the i-th observation, Hij(std) represents the standardized elevation data of the j-th ground sampling point in the corrected elevation dataset Dc, H(i+1)j(std) represents the standardized elevation data of the j-th ground sampling point in the (i+1)-th observation, Ti and Tk represent adjacent sampling times, specifically representing the sampling time Ti and the sampling time T(i+1).

[0092] S32, comparing the short-term elevation change index △Hij with the preset short-term change assessment threshold Hthe, determining the interference state result Mij of the ground sampling point j, and marking the interference point at the ground sampling point j according to the interference state result Mij, and obtaining the eliminated elevation data set Ds after the corrected elevation data set Dc is dynamically eliminated;

[0093] The interference state result Mij is obtained by the following comparison method:

[0094]

[0095] Among them, when the short-term elevation change index △Hij> the short-term change assessment threshold Hthe, the interference state result Mij=1, which specifically indicates that the position of the ground sampling point j needs to be marked as an interference point;

[0096] When the short-term elevation change index △Hij≤the short-term change assessment threshold Hthe, the interference state result Mij=0, which specifically indicates that the position of the ground sampling point j does not need to be marked as an interference point.

[0097] In this embodiment, the short-term dynamic change analysis and interference point marking of the corrected elevation data set Dc are realized, which solves the problem that short-term surface changes may introduce dynamic interference and thus reduce the accuracy of elevation data. By calculating the short-term elevation change index △H, the short-term elevation fluctuation degree of the ground sampling point j between adjacent time points is quantified, and the abnormal changes caused by temporary factors such as vegetation coverage and rainwater accumulation in a short period of time are effectively captured. Based on the comparison between the short-term elevation change index ΔH and the preset short-term change assessment threshold Hthe, the interference state result is generated, and the interference points are marked and dynamically removed to generate the removed elevation data set Ds, thereby eliminating the influence of dynamic interference points on the elevation data to the greatest extent, and can accurately capture the elevation fluctuations of the ground sampling points between adjacent time points, and also realize accurate dynamic removal based on the short-term change assessment threshold Hthe. Compared with the traditional static removal method, it can dynamically mark and remove short-term abnormal points while retaining stable terrain change information, providing strong dynamic interference robustness support for elevation data. This method significantly improves the temporal consistency and data quality of the eliminated elevation data set Ds, laying a more reliable foundation for dynamic change processing for the subsequent construction of the ground elevation model.

[0098] Example 5

[0099] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: the S4 includes S41 and S42;

[0100] S41, by fusing the removed elevation data sets Ds of different time series, obtaining the elevation fusion evaluation index Hf, and integrating them to form a ground elevation data set DEMb = {Hf, j, Xj, Yj}, where j represents a ground sampling point, and Xj and Yj represent the horizontal axis coordinate and the vertical axis coordinate of the ground sampling point;

[0101] The elevation fusion evaluation index Hf is obtained by the following calculation formula:

[0102]

[0103] Where Hij(std) represents the standardized elevation data of the jth ground sampling point after eliminating the i-th observation in the elevation data set Ds, and n represents the total number of sampling observations.

[0104] S42, obtaining the available status result of the ground elevation dataset DEMb by extracting and comparing the status with the preset accuracy allowable error threshold Tthe from the ground elevation dataset DEMb;

[0105] The available status results of the ground elevation dataset DEMb are obtained by the following comparison method:

[0106]

[0107] in, Indicates the existence of a logical symbol, specifically indicating that there is at least one elevation fusion evaluation index Hf ≥ the accuracy allowable error threshold Tthe in the ground elevation dataset DEMb, and returns 1; Represents the overall logical symbol, specifically indicating that all elevation fusion evaluation indexes Hf in the ground elevation dataset DEMb meet the accuracy tolerance threshold Tthe, and return 0;

[0108] When Mdem=1, the available status of the ground elevation dataset DEMb is obtained as available;

[0109] When Mdem=0, the available status of the ground elevation dataset DEMb is obtained as unavailable.

[0110] The S5 includes S51;

[0111] S51. Trigger the execution of an iterative optimization mechanism according to the available status result of the ground elevation dataset DEMb. When the available status result of the ground elevation dataset DEMb is available, the execution of the iterative optimization mechanism is not triggered, and a prompt is given to build a ground elevation model. When the available status result of the ground elevation dataset DEMb is unavailable, the execution of the iterative optimization mechanism is triggered, including adjusting the accuracy allowable error threshold Tthe, the short-term change assessment threshold Hthe and the consistency status threshold Cthe.

[0112] In this embodiment, by fusing and eliminating the elevation data set Ds, calculating the elevation fusion evaluation index Hf, and integrating and generating the ground elevation data set DEMb, the efficient fusion and spatial matching of multi-period data are completed. Furthermore, by comparing the elevation fusion evaluation index Hf with the preset accuracy tolerance error threshold Tthe, the availability of the ground elevation data set DEMb can be accurately judged. Through the trigger mechanism of the available state result, the accuracy tolerance error threshold Tthe, the short-term change evaluation threshold Hthe and the consistency state threshold Cthe are automatically adjusted for the unavailable ground elevation data set, and the data fusion quality is iteratively optimized. It can not only efficiently quantify the accuracy state of the ground elevation data, but also intelligently adjust the unavailable data through the iterative optimization mechanism, realizing the closed-loop management of ground elevation data from quality assessment to model construction. Compared with the traditional single error assessment method, by adjusting multiple key thresholds, the accuracy and stability of data fusion are continuously optimized, ensuring that the final constructed ground elevation model has high accuracy and strong robustness under different environmental conditions, providing reliable support for large-scale and multi-period terrain mapping.

[0113] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for generating a ground elevation model based on co-orbital satellites, characterized in that: The following steps are involved: S1, collect multi-period elevation data of satellites in the same orbit, obtain relevant observation data, and pre-process them to form an elevation data set D; S2, extract features from the elevation dataset D, and then perform time series consistency detection to determine the stability of the elevation dataset D. By quantifying the consistency of multi-period data, obtain the consistency factor C, and make corrections based on the consistency factor C to obtain the corrected elevation dataset Dc; S3, analyzing the short-term surface changes of the corrected elevation data set Dc, calculating the short-term elevation change index △H to dynamically remove interference points, and marking the interference points to obtain the removed elevation data set Ds after dynamic removal; S4, by fusing the removed elevation data sets Ds of different time series, obtaining the elevation fusion evaluation index Hf, and integrating them to form the ground elevation dataset DEMb, and comparing them with the preset accuracy allowable error threshold Tthe, obtaining the available status result of the ground elevation dataset DEMb; S5. Iteratively optimize and construct a ground elevation model based on the available status results of the ground elevation dataset DEMb.

2. The method for generating a ground elevation model based on co-orbital satellites according to claim 1, characterized in that: Said S1 includes S11 and S12; S11, collect multi-period elevation data of the same-orbit satellite, record the elevation data Hij of each observation and related observation conditions, including sampling time Ti and illumination conditions Li, to form an initial elevation data set Dr = {Hij, Ti, Li}; Among them, the elevation data Hij is specifically represented by the elevation value of the j-th ground sampling point of the i-th observation; the sampling time T specifically represents the observation timestamp of the i-th sampling, which is used to identify the time sequence of the observation; the lighting condition Li represents the ambient lighting condition of the i-th sampling.

3. The method for generating a ground elevation model based on co-orbital satellites according to claim 2, characterized in that: S12, preprocessing the initial elevation data set Dr, including noise removal, outlier correction and data standardization, to generate an elevation data set D = {Hij(std), Ti, Li(std)}, where Hij(std) and Li(std) represent the standardized elevation data and standardized illumination conditions after data standardization preprocessing, respectively; Among them, noise removal is carried out by using the upper and lower quartile method to detect the initial elevation data set Dr and remove the noise points in the observation data; outlier correction is carried out by using the linear interpolation method to correct the outliers of the initial elevation data set Dr after noise removal; data standardization is carried out by using the minimum-maximum normalization method to normalize the initial elevation data set Dr after the outlier correction to the range of 0 to 1.

4. The method for generating a ground elevation model based on co-orbital satellites according to claim 1, characterized in that: The S2 includes S21 and S22; S21, extract features from the elevation dataset D, obtain standardized elevation data Hij(std) and sampling time Ti, perform time series consistency detection on multi-period data of the same ground sampling point j, and form a time series feature set Hj={H1j(std), H2j(std), H3j(std)..., Hnj(std)} to judge the stability of the elevation dataset D, and obtain the consistency factor C by quantifying the consistency of multi-period data; Where n represents the total number of sampling observations, Hj represents the elevation time series characteristics of ground sampling point j; The consistency factor C is obtained by the following calculation formula: In the formula, Cj represents the consistency factor of the ground sampling point j, σ(Hj) represents the standard deviation of the time series feature set Hj, which is specifically used to measure the degree of data fluctuation, Hmax and Hmin represent the upper and lower elevation values ​​at the ground sampling point j, respectively; Among them, the standard deviation σ(Hj) is obtained by The calculation formula is obtained, where: Represents the mean of the standardized elevation data Hij(std) in the time series feature set.

5. The method for generating a ground elevation model based on co-orbital satellites according to claim 4, characterized in that: S22, comparing the obtained consistency factor C with a preset consistency state threshold Cthe, obtaining a dataset correction execution state result, and correcting the elevation dataset D according to the dataset correction execution state result to obtain a corrected elevation dataset Dc; The dataset modification execution status result is obtained by the following comparison method: When the consistency factor C is less than the consistency state threshold Cthe, the result of the dataset correction execution state is obtained as execution correction, including correcting the standardized elevation data Hij(std) of the ground sampling point j to the mean value The data of the replaced ground sampling point j is added to the corrected elevation data set Dc; When the consistency factor C≥consistency state threshold Cthe, the dataset correction execution state result is obtained as no correction is performed, and the standardized elevation data Hij(std) of the ground sampling point j is directly added to the corrected elevation dataset Dc.

6. The method for generating a ground elevation model based on co-orbital satellites according to claim 5, characterized in that: The S3 includes S31 and S32; S31, analyzing the short-term surface changes of the corrected elevation data set Dc, including calculating the short-term fluctuations of the same ground sampling point j between adjacent time points, obtaining the short-term elevation change index △H, and dynamically removing interference points; The short-term elevation change index △H is obtained by the following calculation formula: Where △Hij represents the short-term elevation change index of the j-th ground sampling point in the i-th observation, Hij(std) represents the standardized elevation data of the j-th ground sampling point in the corrected elevation dataset Dc, H(i+1)j(std) represents the standardized elevation data of the j-th ground sampling point in the (i+1)-th observation, Ti and Tk represent adjacent sampling times, specifically representing the sampling time Ti and the sampling time T(i+1).

7. The method for generating a ground elevation model based on co-orbital satellites according to claim 6, characterized in that: S32, comparing the short-term elevation change index △Hij with the preset short-term change assessment threshold Hthe, determining the interference state result Mij of the ground sampling point j, and marking the interference point at the ground sampling point j according to the interference state result Mij, and obtaining the eliminated elevation data set Ds after the corrected elevation data set Dc is dynamically eliminated; The interference state result Mij is obtained by the following comparison method: Among them, when the short-term elevation change index △Hij> the short-term change assessment threshold Hthe, the interference state result Mij=1, which specifically indicates that the position of the ground sampling point j needs to be marked as an interference point; When the short-term elevation change index △Hij≤the short-term change assessment threshold Hthe, the interference state result Mij=0, which specifically indicates that the position of the ground sampling point j does not need to be marked as an interference point.

8. The method for generating a ground elevation model based on co-orbital satellites according to claim 1, characterized in that: The S4 includes S41 and S42; S41, by fusing the removed elevation data sets Ds of different time series, obtaining the elevation fusion evaluation index Hf, and integrating them to form a ground elevation data set DEMb = {Hf, j, Xj, Yj}, where j represents a ground sampling point, and Xj and Yj represent the horizontal axis coordinate and the vertical axis coordinate of the ground sampling point; The elevation fusion evaluation index Hf is obtained by the following calculation formula: Where Hij(std) represents the standardized elevation data of the jth ground sampling point after eliminating the i-th observation in the elevation data set Ds, and n represents the total number of sampling observations.

9. The method for generating a ground elevation model based on co-orbital satellites according to claim 8, characterized in that: S42, obtaining the available status result of the ground elevation dataset DEMb by extracting and comparing the status with the preset accuracy allowable error threshold Tthe from the ground elevation dataset DEMb; The available status results of the ground elevation dataset DEMb are obtained by the following comparison method: in, Indicates the presence of logical symbols, Represents all logical symbols; When Mdem=1, the available status of the ground elevation dataset DEMb is obtained as available; When Mdem=0, the available status of the ground elevation dataset DEMb is obtained as unavailable.

10. The method for generating a ground elevation model based on co-orbital satellites according to claim 1, characterized in that: The S5 includes S51; S51. Trigger the execution of an iterative optimization mechanism according to the available status result of the ground elevation dataset DEMb. When the available status result of the ground elevation dataset DEMb is available, the execution of the iterative optimization mechanism is not triggered, and a prompt is given to build a ground elevation model. When the available status result of the ground elevation dataset DEMb is unavailable, the execution of the iterative optimization mechanism is triggered, including adjusting the accuracy allowable error threshold Tthe, the short-term change assessment threshold Hthe and the consistency status threshold Cthe.

Images