A method and system for terrain inversion based on multi-source satellite remote sensing data

Abstract

This invention relates to the field of data processing technology, specifically to a method and system for terrain inversion based on multi-source satellite remote sensing data. The method includes: acquiring multi-source satellite remote sensing data from various locations within the area to be inverted using various remote sensing satellites; performing error elimination on the multi-source satellite remote sensing data from each location to obtain clean multi-source satellite remote sensing data for each location; evaluating the accuracy of the clean multi-source satellite remote sensing data from each location during the data fusion process based on the positions of the transmitting and receiving electromagnetic waves by each remote sensing satellite; determining the final satellite remote sensing data for each location based on the clean multi-source satellite remote sensing data and the accuracy; and inverting the terrain elevation model of the area to be inverted based on the final satellite remote sensing data from each location. Thus, this invention improves the accuracy of the terrain elevation model inversion results.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method and system for terrain inversion based on multi-source satellite remote sensing data. Background Technology

[0002] Multi-source satellite remote sensing data refers to remote sensing data acquired from different remote sensing satellites with varying spatial, spectral, and temporal resolutions. It involves remote sensing satellites detecting the reflection of electromagnetic waves by objects on the Earth's surface from space, extracting object information through the transmission and reception of electromagnetic waves, and completing the task of long-distance object identification. Compared to single-source remote sensing data, multi-source remote sensing data, while containing some information redundancy, benefits from complementary and collaborative information. By cross-referencing data obtained from different remote sensing satellites observing the same area from different locations and angles, these data, though originating from independent satellites, possess inherent correlations. Intelligent fusion can improve the accuracy of planar mapping, thereby enabling more precise and intuitive inversion of digital elevation models.

[0003] In some scenarios, directly fusing data from various sources to invert the terrain elevation model has significant limitations: it fails to adequately consider the varying degrees of error caused by displacement differences between different satellites during data acquisition, and it also ignores the potential for insufficient accuracy due to external environmental interference. These unprocessed differences and interferences are directly incorporated into the fusion process, ultimately resulting in inaccurate and unsatisfactory inversion results for the terrain elevation model. Summary of the Invention

[0004] To address the technical problems of inaccurate and unsatisfactory inversion results from terrain elevation models, the present invention aims to provide a terrain inversion method and system based on multi-source satellite remote sensing data.

[0005] To solve the above technical problems, the specific technical solution adopted is as follows:

[0006] In a first aspect, embodiments of the present invention provide a terrain inversion method based on multi-source satellite remote sensing data, comprising: acquiring multi-source satellite remote sensing data at each acquisition location in the area to be inverted using various remote sensing satellites; performing error elimination on the multi-source satellite remote sensing data at each acquisition location based on the positional changes of each remote sensing satellite in transmitting and receiving electromagnetic waves, and the relative positions of each remote sensing satellite in transmitting and receiving electromagnetic waves with each acquisition location, to obtain clean multi-source satellite remote sensing data at each acquisition location; evaluating the accuracy of the clean multi-source satellite remote sensing data at each acquisition location in the data fusion process based on the positions of each remote sensing satellite in transmitting and receiving electromagnetic waves; determining the final satellite remote sensing data at each acquisition location based on the clean multi-source satellite remote sensing data and the accuracy, and inverting the terrain elevation model of the area to be inverted based on the final satellite remote sensing data at each acquisition location.

[0007] Optionally, based on the positional changes of each remote sensing satellite during electromagnetic wave transmission and reception, and the relative positions of each remote sensing satellite during electromagnetic wave transmission and reception with each acquisition location, error elimination is performed on the multi-source satellite remote sensing data at each acquisition location. This includes: determining the first latitude and longitude information of the satellite's nadir point when each remote sensing satellite transmits electromagnetic waves, and the first distance between the satellite's nadir point and the Earth's horizontal height baseline; determining the second latitude and longitude information of the satellite's nadir point when each remote sensing satellite receives electromagnetic waves, and the second distance between the satellite's nadir point and the Earth's horizontal height baseline; and determining the distance from each acquisition location to each remote sensing satellite during electromagnetic wave transmission based on the first latitude and longitude information, the first distance, the third latitude and longitude information of each acquisition location, and the multi-source satellite remote sensing data. The third distance between the locations, and the fourth distance between each acquisition location and the location of each remote sensing satellite when emitting electromagnetic waves, are determined based on the second latitude and longitude information, the second distance, the third latitude and longitude information of each acquisition location, and multi-source satellite remote sensing data. The round-trip distance between each acquisition location and each remote sensing satellite is determined based on the time difference between the transmission and reception of electromagnetic waves by each remote sensing satellite and the propagation speed of electromagnetic waves. Based on the third distance, the fourth distance, and the distance, the error degree of the multi-source satellite remote sensing data at each acquisition location after error elimination is determined. The multi-source satellite remote sensing data is changed unit by unit and the corresponding error degree is calculated until the obtained error degree no longer decreases. The multi-source satellite remote sensing data corresponding to the minimum error degree is selected as the clean multi-source satellite remote sensing data for each acquisition location.

[0008] Optionally, determining the third distance between each acquisition location and the location of each remote sensing satellite when emitting electromagnetic waves, based on the first latitude and longitude information, the first distance, the third latitude and longitude information of each acquisition location, and multi-source satellite remote sensing data, includes: determining the first difference between the corresponding longitude and latitude coordinates in the first latitude and longitude information and the third latitude and longitude information, and the second difference between the multi-source satellite remote sensing data and the first distance; and determining the third distance based on the first difference and the second difference.

[0009] Optionally, based on the second latitude and longitude information, the second distance, the third latitude and longitude information of each acquisition location, and the multi-source satellite remote sensing data, determining the fourth distance between each acquisition location and the location of each remote sensing satellite when transmitting electromagnetic waves includes: determining the third difference between the corresponding longitude and latitude coordinates in the second and third latitude and longitude information, and the fourth difference between the multi-source satellite remote sensing data and the first distance; and determining the fourth distance based on the third difference and the fourth difference.

[0010] Optionally, based on the positions of each remote sensing satellite in transmitting and receiving electromagnetic waves, the accuracy of clean multi-source satellite remote sensing data at each acquisition location during the data fusion process is evaluated, including: determining the average altitude of each remote sensing satellite based on a first and second spacing; determining the first sum of the absolute values ​​of the differences between the clean multi-source satellite remote sensing data at each acquisition location and the clean multi-source satellite remote sensing data at other locations within the neighborhood of each acquisition location; determining the range of the clean multi-source satellite remote sensing data at each acquisition location under each remote sensing satellite; determining the detection difficulty of the clean multi-source satellite remote sensing data at each acquisition location based on the average altitude, the first sum, and the range, wherein the detection difficulty is inversely proportional to the data reference value of the clean multi-source satellite remote sensing data; and evaluating the accuracy based on the clean multi-source satellite remote sensing data, error rate, and detection difficulty at each acquisition location.

[0011] Optionally, the accuracy assessment, based on the clean multi-source satellite remote sensing data, error rate, and detection difficulty of each remote sensing satellite at each acquisition location, includes: calculating the second sum of the reciprocals of the error rates of each remote sensing satellite at each acquisition location; determining representative satellite remote sensing data for each acquisition location based on the second sum, the reciprocals of the error rates of each remote sensing satellite at each acquisition location, and the clean multi-source satellite remote sensing data of each remote sensing satellite at each acquisition location; and assessing the accuracy based on the representative satellite remote sensing data, detection difficulty, and clean multi-source satellite remote sensing data.

[0012] Optionally, the accuracy assessment based on representative satellite remote sensing data, detection difficulty, and clean multi-source satellite remote sensing data includes: determining the absolute value of the difference between clean multi-source satellite remote sensing data and representative satellite remote sensing data; and determining the accuracy of clean multi-source satellite remote sensing data based on the absolute value of the difference and detection difficulty, wherein the accuracy is directly proportional to the accuracy of clean multi-source satellite remote sensing data.

[0013] Optionally, determining the final satellite remote sensing data for each acquisition location based on the clean multi-source satellite remote sensing data and accuracy of each acquisition location includes: determining the third sum of the accuracy of the clean multi-source satellite remote sensing data of all remote sensing satellites at each acquisition location; and determining the final satellite remote sensing data for each acquisition location based on the third sum, the accuracy of the clean multi-source satellite remote sensing data at each acquisition location, and the clean multi-source satellite remote sensing data at each acquisition location.

[0014] Secondly, embodiments of the present invention provide a terrain inversion system based on multi-source satellite remote sensing data, comprising: an acquisition module for acquiring multi-source satellite remote sensing data of each acquisition location in the area to be inverted via each remote sensing satellite; an elimination module for eliminating errors in the multi-source satellite remote sensing data of each acquisition location based on the positional changes of each remote sensing satellite in transmitting and receiving electromagnetic waves, and the relative positions of each remote sensing satellite in transmitting and receiving electromagnetic waves with each acquisition location, to obtain clean multi-source satellite remote sensing data of each acquisition location; an evaluation module for evaluating the accuracy of the clean multi-source satellite remote sensing data of each acquisition location in the data fusion process based on the positions of each remote sensing satellite in transmitting and receiving electromagnetic waves; and an inversion module for determining the final satellite remote sensing data of each acquisition location based on the clean multi-source satellite remote sensing data and the accuracy, and inverting the terrain elevation model of the area to be inverted based on the final satellite remote sensing data of each acquisition location.

[0015] Thirdly, embodiments of the present invention provide a terrain inversion system based on multi-source satellite remote sensing data, comprising: a processor and a memory; wherein the memory is used to store a computer program that can run on the processor; the processor is used to execute the program stored in the memory to implement the steps of the terrain inversion method based on multi-source satellite remote sensing data as mentioned in the first aspect.

[0016] The present invention has the following beneficial effects:

[0017] This invention, based on the dynamic positional changes of each remote sensing satellite during electromagnetic wave transmission and reception, and combined with the spatial positional relationship between the satellites at the moment of electromagnetic wave transmission and reception and the relative positions of each acquisition location, can eliminate errors in multi-source satellite remote sensing data from different acquisition locations. This effectively removes interference caused by differences in satellite displacement, resulting in more accurate and cleaner multi-source satellite remote sensing data. Furthermore, the accuracy of the clean multi-source satellite remote sensing data from each acquisition location during the fusion process is evaluated. Finally, based on the accuracy and characteristics of the clean multi-source satellite remote sensing data itself, the optimal final satellite remote sensing data for each acquisition location is determined. In this way, not only can errors caused by displacement differences between different remote sensing satellites during the data acquisition stage be accurately eliminated, but also more reliable data sources can be selected through accuracy evaluation, removing low-accuracy data sources caused by external environmental interference. This improves the accuracy of satellite remote sensing data from each acquisition location, ultimately making the terrain elevation model inversion results more accurate and the final inversion results more ideal. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a terrain inversion method based on multi-source satellite remote sensing data, provided as an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the relationship between the position change of a remote sensing satellite and the acquisition location, provided as an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of a satellite perspective provided for an embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram of the structure of a terrain inversion system based on multi-source satellite remote sensing data, provided in one embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of another terrain inversion system based on multi-source satellite remote sensing data, provided as an embodiment of the present invention. Detailed Implementation

[0024] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a terrain inversion method and system based on multi-source satellite remote sensing data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] The specific scheme of the terrain inversion method based on multi-source satellite remote sensing data provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0027] Example 1:

[0028] Please see Figure 1 The flowchart illustrates a terrain inversion method based on multi-source satellite remote sensing data provided by an embodiment of the present invention, including:

[0029] Step S101: Acquire multi-source satellite remote sensing data of each collection location in the area to be inverted using various remote sensing satellites.

[0030] Specifically, in this embodiment of the invention, the area to be inverted based on terrain is determined as the area to be inverted. Several remote sensing satellites capable of transmitting electromagnetic waves that can completely cover the area to be inverted are invoked to detect the terrain, obtaining multi-source satellite remote sensing data for each acquisition location within the inversion area from each remote sensing satellite. The multi-source satellite remote sensing data includes remote sensing data acquired by each remote sensing satellite at each acquisition location.

[0031] For example, the number of remote sensing satellites invoked in this embodiment of the invention is: The area to be inverted contains a total of [number] data collection locations. Each location is recorded as a unit, and its latitude and longitude information is recorded as follows: ,in, Indicates the first collection location to the second collection location. The longitude coordinates of each data collection location. Indicates the first collection location to the second collection location. The latitude coordinates of each acquisition location. Further, in this embodiment of the invention, the set of satellite remote sensing data obtained from the remote sensing satellite is recorded as follows: .in, For the first The set of satellite remote sensing data obtained from each location in the area to be inverted by a remote sensing satellite, containing... For the first The first remote sensing satellite obtained the data of the area to be inverted. Satellite remote sensing data at each acquisition location. The satellite remote sensing data from all acquisition locations in the area to be inverted, obtained from all remote sensing satellites, constitute the multi-source satellite remote sensing data in this embodiment of the invention. In this embodiment of the invention, For natural numbers greater than or equal to 1, such as It can take the value 8. It is a natural number greater than or equal to 2, such as 20 in this embodiment of the invention.

[0032] Step S102: Based on the positional changes of each remote sensing satellite in transmitting and receiving electromagnetic waves, and the relative positions of each remote sensing satellite in transmitting and receiving electromagnetic waves with each acquisition location, error elimination is performed on the multi-source satellite remote sensing data of each acquisition location to obtain clean multi-source satellite remote sensing data of each acquisition location.

[0033] Specifically, to achieve global coverage and high-resolution observation, remote sensing satellites are in motion relative to the Earth. This results in a positional difference between the satellite's transmitting and receiving electromagnetic waves and its location relative to the Earth. Furthermore, the satellite's nadir point and the data acquisition location may not be at the same latitude and longitude. All of these factors contribute to errors in the obtained satellite remote sensing data. During the fusion of multi-source satellite remote sensing data, the data from different satellites all contain varying degrees of error, significantly impacting the accuracy of the final fusion result and thus affecting the inversion precision.

[0034] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram illustrating the relationship between the position change of a remote sensing satellite and the acquisition location, provided as an embodiment of the present invention. Figure 2In this context, the positions of a remote sensing satellite when emitting electromagnetic waves and its nadir are not the same, nor are the positions of the satellite and its nadir when receiving electromagnetic waves. Furthermore, the target point (the data acquisition location) and the nadir of the remote sensing satellite are not at the same latitude and longitude. The actual elevation data of the target point is the distance between the horizontal altitude of the target point and the Earth's horizontal altitude baseline. However, when acquiring satellite remote sensing data of the target point, the remote sensing satellite calculates the round-trip distance between the target point and the remote sensing satellite based on the time difference between transmitting and receiving electromagnetic waves and the propagation speed of electromagnetic waves, as shown by the sum of line segments 1 and 2 in the figure. Since the position of the remote sensing satellite changes during transmission and reception, directly using half of the round-trip distance to represent the distance between the remote sensing satellite and the target point introduces an error. Moreover, since the nadir of the remote sensing satellite and the target point are not at the same latitude and longitude, directly subtracting the distance between the remote sensing satellite and the target point from the satellite's altitude to represent the elevation data of the target point results in a significant error compared to the actual elevation data. Satellite remote sensing data includes, but is not limited to, the elevation data mentioned above.

[0035] Furthermore, as an optional embodiment of the present invention, based on the positional changes of each remote sensing satellite in transmitting and receiving electromagnetic waves, and the relative positions of each remote sensing satellite in transmitting and receiving electromagnetic waves with each acquisition location, error elimination of the multi-source satellite remote sensing data at each acquisition location includes: determining the first latitude and longitude information of the satellite's nadir point when each remote sensing satellite transmits electromagnetic waves and the first distance between the satellite's nadir point and the horizontal height baseline of the Earth's surface; determining the second latitude and longitude information of the satellite's nadir point when each remote sensing satellite receives electromagnetic waves and the second distance between the satellite's nadir point and the horizontal height baseline of the Earth's surface; and determining the distance from each acquisition location to each remote sensing satellite based on the first latitude and longitude information, the first distance, the third latitude and longitude information of each acquisition location, and the multi-source satellite remote sensing data. The third distance between the locations where electromagnetic waves were emitted, and the fourth distance between each acquisition location and the location of each remote sensing satellite when they emitted electromagnetic waves, based on the second latitude and longitude information, the second distance, the third latitude and longitude information of each acquisition location, and multi-source satellite remote sensing data, are determined. The round-trip distance between each acquisition location and each remote sensing satellite is determined based on the time difference between the emission and reception of electromagnetic waves by each remote sensing satellite and the propagation speed of electromagnetic waves. Based on the third distance, the fourth distance, and the distance, the error degree of the multi-source satellite remote sensing data at each acquisition location after error elimination is determined. The multi-source satellite remote sensing data is changed unit by unit and the corresponding error degree is calculated until the obtained error degree no longer decreases. The multi-source satellite remote sensing data corresponding to the minimum error degree is selected as the clean multi-source satellite remote sensing data for each acquisition location.

[0036] Specifically, in this embodiment of the invention, the latitude and longitude information can be latitude and longitude coordinates. For example, please refer to... Figure 2 In this embodiment of the invention, the latitude and longitude coordinates of the nadir point of the remote sensing satellite when it transmits electromagnetic waves are defined as follows: Let the first distance between the satellite's nadir point and the Earth's horizontal altitude baseline be denoted as . Let the latitude and longitude coordinates of the nadir point of the remote sensing satellite be denoted as when the remote sensing satellite receives electromagnetic waves. The second distance between the satellite's nadir point and the Earth's horizontal elevation baseline is denoted as... Collection location The latitude and longitude coordinates are as follows In this embodiment of the invention, the data collection location is set. In the The purified multi-source satellite remote sensing data (elevation data) obtained after error elimination from multiple remote sensing satellite detections is as follows: The initial record of the embodiments of the present invention Then, adjustments were made gradually based on the data error rate.

[0037] Furthermore, as an optional embodiment of the present invention, determining the third distance between each acquisition location and the location of each remote sensing satellite when emitting electromagnetic waves, based on the first latitude and longitude information, the first distance, the third latitude and longitude information of each acquisition location, and the multi-source satellite remote sensing data, includes: determining the first difference between the corresponding longitude and latitude coordinates in the first latitude and longitude information and the third latitude and longitude information, and the second difference between the multi-source satellite remote sensing data and the first distance; and determining the third distance based on the first difference and the second difference.

[0038] Specifically, embodiments of the present invention use the collection location In the Taking satellite remote sensing data collected by a remote sensing satellite as an example, this embodiment of the invention specifically uses the following formula to calculate the acquisition location. With the The third distance between the positions of the remote sensing satellites when they emit electromagnetic waves:

[0039] ;

[0040] In the above formula, Indicates the data collection location With the The third distance between the positions of the remote sensing satellites when they emit electromagnetic waves. Indicates the data collection location The longitude coordinates. Indicates the first The longitude coordinates of the nadir point of a remote sensing satellite when it emits electromagnetic waves. Indicates the data collection location Latitude coordinates. Indicates the first The latitude coordinates of the nadir point of a remote sensing satellite when it emits electromagnetic waves. Indicates the first The first distance between the nadir point of a remote sensing satellite and the horizontal elevation baseline of the Earth's surface. Indicates the acquisition position after error elimination In the Multi-source satellite remote sensing data obtained after eliminating errors from multiple remote sensing satellite detections.

[0041] Furthermore, as an optional embodiment of the present invention, determining the fourth distance between each acquisition location and the location of each remote sensing satellite when emitting electromagnetic waves, based on the second latitude and longitude information, the second distance, the third latitude and longitude information of each acquisition location, and the multi-source satellite remote sensing data, includes: determining the third difference between the corresponding longitude and latitude coordinates in the second and third latitude and longitude information, and the fourth difference between the multi-source satellite remote sensing data and the first distance; and determining the fourth distance based on the third difference and the fourth difference.

[0042] Specifically, embodiments of the present invention use the collection location In the Taking satellite remote sensing data collected by a remote sensing satellite as an example, this embodiment of the invention specifically uses the following formula to calculate the acquisition location. With the The fourth distance between the positions of the remote sensing satellites when receiving electromagnetic waves:

[0043] ;

[0044] In the above formula, Indicates the data collection location With the The third distance between the positions of the remote sensing satellites when they receive electromagnetic waves. Indicates the data collection location The longitude coordinates. Indicates the first The longitude coordinates of the nadir point of a remote sensing satellite when it receives electromagnetic waves. Indicates the data collection location Latitude coordinates. Indicates the first The latitude coordinates of the nadir point of a remote sensing satellite when it receives electromagnetic waves. Indicates the first The second distance between the nadir point of a remote sensing satellite and the horizontal elevation baseline of the Earth's surface. Indicates the acquisition position after error elimination In the Pure multi-source satellite remote sensing data obtained after eliminating errors from multiple remote sensing satellite detections.

[0045] Furthermore, in this embodiment of the invention, the round-trip distance between the acquisition location and the remote sensing satellite is calculated based on the time difference between the transmission and reception of electromagnetic waves by the remote sensing satellite and the propagation speed of the electromagnetic waves, and denoted as... This is the actual round-trip distance between the data acquisition location and the remote sensing satellite. Specifically, it is calculated by multiplying the time difference by the speed of electromagnetic wave propagation. In this embodiment of the invention, the purified multi-source satellite remote sensing data after error elimination is used to obtain purified multi-source satellite remote sensing data. The error rate is calculated using the following formula:

[0046] ;

[0047] In the above formula, Indicates pure multi-source satellite remote sensing data The degree of error. Indicates the data collection location The round trip distance between the remote sensing satellite and the remote sensing satellite. Indicates the data collection location With the The third distance between the positions of the remote sensing satellites when they emit electromagnetic waves. Indicates the data collection location With the The third distance between the positions of the remote sensing satellites when they receive electromagnetic waves. and The sum represents the pure multi-source satellite remote sensing data. The distance between the data acquisition location and the remote sensing satellite in one round trip is the calculated distance. and The absolute value of the difference between them is the pure multi-source satellite remote sensing data. The degree of error.

[0048] Furthermore, embodiments of the present invention provide clean multi-source satellite remote sensing data after error elimination. From its initial data First, calculate the error rate for this data. Then change unit by unit And calculate several accordingly. Value, until newly obtained Take the minimum error degree until the value no longer decreases. The corresponding clean multi-source satellite remote sensing data This represents the final, clean multi-source satellite remote sensing data from each acquisition location. The term "unit-by-unit change" refers to first... Calculate the change in height by 1 unit upwards or downwards. Value, take The direction in which the value decreases is the direction of change, and the value then changes unit by unit height along this direction. .

[0049] Step S103: Based on the location of each remote sensing satellite in transmitting and receiving electromagnetic waves, evaluate the accuracy of clean multi-source satellite remote sensing data from each acquisition location during the data fusion process.

[0050] Specifically, during the acquisition process of multi-source satellite remote sensing data, data obtained from different angles may be inaccurate due to magnetic field interference or obstructions in the electromagnetic wave propagation path, resulting in significant discrepancies between some satellite remote sensing data and the actual data. Furthermore, the different altitudes of the remote sensing satellites affect the density of the obtained data, thus impacting the accuracy. Therefore, during data fusion, it is crucial to reference as much accurate satellite remote sensing data as possible to improve inversion accuracy. Consequently, it is necessary to evaluate the accuracy of multi-source satellite remote sensing data from various acquisition locations. This embodiment of the invention assesses data accuracy by considering the altitude of the remote sensing satellites and the relationship between the obtained multi-source satellite remote sensing data and the overall data.

[0051] Furthermore, as an optional embodiment of the present invention, the accuracy of the clean multi-source satellite remote sensing data at each acquisition location in the data fusion process is evaluated based on the positions of each remote sensing satellite in transmitting and receiving electromagnetic waves. This includes: determining the average altitude of each remote sensing satellite based on a first spacing and a second spacing; determining a first sum of the absolute values ​​of the differences between the clean multi-source satellite remote sensing data at each acquisition location and the clean multi-source satellite remote sensing data at other locations within the neighborhood of each acquisition location; determining the range of the clean multi-source satellite remote sensing data at each acquisition location under each remote sensing satellite; determining the detection difficulty of the clean multi-source satellite remote sensing data at each acquisition location based on the average altitude, the first sum, and the range, wherein the detection difficulty is inversely proportional to the data reference value of the clean multi-source satellite remote sensing data; and evaluating the accuracy based on the clean multi-source satellite remote sensing data at each acquisition location, the error rate, and the detection difficulty.

[0052] Specifically, in areas with complex terrain, some locations can only be detected from specific angles. Therefore, the same location presents different detection challenges for different remote sensing satellites, and the true data information of some satellites may be difficult to detect from their locations. For example, ... Figure 3 As shown, Figure 3 This is a schematic diagram of a satellite perspective provided for an embodiment of the present invention. Figure 3In this context, the higher the altitude of the remote sensing satellite, the smaller the area to be inverted is within its "field of view," the more closely the points are clustered together, the larger the range represented by the obtained data points, and the more ambiguous the data. Conversely, the lower the satellite altitude, the larger the area to be inverted appears, the more sparsely the points are spaced out, the smaller the range represented by the obtained data points, and the more accurate the data. Therefore, in this embodiment of the invention, the average altitude of each remote sensing satellite is determined as the average of the first spacing and the second spacing, i.e. .in, Indicates the first The average altitude of each remote sensing satellite Indicates the first The first distance between the nadir point of a remote sensing satellite and the horizontal elevation baseline of the Earth's surface. Indicates the second spacing.

[0053] Furthermore, by Figure 3 It is known that the higher the altitude of the remote sensing satellite, the larger the area covered by the satellite remote sensing data at a given acquisition location, the smaller the differences between the data, and the greater the ambiguity. Therefore, this embodiment of the invention determines the first sum of the absolute values ​​of the differences between the clean multi-source satellite remote sensing data at each acquisition location and the clean multi-source satellite remote sensing data at other locations within the neighborhood of each acquisition location. The neighborhood can be the eight neighborhoods surrounding the acquisition location. This embodiment of the invention denotes the first sum as... .

[0054] Furthermore, the higher the difference in data detected by different remote sensing satellites at the same acquisition location, the worse the accuracy of the satellite remote sensing data at that acquisition location, and the more difficult it is to detect. This embodiment of the invention uses the acquisition location... For example, the collection location The range of the pure multi-source satellite remote sensing data obtained from various remote sensing satellites is denoted as . .in, Indicates the data collection location The maximum value of clean multi-source satellite remote sensing data obtained from various remote sensing satellites. Indicates the data collection location The minimum value of pure multi-source satellite remote sensing data obtained from various remote sensing satellites.

[0055] Furthermore, in this embodiment of the invention, the detection difficulty of clean multi-source satellite remote sensing data at each acquisition location is specifically calculated using the following formula:

[0056] ;

[0057] In the above formula, Indicates the first The collection location is at the [number]th [location]. The difficulty of detecting pure multi-source satellite remote sensing data from multiple remote sensing satellites. Indicates the first The altitude of the remote sensing satellite. Indicates the data collection location The range of pure multi-source satellite remote sensing data obtained from various remote sensing satellites. Indicates the first The collection location is at the [number]th [location]. Pure multi-source satellite remote sensing data from one remote sensing satellite and the first The first sum of the absolute values ​​of the differences between clean multi-source satellite remote sensing data from other locations within the neighborhood of a given acquisition location. The larger the value, the better the data collection location. The greater the discrepancy between data obtained from different remote sensing satellites, the more difficult it is to detect precise values ​​at that acquisition location, and the higher the difficulty for remote sensing satellites to analyze the data at that location. The larger the value, the farther the remote sensing satellite is from the area to be inverted, the more blurred the obtained satellite remote sensing data, and the higher the difficulty of detecting the satellite remote sensing data. Indicates the first The remote sensing satellite for the first The value represents the degree of difference between satellite remote sensing data obtained from a collection location and satellite remote sensing data in its surrounding area. The smaller the value, the higher the consistency of the data obtained by the remote sensing satellite in the area to be inverted, the stronger the ambiguity, and the higher the difficulty of detecting the data at that collection location. The larger the value, the more difficult the detection, and the lower the reference value of the obtained satellite remote sensing data.

[0058] Furthermore, based on the clean multi-source satellite remote sensing data, error rate, and detection difficulty of each remote sensing satellite at each acquisition location, the accuracy evaluation includes: calculating the second sum of the reciprocals of the error rates of each remote sensing satellite at each acquisition location; determining representative satellite remote sensing data for each acquisition location based on the second sum, the reciprocals of the error rates of each remote sensing satellite at each acquisition location, and the clean multi-source satellite remote sensing data of each remote sensing satellite at each acquisition location; and evaluating the accuracy based on the representative satellite remote sensing data, detection difficulty, and clean multi-source satellite remote sensing data.

[0059] Specifically, in the embodiments of the present invention, some of the pure multi-source satellite remote sensing data from each satellite are inaccurate due to electromagnetic interference or angular obstruction, while most of the data are relatively accurate. Therefore, the embodiments of the present invention can initially synthesize the pure multi-source satellite remote sensing data obtained by each remote sensing satellite at the same acquisition location to calculate the representative data of that acquisition location.

[0060] Furthermore, in the embodiments of the present invention, the first... Taking a single data collection location as an example, the remote sensing satellites obtained above at the [number]th [location]... The clean multi-source satellite remote sensing data from each acquisition location are denoted as The error degree is denoted as In this embodiment of the invention, the second sum of the reciprocals of the error degree is first calculated, specifically using the following formula:

[0061] ;

[0062] In the above formula, Indicates the first The collection location is at the [number]th [location]. The sum of the reciprocals of the error rates of each remote sensing satellite. Indicates the first The collection location is at the [number]th [location]. Error rate under remote sensing satellites. This indicates the number of remote sensing satellites.

[0063] Furthermore, the reciprocal of the error rate is used as a reference for each clean multi-source satellite remote sensing data to obtain representative data at the acquisition location. Specifically, the following formula is used for calculation:

[0064] ;

[0065] In the above formula, Indicates the first Representative satellite remote sensing data from each acquisition location. Indicates the first The collection location is at the [number]th [location]. The sum of the reciprocals of the error rates of each remote sensing satellite. Indicates the first The collection location is at the [number]th [location]. Error rate under remote sensing satellites. This indicates the number of remote sensing satellites. Indicates the first The collection location is at the [number]th [location]. Pure multi-source satellite remote sensing data after error elimination from multiple remote sensing satellites.

[0066] Furthermore, as an optional embodiment of the present invention, the accuracy assessment based on representative satellite remote sensing data, detection difficulty, and clean multi-source satellite remote sensing data includes: determining the absolute value of the difference between clean multi-source satellite remote sensing data and representative satellite remote sensing data; and determining the accuracy of clean multi-source satellite remote sensing data based on the absolute value of the difference and the detection difficulty, wherein the accuracy is directly proportional to the accuracy of clean multi-source satellite remote sensing data, and the higher the accuracy, the higher the reference value of clean multi-source satellite remote sensing data in the data fusion process.

[0067] Specifically, the embodiments of the present invention take the first... The remote sensing satellite in the Data obtained at each location after error correction For example, the following formula is used to calculate the first... The remote sensing satellite in the Data obtained at each location after error correction accuracy :

[0068] ;

[0069] In the above formula, Indicates the first The remote sensing satellite in the Data obtained from each sampling location after error correction The accuracy rate. Indicates the first The remote sensing satellite in the The clean, error-free multi-source satellite remote sensing data obtained at each location. Indicates the first Representative satellite remote sensing data from each acquisition location. Indicates the first The collection location is at the [number]th [location]. The difficulty of detecting clean multi-source satellite remote sensing data from multiple remote sensing satellites. exp represents an exponential function with base e. Used for Perform inverse proportional normalization. Among them, The smaller the value, the easier it is for the remote sensing satellite to detect at the acquisition location, and the more accurate the data obtained. Representing data The smaller this value is, the better the difference between the data and the satellite remote sensing data. The closer the data is to representative satellite remote sensing data, the better the data is. The less external influence the acquisition process is affected, the more accurate the data will be. The larger the value, the better the data. The more accurate the data, the more valuable it is for reference during the data fusion process.

[0070] Step S104: Determine the final satellite remote sensing data for each acquisition location based on the clean multi-source satellite remote sensing data and accuracy of each acquisition location, and invert the topographic elevation model of the area to be inverted based on the final satellite remote sensing data for each acquisition location.

[0071] Specifically, this embodiment of the invention evaluates the accuracy of each remote sensing satellite at each acquisition location by combining the clean multi-source satellite remote sensing data obtained from all satellites at the acquisition location using the altitude of each remote sensing satellite. Then, based on the accuracy and the clean multi-source satellite remote sensing data, the final satellite remote sensing data that best approximates the actual situation at each acquisition location is determined.

[0072] Furthermore, as an optional embodiment of the present invention, determining the final satellite remote sensing data for each acquisition location based on the clean multi-source satellite remote sensing data and accuracy of each acquisition location includes: determining a third sum of the accuracy of the clean multi-source satellite remote sensing data of all remote sensing satellites at each acquisition location; and determining the final satellite remote sensing data for each acquisition location based on the third sum, the accuracy of the clean multi-source satellite remote sensing data at each acquisition location, and the clean multi-source satellite remote sensing data at each acquisition location.

[0073] Specifically, the embodiments of the present invention take the first... Taking the first collection location as an example, the following formula is used to calculate the first collection location. The third sum of the accuracy of clean multi-source satellite remote sensing data from all remote sensing satellites at each acquisition location:

[0074] ;

[0075] In the above formula, Indicates the first The third sum of the accuracy of clean multi-source satellite remote sensing data from all remote sensing satellites at each acquisition location. Indicates the first The remote sensing satellite in the The accuracy of the clean multi-source satellite remote sensing data obtained from each acquisition location after error correction. This indicates the number of remote sensing satellites.

[0076] Furthermore, in this embodiment of the invention, the clean multi-source satellite remote sensing data from each acquisition location and their accuracy are combined to perform final data fusion, obtaining the final satellite remote sensing data for each acquisition location within the area to be inverted. This embodiment of the invention uses the first... Taking the first collection location as an example, the following formula is used to calculate the first collection location. Final satellite remote sensing data from each acquisition location:

[0077] ;

[0078] In the above formula, Indicates the first The final satellite remote sensing data from each acquisition location. Indicates the first The third sum of the accuracy of clean multi-source satellite remote sensing data from all remote sensing satellites at each acquisition location. Indicates the first The remote sensing satellite in the The accuracy of the clean multi-source satellite remote sensing data obtained from each acquisition location after error correction. This indicates the number of remote sensing satellites. Indicates the first The remote sensing satellite in the Pure multi-source satellite remote sensing data from each acquisition location.

[0079] Furthermore, the topographic elevation model of the area to be inverted is obtained by simulating the final satellite remote sensing data from each acquisition location.

[0080] This invention, based on the dynamic positional changes of each remote sensing satellite during electromagnetic wave transmission and reception, and combined with the spatial positional relationship between the satellites at the moment of electromagnetic wave transmission and reception and the relative positions of each acquisition location, can eliminate errors in multi-source satellite remote sensing data from different acquisition locations. This effectively removes interference caused by differences in satellite displacement, resulting in more accurate and cleaner multi-source satellite remote sensing data. Furthermore, the accuracy of the clean multi-source satellite remote sensing data from each acquisition location during the fusion process is evaluated. Finally, based on the accuracy and characteristics of the clean multi-source satellite remote sensing data itself, the optimal final satellite remote sensing data for each acquisition location is determined. In this way, not only can errors caused by displacement differences between different remote sensing satellites during the data acquisition stage be accurately eliminated, but also more reliable data sources can be selected through accuracy evaluation, removing low-accuracy data sources caused by external environmental interference. This improves the accuracy of satellite remote sensing data from each acquisition location, ultimately making the terrain elevation model inversion results more accurate and the final inversion results more ideal.

[0081] Example 2:

[0082] Corresponding to the terrain inversion method based on multi-source satellite remote sensing data provided in the above embodiments, and based on the same technical concept, this invention also provides a terrain inversion system based on multi-source satellite remote sensing data. This terrain inversion system is used to execute the above-described terrain inversion method based on multi-source satellite remote sensing data. Figure 4 This is a schematic diagram of the structure of a terrain inversion system based on multi-source satellite remote sensing data, provided in one embodiment of the present invention. Figure 4As shown, the terrain inversion system 400 based on multi-source satellite remote sensing data includes: an acquisition module 401, used to acquire multi-source satellite remote sensing data of each acquisition location in the area to be inverted through each remote sensing satellite; an elimination module 402, used to eliminate errors in the multi-source satellite remote sensing data of each acquisition location based on the positional changes of each remote sensing satellite in transmitting and receiving electromagnetic waves, and the relative position of each remote sensing satellite in transmitting and receiving electromagnetic waves with each acquisition location, to obtain clean multi-source satellite remote sensing data of each acquisition location; an evaluation module 403, used to evaluate the accuracy of the clean multi-source satellite remote sensing data of each acquisition location in the data fusion process based on the position of each remote sensing satellite in transmitting and receiving electromagnetic waves; and an inversion module 404, used to determine the final satellite remote sensing data of each acquisition location based on the clean multi-source satellite remote sensing data and accuracy of each acquisition location, and to invert the terrain elevation model of the area to be inverted based on the final satellite remote sensing data of each acquisition location.

[0083] It should be noted that the terrain inversion system based on multi-source satellite remote sensing data provided in this embodiment of the invention and the terrain inversion method based on multi-source satellite remote sensing data provided in this embodiment of the invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned terrain inversion method based on multi-source satellite remote sensing data, and has the same or similar beneficial effects. Repeated parts will not be described again.

[0084] Example 3:

[0085] Corresponding to the terrain inversion method based on multi-source satellite remote sensing data provided in the above embodiments, and based on the same technical concept, this invention also provides a terrain inversion system based on multi-source satellite remote sensing data. This terrain inversion system is used to execute the above-described terrain inversion method based on multi-source satellite remote sensing data. Figure 5 This is a schematic diagram of another terrain inversion system based on multi-source satellite remote sensing data provided in one embodiment of the present invention, as shown below. Figure 5 As shown. Terrain inversion systems based on multi-source satellite remote sensing data can vary significantly due to differences in configuration or performance. They may include one or more processors 501 and memory 502. Memory 502 stores computer programs that can run on processor 501. Processor 501 executes the programs stored in memory 502 to achieve the above... Figure 1 The various steps in the method embodiment are described. The memory 502 can be temporary or persistent storage. The application stored in the memory 502 may include one or more modules (not shown in the figures), each module may include a series of computer-executable instructions for the terrain inversion system based on multi-source satellite remote sensing data.

[0086] Furthermore, the processor 501 can be configured to communicate with the memory 502 and execute a series of computer-executable instructions stored in the memory 502 on the terrain inversion system based on multi-source satellite remote sensing data. The terrain inversion system based on multi-source satellite remote sensing data may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, and one or more keyboards 506.

[0087] Specifically, in this embodiment, the terrain inversion system based on multi-source satellite remote sensing data includes a processor, a communication interface, a memory, and a communication bus; wherein, the processor, communication interface, and memory communicate with each other via the bus; the memory is used to store computer programs; and the processor is used to execute the programs stored in the memory to achieve the above... Figure 1 The various steps in the method embodiments are the same as those in the above method embodiments, and have the same beneficial effects. To avoid repetition, the embodiments of the present invention will not be described again here.

[0088] It should be noted that the terrain inversion system based on multi-source satellite remote sensing data provided in this embodiment of the invention and the terrain inversion method based on multi-source satellite remote sensing data provided in this embodiment of the invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned terrain inversion method based on multi-source satellite remote sensing data, and has the same or similar beneficial effects. Repeated parts will not be described again.

[0089] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0091] This invention also provides a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figure 1 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods in the preceding method embodiments, and will not be repeated here.

[0092] The computer-readable storage media include read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for terrain inversion based on multi-source satellite remote sensing data, characterized in that, The application relates to a method for obtaining terrain elevation model of a region to be inversed. Multi-source satellite remote sensing data of each collection position in the region to be inversed is acquired by each remote sensing satellite; Error elimination is performed on the multi-source satellite remote sensing data of each collection position based on position changes of each remote sensing satellite when emitting and receiving electromagnetic waves and relative positions between positions of each remote sensing satellite when emitting and receiving electromagnetic waves and each collection position, so that pure multi-source satellite remote sensing data of each collection position is obtained; According to the positions of each remote sensing satellite when emitting and receiving electromagnetic waves, the accuracy of the pure multi-source satellite remote sensing data of each collection position in the data fusion process is evaluated; Based on the pure multi-source satellite remote sensing data of each collection position and the accuracy, final satellite remote sensing data of each collection position is determined, and a terrain elevation model of the region to be inversed is inversed according to the final satellite remote sensing data of each collection position; The accuracy is obtained by the following method: The collection position The maximum value of the pure multi-source satellite remote sensing data obtained under each remote sensing satellite is denoted as ; wherein, The collection position The minimum value of the pure multi-source satellite remote sensing data obtained under each remote sensing satellite is denoted as The collection position The minimum value of the pure multi-source satellite remote sensing data obtained under each remote sensing satellite is denoted as The detection difficulty of the pure multi-source satellite remote sensing data of each collection position is calculated by the following formula: ； In the above formula, represents the detection difficulty of the pure multi-source satellite remote sensing data of the th acquisition position under the th remote sensing satellite; represents the height of the th remote sensing satellite; represents the first sum of the absolute values of the differences between the pure multi-source satellite remote sensing data of the th acquisition position under the th remote sensing satellite and the pure multi-source satellite remote sensing data of other positions in the neighborhood range of the th acquisition position. The second sum of the reciprocal of the error degree is calculated, and the following formula is used: ； In the above formula, denotes the sum of the reciprocal of the error degree of the acquisition position under the remote sensing satellite; denotes the error degree of the acquisition position under the remote sensing satellite; denotes the number of remote sensing satellites; The representative data of each collection position is obtained by taking the reciprocal of the error degree as the reference of the pure multi-source satellite remote sensing data, and the following formula is used for calculation: ； In the above formula, representing the representative satellite remote sensing data of the acquisition position; representing the pure multi-source satellite remote sensing data after error elimination of the acquisition position under the remote sensing satellite; The following formula is used to calculate the first... The remote sensing satellite in the Data obtained at each location after error correction accuracy : ； In the above formula, exp represents an exponential function with the natural constant e as the base number; for performing inverse proportional normalization processing on the ratio of The final satellite remote sensing data is obtained by the following method: The third harmonic mean of the accuracy of the pure multi-source satellite remote sensing data of all remote sensing satellites at the i-th acquisition location is calculated using the following formula: ​ ； In the above formula, a third min value representing the accuracy of the pure multi-source satellite remote sensing data of all remote sensing satellites at the i-th acquisition position. a third min value representing the accuracy of the pure multi-source satellite remote sensing data of all remote sensing satellites at the i-th acquisition position. The pure multi-source satellite remote sensing data of each collection position and the accuracy are integrated to obtain the final satellite remote sensing data of each collection position in the region to be inverted. The final satellite remote sensing data of the first collection position is calculated by the following formula: ； In the above formula, represent the final satellite remote sensing data of the first acquisition position.

2. The method according to claim 1, wherein, The error elimination on the multi-source satellite remote sensing data of each collection position based on the position changes of each remote sensing satellite when emitting and receiving electromagnetic waves and the relative positions between the positions of each remote sensing satellite when emitting and receiving electromagnetic waves and each collection position includes: The first longitude and latitude information of the satellite foot point of each remote sensing satellite when emitting electromagnetic waves and the first distance between the satellite foot point and the ground level reference line, the second longitude and latitude information of the satellite foot point of each remote sensing satellite when receiving electromagnetic waves and the second distance between the satellite foot point and the ground level reference line are determined; The third distance between each collection position and the position of each remote sensing satellite when emitting electromagnetic waves is determined according to the first longitude and latitude information, the first distance, the third longitude and latitude information of each collection position and the multi-source satellite remote sensing data, and the fourth distance between each collection position and the position of each remote sensing satellite when emitting electromagnetic waves is determined according to the second longitude and latitude information, the second distance, the third longitude and latitude information of each collection position and the multi-source satellite remote sensing data; The distance between each collection position and each remote sensing satellite for a round trip is determined according to the time difference between the emission and reception of electromagnetic waves of each remote sensing satellite and the propagation speed of electromagnetic waves; Based on the third distance, the fourth distance and the distance, the error degree of the multi-source satellite remote sensing data of each collection position after error elimination is determined, the multi-source satellite remote sensing data is changed by unit and the corresponding error degree is calculated until the error degree is no longer reduced, and the multi-source satellite remote sensing data corresponding to the minimum error degree is selected as the pure multi-source satellite remote sensing data of each collection position.

3. The method according to claim 2, wherein, The third distance between each collection position and the position of each remote sensing satellite when emitting electromagnetic waves is determined according to the first latitude and longitude information, the first distance, the third latitude and longitude information of each collection position, and the multi-source satellite remote sensing data. A first difference between corresponding longitude coordinates and latitude coordinates in the first latitude and longitude information and the third latitude and longitude information, and a second difference between the multi-source satellite remote sensing data and the first distance are determined. The third distance is determined based on the first difference and the second difference.

4. The method according to claim 2, wherein, The fourth distance between each collection position and the position of each remote sensing satellite when emitting electromagnetic waves is determined according to the second latitude and longitude information, the second distance, the third latitude and longitude information of each collection position, and the multi-source satellite remote sensing data. A third difference between corresponding longitude coordinates and latitude coordinates in the second latitude and longitude information and the third latitude and longitude information, and a fourth difference between the multi-source satellite remote sensing data and the first distance are determined. The fourth distance is determined based on the third difference and the fourth difference. 5.The method of claim 2, wherein, The accuracy of the pure multi-source satellite remote sensing data of each collection position in the data fusion process is evaluated according to the positions of each remote sensing satellite when emitting electromagnetic waves and receiving electromagnetic waves. The average height of each remote sensing satellite is determined according to the first distance and the second distance. The first sum of absolute values of differences between the pure multi-source satellite remote sensing data of each collection position and the pure multi-source satellite remote sensing data of other positions within the neighborhood range of each collection position is determined. The range of the pure multi-source satellite remote sensing data of each collection position under each remote sensing satellite is determined. The detection difficulty of the pure multi-source satellite remote sensing data of each collection position is determined according to the average height, the first sum, and the range, and the detection difficulty is inversely proportional to the data reference value of the pure multi-source satellite remote sensing data. The accuracy is evaluated according to the pure multi-source satellite remote sensing data, the error degree, and the detection difficulty of each remote sensing satellite at each collection position. 6.A terrain inversion system based on multi-source satellite remote sensing data, characterized in that, The method comprises the following steps: The multi-source satellite remote sensing data of each collection position in the to-be-inverted region is acquired by each remote sensing satellite. The error of the multi-source satellite remote sensing data of each collection position is eliminated based on the position change of each remote sensing satellite when emitting electromagnetic waves and receiving electromagnetic waves, and the relative position between the position of each remote sensing satellite when emitting electromagnetic waves and receiving electromagnetic waves and each collection position, to obtain the pure multi-source satellite remote sensing data of each collection position. The accuracy of the pure multi-source satellite remote sensing data of each collection position in the data fusion process is evaluated according to the position of each remote sensing satellite when emitting electromagnetic waves and receiving electromagnetic waves. The detection difficulty of the pure multi-source satellite remote sensing data of each collection position is calculated using the following formula: Collection location The range of the pure multi-source satellite remote sensing data obtained from various remote sensing satellites is denoted as . ;in, Indicates the data collection location The maximum value of clean multi-source satellite remote sensing data obtained from various remote sensing satellites. Indicates the data collection location The minimum value of pure multi-source satellite remote sensing data obtained from various remote sensing satellites; The second sum of the reciprocal of the error degree is calculated using the following formula: ； In the above formula, represents the detection difficulty of pure multi-source satellite remote sensing data under the first remote sensing satellite at the first acquisition position; represents the height of the first remote sensing satellite; represents the detection difficulty of pure multi-source satellite remote sensing data under the first remote sensing satellite at the first acquisition position; acquisition position and the pure multi-source satellite remote sensing data of other positions within the neighborhood range of the first acquisition position. ​ ； In the above formula, denotes the sum of the inverse of the error degree of the th acquisition position under the th remote sensing satellite; denotes the error degree of the th acquisition position under the th remote sensing satellite; denotes the number of remote sensing satellites; The representative data at the collection position is obtained by taking the inverse of the error degree as the reference of each pure multi-source satellite remote sensing data, and is specifically calculated by the following formula: ； In the above formula, represent the representative satellite remote sensing data of the th acquisition location; represent the error-corrected pure multi-source satellite remote sensing data of the th acquisition location under the th remote sensing satellite; The following formula is used to calculate the first... The remote sensing satellite in the Data obtained at each location after error correction accuracy : ； In the above formula, exp represents an exponential function with the natural constant e as the base number; for performing inverse proportional normalization processing on the ratio of The inversion module is configured to determine the final satellite remote sensing data of each collection position based on the pure multi-source satellite remote sensing data of each collection position and the accuracy, and to invert the terrain elevation model of the to-be-inverted region according to the final satellite remote sensing data of each collection position; The final satellite remote sensing data is obtained by: The third harmonic mean of the accuracy of the pure multi-source satellite remote sensing data of all remote sensing satellites at the i-th acquisition location is calculated using the following formula: The third harmonic mean of the accuracy of the pure multi-source satellite remote sensing data of all remote sensing satellites at the i-th acquisition location is calculated using the following formula: ； In the above formula, a third min value representing the accuracy of the pure multi-source satellite remote sensing data of all remote sensing satellites at the i-th acquisition position. a third min value representing the accuracy of the pure multi-source satellite remote sensing data of all remote sensing satellites at the i-th acquisition position. The pure multi-source satellite remote sensing data of each collection position and the accuracy are integrated to obtain the final satellite remote sensing data of each collection position in the region to be inverted. The final satellite remote sensing data of the first collection position is calculated by the following formula: ； In the above formula, represent the final satellite remote sensing data of the first acquisition position. 7.A terrain inversion system based on multi-source satellite remote sensing data, characterized in that, The method comprises the following steps: A processor and a memory; wherein the memory is used to store a computer program which can be run on the processor; The processor is used to execute the program stored on the memory, and realize the steps of the terrain inversion method based on multi-source satellite remote sensing data according to any one of claims 1-5.

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