Three-dimensional terrain high-low precision data fusion method and system

Abstract

The invention relates to a three-dimensional terrain high-low precision data fusion method and system, and the method specifically comprises the steps: collecting the three-dimensional coordinate data of each terrain, the three-dimensional coordinate data comprise high-precision terrain data and low-precision terrain data, and carrying out the normalization processing; generating a low-precision topographic map according to the low-precision topographic data; combining high-precision grids through grid embedding to obtain high-precision topographic data, and extracting a high-precision topographic boundary by using a boundary extraction algorithm based on neighborhood analysis; extracting each pixel point and elevation of the intersection of the high-precision terrain boundary and the high-precision terrain data to construct a high-precision point set; constructing a buffer area boundary by using the high-precision terrain boundary and a preset buffer distance; according to the buffer boundary and the low-precision terrain, obtaining each vertex and elevation on the buffer boundary to construct a low-precision point set; and performing bilinear interpolation by using the high-precision point set and the low-precision point set to obtain a slope grid map.

Description

Technical Field

[0001] The present invention relates to the field of geographic information system technology, and in particular to a method and system for fusing high- and low-precision data of three-dimensional terrain. Background Art

[0002] With the continuous advancement of modern surveying and mapping technology, the means of obtaining three-dimensional terrain data are becoming increasingly diverse, such as satellite remote sensing, aerial photogrammetry, ground laser scanning, drone mapping, etc. Each method has its own unique advantages and limitations, and the acquired data differ in accuracy, resolution, coverage, etc., which has prompted the need to fuse multi-source data to give full play to their respective advantages.

[0003] In the power industry, a common user scenario for terrain data is that users have terrain data of two accuracies: one is a large-scale low-precision terrain, and the other is a strip-shaped high-precision terrain about 200m around the transmission line. When terrains of different accuracies from different sources are displayed at the same time, in order to ensure the continuity of the terrain data, the natural undulations of the terrain, and better display of the three-dimensional scene, it is necessary to fuse data of different accuracies from different sources.

[0004] Traditional terrain data processing methods often suffer from problems such as loss of accuracy and data inconsistency when fusing high- and low-precision data. For example, a simple weighted average method may not fully consider the characteristics and spatial distribution differences of high- and low-precision data, resulting in the fusion results being difficult to achieve ideal results in terms of accuracy and authenticity. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes a method and system for fusing high- and low-precision data of three-dimensional terrain.

[0006] The technical solution of the present invention is as follows:

[0007] On the one hand, the present invention proposes a method for fusing high-precision and low-precision data of three-dimensional terrain, and the specific steps include:

[0008] Collecting three-dimensional coordinate data of various terrains, the three-dimensional coordinate data including high-precision terrain data and low-precision terrain data, and performing normalization processing;

[0009] Generate low-precision terrain maps based on low-precision terrain data;

[0010] High-precision terrain data is obtained by merging high-precision rasters through raster mosaic, and the high-precision terrain boundary is extracted using a boundary extraction algorithm based on neighborhood analysis; each pixel point and elevation where the high-precision terrain boundary intersects with the high-precision terrain data is extracted to construct a high-precision point set;

[0011] Construct buffer boundaries using high-precision terrain boundaries and preset buffer distances;

[0012] According to the buffer boundary and low-precision terrain, obtain each vertex and elevation on the buffer boundary to construct a low-precision point set;

[0013] The slope grid map is obtained by bilinear interpolation using high-precision point sets and low-precision point sets;

[0014] Merge high-precision terrain data and slope raster maps through raster mosaic to obtain high-precision terrain data with buffer zones;

[0015] The terrain is generated according to the high-precision terrain data with a buffer zone, and the elevation information in the area corresponding to the low-precision terrain A is covered to obtain a final terrain map that merges the high-precision and low-precision terrain data.

[0016] As a preferred implementation, the step of extracting high-precision terrain boundaries using a boundary extraction algorithm based on neighborhood analysis is specifically as follows:

[0017] By calculating whether the difference between the normal vector and curvature of each point in the high-precision terrain data and the adjacent points meets the threshold, it is determined whether the target point is a boundary point, where:

[0018] The target point and its surrounding points are extracted to construct a target point set. The normal vector is calculated as follows:

[0019] Construct the covariance matrix C1 about the target point, specifically:

[0020]

[0021] Where n is the number of target points, p' j is the offset of the target point relative to the mean of the target point set, is the transpose of the offset;

[0022] Solve the eigenvalues ​​of the covariance matrix and their corresponding eigenvectors, where the eigenvector corresponding to the minimum eigenvalue is the normal vector of the target point;

[0023] The curvature is calculated as follows:

[0024] Calculate the centroid c of the target point set. The specific calculation formula is as follows:

[0025]

[0026] Where n is the number of target points, p i is the target point;

[0027] Construct the covariance matrix C2, specifically:

[0028]

[0029] In the formula, T is the transpose;

[0030] Perform eigenvalue decomposition on the covariance matrix C2 to obtain eigenvalues ​​λ1 and λ2, and calculate the curvature based on the eigenvalues ​​λ1 and λ2, specifically:

[0031]

[0032] Where K is the curvature of the target point.

[0033] As a preferred implementation, the step of using a high-precision point set and a low-precision point set to perform bilinear interpolation to obtain a slope grid map is specifically as follows:

[0034] Select any four neighboring point elevation values ​​to calculate the elevation value of the grid cell. First, perform linear interpolation in the x direction to obtain the elevation values ​​of the two intermediate points:

[0035]

[0036] Where (x, y) is the coordinate of the grid cell, (x1, y1), (x2, y1), (x1, y2) and (x2, y2) are the coordinates of the four neighboring points, and f(x1, y1), f(x2, y1), f(x1, y2) and f(x2, y2) are the elevation values ​​of the four neighboring points.

[0037] Then perform linear interpolation on the two intermediate points in the y direction to obtain the elevation value of the grid cell:

[0038]

[0039] The calculated grid cell elevation values ​​are assigned to the corresponding grid cells, the blank grid dataset is updated, and the slope grid map is obtained.

[0040] As a preferred implementation, the raster mosaic uses ArcGIS and ENVI tools.

[0041] On the other hand, the present invention provides a high-precision and low-precision data fusion system for three-dimensional terrain, and the specific steps include:

[0042] A three-dimensional data acquisition module collects three-dimensional coordinate data of various terrains, wherein the three-dimensional coordinate data includes high-precision terrain data and low-precision terrain data, and performs normalization processing;

[0043] A low-precision terrain generation module generates a low-precision terrain map based on low-precision terrain data;

[0044] The high-precision terrain boundary and point set generation module obtains high-precision terrain data by merging high-precision rasters through raster mosaicking, and uses a boundary extraction algorithm based on neighborhood analysis to extract high-precision terrain boundaries; extracts each pixel point and elevation where the high-precision terrain boundary intersects with the high-precision terrain data to construct a high-precision point set;

[0045] Buffer construction module, which constructs buffer boundaries using high-precision terrain boundaries and preset buffer distances;

[0046] The low-precision point set generation module obtains each vertex and elevation on the buffer boundary to construct a low-precision point set based on the buffer boundary and low-precision terrain;

[0047] The slope grid map construction module uses high-precision point sets and low-precision point sets to perform bilinear interpolation to obtain the slope grid map;

[0048] High-precision terrain data update module, which merges high-precision terrain data and slope raster map through raster mosaic to obtain high-precision terrain data with buffer zone;

[0049] The high- and low-precision map fusion module generates terrain based on the high-precision terrain data with a buffer zone, covers the elevation information in the area corresponding to the low-precision terrain A, and obtains the final terrain map that merges the high- and low-precision terrain data.

[0050] As a preferred implementation, the step of extracting high-precision terrain boundaries using a boundary extraction algorithm based on neighborhood analysis is specifically as follows:

[0051] By calculating whether the difference between the normal vector and curvature of each point in the high-precision terrain data and the adjacent points meets the threshold, it is determined whether the target point is a boundary point, where:

[0052] The target point and its surrounding points are extracted to construct a target point set. The normal vector is calculated as follows:

[0053] Construct the covariance matrix C1 about the target point, specifically:

[0054]

[0055] Where n is the number of target points, p' j is the offset of the target point relative to the mean of the target point set, is the transpose of the offset;

[0056] Solve the eigenvalues ​​of the covariance matrix and their corresponding eigenvectors, where the eigenvector corresponding to the minimum eigenvalue is the normal vector of the target point;

[0057] The curvature is calculated as follows:

[0058] Calculate the centroid c of the target point set. The specific calculation formula is as follows:

[0059]

[0060] Where n is the number of target points, p i is the target point;

[0061] Construct the covariance matrix C2, specifically:

[0062]

[0063] In the formula, T is the transpose;

[0064] Perform eigenvalue decomposition on the covariance matrix C2 to obtain eigenvalues ​​λ1 and λ2, and calculate the curvature based on the eigenvalues ​​λ1 and λ2, specifically:

[0065]

[0066] Where K is the curvature of the target point.

[0067] As a preferred implementation, the step of using a high-precision point set and a low-precision point set to perform bilinear interpolation to obtain a slope grid map is specifically as follows:

[0068] Select any four neighboring point elevation values ​​to calculate the elevation value of the grid cell. First, perform linear interpolation in the x direction to obtain the elevation values ​​of the two intermediate points:

[0069]

[0070] Where (x, y) is the coordinate of the grid cell, (x1, y1), (x2, y1), (x1, y2) and (x2, y2) are the coordinates of the four neighboring points, f9x1, y1), f(x2, y1), f(x1, y2) and f(x2, y2) are the elevation values ​​of the four neighboring points;

[0071] Then perform linear interpolation on the two intermediate points in the y direction to obtain the elevation value of the grid cell:

[0072]

[0073] The calculated grid cell elevation values ​​are assigned to the corresponding grid cells, the blank grid dataset is updated, and the slope grid map is obtained.

[0074] As a preferred implementation, the raster mosaic uses ArcGIS and ENVI tools.

[0075] On the other hand, the present invention proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for fusing high-precision and low-precision three-dimensional terrains as described in any embodiment of the present invention is implemented.

[0076] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for fusing high-precision and low-precision three-dimensional terrains as described in any embodiment of the present invention.

[0077] The present invention has the following beneficial effects:

[0078] 1. The present invention combines the precise details of high-precision terrain data with the large-area coverage of low-precision terrain data, so that the final topographic map generated has high-precision terrain information in local areas, while maintaining a wider coverage range as a whole, making up for the shortcomings of single-precision terrain data and greatly improving the overall accuracy and integrity of terrain data.

[0079] 2. The present invention constructs a high-precision point set by extracting the pixel points and elevations where the high-precision terrain boundary intersects with the high-precision terrain data, which can capture the subtle features and changes of the terrain, such as hillocks, depressions, steep slopes, etc. Then, the slope raster map is obtained by bilinear interpolation, which further refines the transition area of ​​the terrain, making the expression of the terrain more realistic and accurate, and providing richer terrain detail information for various applications.

[0080] 3. When collecting three-dimensional coordinate data of various terrains, the present invention uniformly processes data from different sources and with different precisions, so that the data format and coordinate system remain consistent, which is convenient for subsequent fusion and analysis operations. This avoids data confusion and errors caused by inconsistent data formats or inconsistent coordinate systems, and improves data consistency and compatibility.

[0081] 4. The present invention uses the high-precision terrain boundary to construct the buffer boundary, and obtains the low-precision point set based on the buffer boundary, so that the high-precision and low-precision data are effectively fused within the buffer range. This buffer-based fusion method can establish a smooth transition between high-precision and low-precision data, reduce the discontinuity and mutation phenomenon at the data splicing point, and enhance the overall consistency of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 It is a schematic diagram of the steps of the present invention. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0085] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0086] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0087] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0088] Embodiment 1:

[0089] See also Figure 1 , a method for fusing high- and low-precision data of three-dimensional terrain, the specific steps include:

[0090] Collecting three-dimensional coordinate data of various terrains, the three-dimensional coordinate data including high-precision terrain data and low-precision terrain data, and performing normalization processing;

[0091] Generate low-precision terrain maps based on low-precision terrain data;

[0092] High-precision terrain data is obtained by merging high-precision rasters through raster mosaic, and the high-precision terrain boundary is extracted using a boundary extraction algorithm based on neighborhood analysis; each pixel point and elevation where the high-precision terrain boundary intersects with the high-precision terrain data is extracted to construct a high-precision point set;

[0093] Construct buffer boundaries using high-precision terrain boundaries and preset buffer distances;

[0094] According to the buffer boundary and low-precision terrain, obtain each vertex and elevation on the buffer boundary to construct a low-precision point set;

[0095] The slope grid map is obtained by bilinear interpolation using high-precision point sets and low-precision point sets;

[0096] Merge high-precision terrain data and slope raster maps through raster mosaic to obtain high-precision terrain data with buffer zones;

[0097] The terrain is generated according to the high-precision terrain data with a buffer zone, and the elevation information in the area corresponding to the low-precision terrain A is covered to obtain a final terrain map that merges the high-precision and low-precision terrain data.

[0098] As a preferred implementation of this embodiment, the step of extracting high-precision terrain boundaries using a boundary extraction algorithm based on neighborhood analysis is specifically as follows:

[0099] By calculating whether the difference between the normal vector and curvature of each point in the high-precision terrain data and the adjacent points meets the threshold, it is determined whether the target point is a boundary point, where:

[0100] The target point and its surrounding points are extracted to construct a target point set. The normal vector is calculated as follows:

[0101] Construct the covariance matrix C1 about the target point, specifically:

[0102]

[0103] Where n is the number of target points, p' j is the offset of the target point relative to the mean of the target point set, is the transpose of the offset;

[0104] Solve the eigenvalues ​​of the covariance matrix and their corresponding eigenvectors, where the eigenvector corresponding to the minimum eigenvalue is the normal vector of the target point;

[0105] The curvature is calculated as follows:

[0106] Calculate the centroid c of the target point set. The specific calculation formula is as follows:

[0107]

[0108] Where n is the number of target points, p i is the target point;

[0109] Construct the covariance matrix C2, specifically:

[0110]

[0111] In the formula, T is the transpose;

[0112] Perform eigenvalue decomposition on the covariance matrix C2 to obtain eigenvalues ​​λ1 and λ2, and calculate the curvature based on the eigenvalues ​​λ1 and λ2, specifically:

[0113]

[0114] Where K is the curvature of the target point.

[0115] In this embodiment, during the calculation of the normal vector, the target point set mean calculation formula is as follows:

[0116]

[0117] In the formula, x j ,y j and z j are the three-dimensional coordinate values ​​of each point in the target point set.

[0118] As a preferred implementation of this embodiment, the step of obtaining a slope grid map by performing bilinear interpolation using a high-precision point set and a low-precision point set is specifically as follows:

[0119] Select any four neighboring point elevation values ​​to calculate the elevation value of the grid cell. First, perform linear interpolation in the x direction to obtain the elevation values ​​of the two intermediate points:

[0120]

[0121] Where (x, y) is the coordinate of the grid cell, (x1, y1), (x2, y1), (x1, y2) and (x2, y2) are the coordinates of the four neighboring points, and f(x1, y1), f(x2, y1), f(x1, y2) and f(x2, y2) are the elevation values ​​of the four neighboring points.

[0122] Then perform linear interpolation on the two intermediate points in the y direction to obtain the elevation value of the grid cell:

[0123]

[0124] The calculated grid cell elevation values ​​are assigned to the corresponding grid cells, the blank grid dataset is updated, and the slope grid map is obtained.

[0125] In this embodiment, the specific process of this step is as follows:

[0126] Determine the grid resolution: Determine the appropriate grid resolution based on the required accuracy of the slope grid map and application requirements. A smaller grid resolution can provide more detailed terrain information, but will increase the amount of data and computational cost; a larger grid resolution will simplify the terrain representation, but may lose some details.

[0127] Create a blank raster: Create a blank raster dataset in the study area according to the determined raster resolution, and initialize the value of each raster cell to the default value (such as zero or an invalid value).

[0128] Traverse the grid cells: For each grid cell, determine its location in geographic space and find the corresponding high-precision point set and neighboring points in the low-precision point set.

[0129] Select interpolation points: Select four adjacent points from the high-precision point set and the low-precision point set respectively. These four points form a quadrilateral, and the grid cell is located inside the quadrilateral. Usually, the four points closest to the center of the grid cell are selected, but other selection methods can also be used according to specific circumstances.

[0130] Perform bilinear interpolation calculation: select any four adjacent point elevation values ​​to calculate the elevation value of the grid cell, first perform linear interpolation in the x direction to obtain the elevation values ​​of the two intermediate points:

[0131]

[0132] Where (x, y) is the coordinate of the grid cell, (x1, y1), (x2, y1), (x1, y2) and (x2, y2) are the coordinates of the four neighboring points, and f(x1, y1), f(x2, y1), f(x1, y2) and f(x2, y2) are the elevation values ​​of the four neighboring points.

[0133] Then perform linear interpolation on the two intermediate points in the y direction to obtain the elevation value of the grid cell:

[0134]

[0135] Assignment and update: Assign the calculated grid cell elevation value to the corresponding grid cell and update the blank grid dataset.

[0136] Post-processing: Perform post-processing on the generated slope raster map, such as smoothing and edge enhancement, to improve the image quality and visualization effect. You can also calculate terrain parameters such as slope and aspect as needed, and add the results to the raster map to more comprehensively describe the characteristics of the slope.

[0137] Accuracy assessment: Use known high-precision terrain data or field measurement data as a reference to assess the accuracy of the generated slope raster map. Statistical indicators such as root mean square error and mean absolute error can be calculated to measure the difference between the interpolation result and the true value.

[0138] Verification and adjustment: According to the results of the accuracy assessment, the interpolation method, parameters or data are verified and adjusted. If the accuracy does not meet the requirements, you can try to change the selection method of interpolation points, increase the density of high-precision point sets or optimize the data preprocessing process to improve the accuracy of the slope grid map.

[0139] As a preferred implementation of this embodiment, the raster mosaic uses ArcGIS and ENVI tools.

[0140] Embodiment 2:

[0141] A three-dimensional terrain high- and low-precision data fusion system, the specific steps include:

[0142] A three-dimensional data acquisition module collects three-dimensional coordinate data of various terrains, wherein the three-dimensional coordinate data includes high-precision terrain data and low-precision terrain data, and performs normalization processing;

[0143] A low-precision terrain generation module generates a low-precision terrain map based on low-precision terrain data;

[0144] The high-precision terrain boundary and point set generation module obtains high-precision terrain data by merging high-precision rasters through raster mosaicking, and uses a boundary extraction algorithm based on neighborhood analysis to extract high-precision terrain boundaries; extracts each pixel point and elevation where the high-precision terrain boundary intersects with the high-precision terrain data to construct a high-precision point set;

[0145] Buffer construction module, which constructs buffer boundaries using high-precision terrain boundaries and preset buffer distances;

[0146] The low-precision point set generation module obtains each vertex and elevation on the buffer boundary to construct a low-precision point set based on the buffer boundary and low-precision terrain;

[0147] The slope grid map construction module uses high-precision point sets and low-precision point sets to perform bilinear interpolation to obtain the slope grid map;

[0148] High-precision terrain data update module, which merges high-precision terrain data and slope raster map through raster mosaic to obtain high-precision terrain data with buffer zone;

[0149] The high- and low-precision map fusion module generates terrain based on the high-precision terrain data with a buffer zone, covers the elevation information in the area corresponding to the low-precision terrain A, and obtains the final terrain map that merges the high- and low-precision terrain data.

[0150] As a preferred implementation of this embodiment, the step of extracting high-precision terrain boundaries using a boundary extraction algorithm based on neighborhood analysis is specifically as follows:

[0151] By calculating whether the difference between the normal vector and curvature of each point in the high-precision terrain data and the adjacent points meets the threshold, it is determined whether the target point is a boundary point, where:

[0152] The target point and its surrounding points are extracted to construct a target point set. The normal vector is calculated as follows:

[0153] Construct the covariance matrix C1 about the target point, specifically:

[0154]

[0155] Where n is the number of target points, p' j is the offset of the target point relative to the mean of the target point set, is the transpose of the offset;

[0156] Solve the eigenvalues ​​of the covariance matrix and their corresponding eigenvectors, where the eigenvector corresponding to the minimum eigenvalue is the normal vector of the target point;

[0157] The curvature is calculated as follows:

[0158] Calculate the centroid c of the target point set. The specific calculation formula is as follows:

[0159]

[0160] Where n is the number of target points, p i is the target point;

[0161] Construct the covariance matrix C2, specifically:

[0162]

[0163] In the formula, T is the transpose;

[0164] Perform eigenvalue decomposition on the covariance matrix C2 to obtain eigenvalues ​​λ1 and λ2, and calculate the curvature based on the eigenvalues ​​λ1 and λ2, specifically:

[0165]

[0166] Where K is the curvature of the target point.

[0167] As a preferred implementation of this embodiment, the step of obtaining a slope grid map by performing bilinear interpolation using a high-precision point set and a low-precision point set is specifically as follows:

[0168] Select any four neighboring point elevation values ​​to calculate the elevation value of the grid cell. First, perform linear interpolation in the x direction to obtain the elevation values ​​of the two intermediate points:

[0169]

[0170] Where (x, y) is the coordinate of the grid cell, (x1, y1), (x2, y1), (x1, y2) and (x2, y2) are the coordinates of the four neighboring points, f9x1, y1), f(x2, y1), f(x1, y2) and f(x2, y2) are the elevation values ​​of the four neighboring points;

[0171] Then perform linear interpolation on the two intermediate points in the y direction to obtain the elevation value of the grid cell:

[0172]

[0173] The calculated grid cell elevation values ​​are assigned to the corresponding grid cells, the blank grid dataset is updated, and the slope grid map is obtained.

[0174] As a preferred implementation of this embodiment, the raster mosaic uses ArcGIS and ENVI tools.

[0175] Embodiment three:

[0176] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a method for fusing high-precision and low-precision three-dimensional terrains as described in any embodiment of the present invention is implemented.

[0177] Embodiment 4:

[0178] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for fusing high-precision and low-precision three-dimensional terrains as described in any embodiment of the present invention.

[0179] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for fusing high- and low-precision data of three-dimensional terrain, characterized in that: The specific steps include: Collecting three-dimensional coordinate data of various terrains, the three-dimensional coordinate data including high-precision terrain data and low-precision terrain data, and performing normalization processing; Generate low-precision terrain maps based on low-precision terrain data; High-precision terrain data is obtained by merging high-precision rasters through raster mosaic, and the high-precision terrain boundary is extracted using a boundary extraction algorithm based on neighborhood analysis; each pixel point and elevation where the high-precision terrain boundary intersects with the high-precision terrain data is extracted to construct a high-precision point set; Construct buffer boundaries using high-precision terrain boundaries and preset buffer distances; According to the buffer boundary and low-precision terrain, obtain each vertex and elevation on the buffer boundary to construct a low-precision point set; The slope grid map is obtained by bilinear interpolation using high-precision point sets and low-precision point sets; Merge high-precision terrain data and slope raster maps through raster mosaic to obtain high-precision terrain data with buffer zones; The terrain is generated according to the high-precision terrain data with a buffer zone, and the elevation information in the area corresponding to the low-precision terrain A is covered to obtain a final terrain map that merges the high-precision and low-precision terrain data.

2. A method for fusion of high and low precision three-dimensional terrain data according to claim 1, characterized in that: The steps of extracting high-precision terrain boundaries using a boundary extraction algorithm based on neighborhood analysis are specifically as follows: By calculating whether the difference between the normal vector and curvature of each point in the high-precision terrain data and the adjacent points meets the threshold, it is determined whether the target point is a boundary point, where: The target point and its surrounding points are extracted to construct a target point set. The normal vector is calculated as follows: Construct the covariance matrix C1 about the target point, specifically: Where n is the number of target points, p' j is the offset of the target point relative to the mean of the target point set, is the transpose of the offset; Solve the eigenvalues ​​of the covariance matrix and their corresponding eigenvectors, where the eigenvector corresponding to the minimum eigenvalue is the normal vector of the target point; The curvature is calculated as follows: Calculate the centroid c of the target point set. The specific calculation formula is as follows: Where n is the number of target points, p i is the target point; Construct the covariance matrix C2, specifically: In the formula, T is the transpose; Perform eigenvalue decomposition on the covariance matrix C2 to obtain eigenvalues ​​λ1 and λ2, and calculate the curvature based on the eigenvalues ​​λ1 and λ2, specifically: Where K is the curvature of the target point.

3. The method for fusion of high and low precision three-dimensional terrain data according to claim 1, characterized in that: The step of using the high-precision point set and the low-precision point set to perform bilinear interpolation to obtain the slope grid map is specifically as follows: Select any four neighboring point elevation values ​​to calculate the elevation value of the grid cell. First, perform linear interpolation in the x direction to obtain the elevation values ​​of the two intermediate points: Where (x, y) is the coordinate of the grid cell, (x1, y1), (x2, y1), (x1, y2) and (x2, y2) are the coordinates of the four neighboring points, and f(x1, y1), f(x2, y1), f(x1, y2) and f(x2, y2) are the elevation values ​​of the four neighboring points. Then perform linear interpolation on the two intermediate points in the y direction to obtain the elevation value of the grid cell: The calculated grid cell elevation values ​​are assigned to the corresponding grid cells, the blank grid dataset is updated, and the slope grid map is obtained.

4. The method for fusion of high and low precision three-dimensional terrain data according to claim 1, characterized in that: The raster mosaic was performed using ArcGIS and ENVI tools.

5. A three-dimensional terrain high- and low-precision data fusion system, characterized in that: The specific steps include: A three-dimensional data acquisition module collects three-dimensional coordinate data of various terrains, wherein the three-dimensional coordinate data includes high-precision terrain data and low-precision terrain data, and performs normalization processing; A low-precision terrain generation module generates a low-precision terrain map based on low-precision terrain data; The high-precision terrain boundary and point set generation module obtains high-precision terrain data by merging high-precision rasters through raster mosaicking, and uses a boundary extraction algorithm based on neighborhood analysis to extract high-precision terrain boundaries; extracts each pixel point and elevation where the high-precision terrain boundary intersects with the high-precision terrain data to construct a high-precision point set; Buffer construction module, which constructs buffer boundaries using high-precision terrain boundaries and preset buffer distances; The low-precision point set generation module obtains each vertex and elevation on the buffer boundary to construct a low-precision point set based on the buffer boundary and low-precision terrain; The slope grid map construction module uses high-precision point sets and low-precision point sets to perform bilinear interpolation to obtain the slope grid map; The high-precision terrain data update module merges the high-precision terrain data and the slope raster map through raster mosaic to obtain high-precision terrain data with a buffer zone; The high- and low-precision map fusion module generates terrain based on the high-precision terrain data with a buffer zone, covers the elevation information in the area corresponding to the low-precision terrain A, and obtains the final terrain map that merges the high- and low-precision terrain data.

6. A three-dimensional terrain high- and low-precision data fusion system according to claim 5, characterized in that: The steps of extracting high-precision terrain boundaries using a boundary extraction algorithm based on neighborhood analysis are specifically as follows: By calculating whether the difference between the normal vector and curvature of each point in the high-precision terrain data and the adjacent points meets the threshold, it is determined whether the target point is a boundary point, where: The target point and its surrounding points are extracted to construct a target point set. The normal vector is calculated as follows: Construct the covariance matrix C1 about the target point, specifically: Where n is the number of target points, p' j is the offset of the target point relative to the mean of the target point set, is the transpose of the offset; Solve the eigenvalues ​​of the covariance matrix and their corresponding eigenvectors, where the eigenvector corresponding to the minimum eigenvalue is the normal vector of the target point; The curvature is calculated as follows: Calculate the centroid c of the target point set. The specific calculation formula is as follows: Where n is the number of target points, p i is the target point; Construct the covariance matrix C2, specifically: In the formula, T is the transpose; Perform eigenvalue decomposition on the covariance matrix C2 to obtain eigenvalues ​​λ1 and λ2, and calculate the curvature based on the eigenvalues ​​λ1 and λ2, specifically: Where K is the curvature of the target point.

7. The high-precision and low-precision data fusion system of three-dimensional terrain according to claim 5 is characterized in that: The step of using the high-precision point set and the low-precision point set to perform bilinear interpolation to obtain the slope grid map is specifically as follows: Select any four neighboring point elevation values ​​to calculate the elevation value of the grid cell. First, perform linear interpolation in the x direction to obtain the elevation values ​​of the two intermediate points: Where (x, y) is the coordinate of the grid cell, (x1, y1), (x2, y1), (x1, y2) and (x2, y2) are the coordinates of the four neighboring points, and f(x1, y1), f(x2, y1), f(x1, y2) and f(x2, y2) are the elevation values ​​of the four neighboring points. Then perform linear interpolation on the two intermediate points in the y direction to obtain the elevation value of the grid cell: The calculated grid cell elevation values ​​are assigned to the corresponding grid cells, the blank grid dataset is updated, and the slope grid map is obtained.

8. The high-precision and low-precision data fusion system of three-dimensional terrain according to claim 5 is characterized in that: The raster mosaic was performed using ArcGIS and ENVI tools.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, a three-dimensional terrain high- and low-precision data fusion method as described in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a method for fusing high- and low-precision data of three-dimensional terrain as described in any one of claims 1 to 4 is implemented.

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