An automatic extraction method of elevation points of DEM spatial model

Abstract

The application belongs to the technical field of height data analysis, and provides a DEM space model height point automatic extraction method, the application carries out clustering on the height data of sampling points by setting different clustering radii, judges the terrain integrity of each clustering range, and divides subspaces; analyzes the reference value of different subspaces on the height interpolation of the projection position point, obtains the reference height value of the projection position point; then analyzes the distance interval condition of each height data in the same region in different subspaces, adjusts the coordinate position of the projection position point, and obtains the corrected DEM space model; finally, the difference condition between the water flow trend and the actual terrain in the corrected DEM space model is analyzed, the reference height value of part of the projection position points is adjusted, and the twice corrected DEM space model is obtained. The accuracy of the interpolation height points of different positions is improved by the method, and the accuracy of the DEM space model construction is improved.

Description

Technical Field

[0001] This invention relates to the field of elevation data analysis technology, and in particular to an automatic method for extracting elevation points from a DEM spatial model. Background Technology

[0002] In Digital Elevation Models (DEMs), ensuring the accuracy of elevation points is paramount. High-precision elevation data forms the basis of terrain analysis (such as slope, aspect, and watershed delineation), and errors in this data significantly propagate to all derived analysis results. Accurate elevation points more realistically reflect geomorphic details, ensuring the scientific rigor and reliability of applications such as hydrological modeling, flood simulation, engineering design, earthwork calculation, and disaster assessment, and preventing decision-making errors or resource waste due to deviations in the basic data. Therefore, pursuing higher accuracy for elevation points is a core requirement for enhancing the application value of DEMs.

[0003] Traditional methods use the Kriging algorithm to obtain interpolated elevation points by referencing global spatial data. However, the impact of using global data as a reference on individual terrain conditions is not clear enough, and the resulting interpolated elevation points do not accurately reflect the actual terrain. Summary of the Invention

[0004] To address the above technical problems, this invention provides an automatic method for extracting elevation points from a DEM spatial model.

[0005] According to the present invention, an automatic method for extracting elevation points from a DEM spatial model is provided, the method comprising:

[0006] Obtain the elevation data of the sampling points;

[0007] By setting different clustering radii, the elevation data is clustered to obtain the clustering ranges corresponding to different clustering radii;

[0008] By analyzing the occurrence of the boundary of each cluster range under different cluster radii and the difference in elevation data on both sides of each cluster range boundary, the terrain integrity of each cluster range is determined, and multiple subspaces are obtained.

[0009] Interpolation is performed on the subspace to obtain the projected position points and their initial elevation values;

[0010] Based on the integrity of the terrain and the projected area of ​​the subspace, the reference value of different subspaces for calculating the elevation interpolation of the projection location point is analyzed. Then, combined with the initial elevation value, the reference elevation value of the projection location point is obtained.

[0011] By analyzing the distance intervals of elevation data within the same region in different subspaces and combining the reference value, the coordinate positions of the projection points are adjusted to obtain a corrected DEM spatial model.

[0012] The differences between the water flow trend and the actual terrain in the modified DEM spatial model are analyzed, and the reference elevation values ​​of some projection points are adjusted to obtain the secondary modified DEM spatial model.

[0013] In some embodiments of the present invention, the occurrence of the boundary of each cluster range under different clustering radii and the differences in elevation data on both sides of the boundary of each cluster range are analyzed to determine the terrain integrity of each cluster range, including:

[0014] Count the total number of times each cluster boundary appears across all cluster radii, and quantify the occurrence of each cluster boundary under different cluster radii;

[0015] Under the cluster radius containing the same cluster range boundary, calculate the absolute value of the difference between the mean elevation data within the cluster range enclosed by the cluster range boundary and the mean elevation data within its adjacent cluster range to obtain the degree of difference in elevation data on both sides of each cluster range boundary.

[0016] By combining the occurrence and the degree of difference, the topographic integrity of the cluster range enclosed by the boundary of each cluster range is obtained.

[0017] In some embodiments of the present invention, multiple subspaces are obtained, including:

[0018] Set a completeness threshold;

[0019] Determine whether the integrity of the terrain is greater than the integrity threshold;

[0020] If so, the cluster range enclosed by the cluster range boundary is used as the subspace for elevation interpolation calculation using the Kriging algorithm, resulting in multiple subspaces.

[0021] In some embodiments of the present invention, interpolation operations are performed on the subspace to obtain the projected position points and their initial elevation values, including:

[0022] The interpolated elevation points are obtained by interpolating the subspace using the Kriging algorithm.

[0023] For the subspace obtained when the cluster radius value is minimized, the projection position points corresponding to the interpolated elevation points are obtained by using the Kriging interpolation algorithm;

[0024] For each subspace containing a single projection location point, obtain the elevation value of the interpolated elevation point closest to the projection location point in each subspace, and obtain the initial elevation value of the projection location point in each subspace.

[0025] In some embodiments of the present invention, based on the integrity of the terrain and the projected area of ​​the subspace, the reference value of different subspaces for calculating the interpolated elevation of the projected location point is analyzed, and then combined with the initial elevation value, the reference elevation value of the projected location point is obtained, including:

[0026] Based on the integrity of the terrain and the projected area of ​​the subspace, the reference value of different subspaces for calculating the interpolation elevation of the projected location point is obtained.

[0027] Based on the reference value and the initial elevation value, the weighted elevation value of the projected position point in each subspace is obtained. The weighted elevation value of the projected position point is obtained by summing all subspaces containing the projected position point.

[0028] In some embodiments of the present invention, the distance intervals of elevation data within the same region in different subspaces are analyzed, and the coordinate positions of the projection points are adjusted based on the reference value to obtain a corrected DEM spatial model, including:

[0029] Calculate the second Euclidean distance between the projected position point and its corresponding projected position points in different subspaces, and combine it with the reference value to obtain the movement vector of the projected position point to its corresponding projected position points in different subspaces;

[0030] Summing up the movement vectors obtained from all the subspaces, we obtain the total reference vector for the movement of the projected position point;

[0031] Based on the moving reference vector, the coordinates of the projected location points are adjusted to obtain the corrected DEM spatial model.

[0032] In some embodiments of the present invention, the analysis of the difference between the water flow trend and the actual terrain in the modified DEM spatial model includes:

[0033] The study analyzes the degree of difference between the water area in the modified DEM spatial model and the actual water area, and analyzes the degree of difference between the water flow velocity in the modified DEM spatial model and the actual water flow velocity.

[0034] In some embodiments of the present invention, analyzing the degree of area difference between the water area in the modified DEM spatial model and the actual water area includes:

[0035] Contour lines are generated using the modified DEM spatial model, converted into polygonal model water bodies according to the elevation step size, and the area of ​​the model water bodies is calculated to obtain the area of ​​the model water bodies in the modified DEM spatial model.

[0036] Obtain the actual water area corresponding to the model water area;

[0037] The difference between the model water area and the actual water area is calculated to obtain the degree of area difference between the water area in the modified DEM spatial model and the actual water area.

[0038] In some embodiments of the present invention, analyzing the degree of velocity difference between the water flow velocity in the modified DEM spatial model and the actual water flow velocity includes:

[0039] Based on the modified DEM spatial model, the flow direction matrix is ​​calculated using the flow direction algorithm, and then the flow accumulation matrix is ​​generated by combining the slope. The model flow velocity of the model water flow in the modified DEM spatial model is obtained by using the accumulation amount of the flow accumulation matrix and the slope.

[0040] Obtain the actual water flow velocity corresponding to the model water flow;

[0041] Obtain the first Euclidean distance between the interpolated elevation point on the model water flow terrain and the center point of the model water area, wherein the model water flow and the model water area are connected;

[0042] Calculate the difference between the model water flow velocity and the actual water flow velocity, and combine it with the first Euclidean distance to obtain the degree of velocity difference between the water flow velocity in the modified DEM spatial model and the actual water flow velocity.

[0043] In some embodiments of the present invention, the sampling points are arranged as follows:

[0044] The sampling points were arranged using the feature point priority method. Sampling points were required to be placed every 5-10 meters along ridges and valleys, with the density increased to 1-3 meters at locations of abrupt changes in slope, and every 20-50 meters in flat areas.

[0045] As can be seen from the above embodiments, the automatic elevation point extraction method for a DEM spatial model provided by the present invention has the following beneficial effects:

[0046] This invention clusters elevation data from all sampling points by setting different clustering radii, analyzes the occurrence of boundaries for each cluster range under different clustering radii and the differences in elevation data on both sides of each cluster range boundary, determines the terrain integrity of the cluster range enclosed by each cluster range boundary, and divides the subspace for elevation interpolation calculation using the Kriging algorithm. Interpolation is performed on the subspace to obtain the projected location points and their initial elevation values. Combining terrain integrity and the projected area of ​​the subspace, the reference value of different subspaces for estimating the elevation interpolation of the projected location points is analyzed. Then, combined with the initial elevation values, the reference elevation values ​​of the projected location points are obtained. Furthermore, by analyzing the distance intervals between elevation data within the same area in different subspaces and considering the magnitude of the reference value, the coordinate positions of the projected location points are adjusted to obtain a corrected DEM spatial model. Finally, by analyzing the differences between the water flow trend and the actual terrain in the corrected DEM spatial model, the reference elevation values ​​of some projected location points are adjusted to obtain a secondary corrected DEM spatial model. By dividing the space into subspaces and analyzing the influence of each subspace on the projection location points, the coordinates and elevation values ​​of the projection location points corresponding to the interpolation elevation points are adjusted, thereby improving the accuracy of obtaining interpolation elevation points at different locations and thus improving the accuracy of DEM spatial model construction.

[0047] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0048] 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.

[0049] Figure 1 This is a schematic diagram of the basic process of an automatic elevation point extraction method for a DEM spatial model provided in an embodiment of the present invention;

[0050] Figure 2 This is a schematic diagram of the moving reference total vector of a projection position point provided in an embodiment of the present invention. Detailed Implementation

[0051] 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 an automatic elevation point extraction method for a DEM spatial model 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.

[0052] 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. Terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device comprising a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes that element. Relational terms such as “first” and “second” are used merely to distinguish one entity or operation from another and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0053] The following section will provide a detailed description of an automatic elevation point extraction method for a DEM spatial model provided in this embodiment, with reference to the accompanying drawings.

[0054] Please see Figure 1 This illustrates the basic flow of an automatic elevation point extraction method for a DEM spatial model provided by an embodiment of the present invention.

[0055] like Figure 1 As shown, an embodiment of the present invention provides an automatic elevation point extraction method for a DEM spatial model, which specifically includes the following steps:

[0056] S100: Obtain the elevation data of the sampling points.

[0057] The sampling points are arranged using the feature point priority method. Sampling points are required to be collected every 5-10 meters along ridges and valleys, with the density increased to 1-3 meters at abrupt changes in slope, and points are supplemented in flat areas using a 20-50 meter grid.

[0058] The DEM is measured using a total station, with a high-precision instrument (such as a Leica TS60, 0.5″ angle measurement). 3-5 control points should be established, evenly covering the survey area and with line-of-sight communication with the station. During measurement, the instrument should be stably mounted (centering error <1mm), and a single-shot precision measurement mode should be used (averaging 2-3 measurements). For complex terrain such as steep cliffs, multi-station intersection measurements should be employed. Finally, elevation data from multiple sampling points are obtained.

[0059] S200: Set different clustering radii to cluster elevation data and obtain the clustering range corresponding to different clustering radii.

[0060] The Kriging algorithm's estimation of elevation points in a DEM spatial model may suffer from smoothing of ridges and valleys due to modeling biases in the variogram. Therefore, the DEM spatial model can be divided into multiple subspaces with consistent topography for estimating elevation data at each location.

[0061] Therefore, in this embodiment of the invention, the elevation data is first clustered by setting different clustering radii to obtain the clustering ranges corresponding to different clustering radii. Specifically, the elevation data of different sampling points are clustered using the DBSCAN density clustering method, and the clustering range is changed by modifying the radius parameter ε. The radius parameter ε is set to 30m, 25m, 20m, 15m, and 10m, and the elevation data of all measured sampling points are clustered with different radius parameter ε values ​​to obtain the clustering range corresponding to each radius parameter ε value. It should be noted that different radius parameter ε values ​​may or may not correspond to the same clustering range.

[0062] S300: Analyze the occurrence of the boundary of each cluster range under different cluster radii and the difference in elevation data on both sides of the boundary of each cluster range to determine the terrain integrity of each cluster range and obtain multiple subspaces.

[0063] After obtaining the range through clustering, the occurrence of each cluster range boundary under different cluster radii and the differences in elevation data on both sides of each cluster range boundary are analyzed. The cluster range boundary refers to the dividing line between different clusters (different cluster ranges) in the clustering results. The terrain integrity of the cluster range enclosed by each cluster range boundary is determined, thus obtaining the subspace for elevation interpolation calculation using the Kriging algorithm. Further steps include:

[0064] First, the total number of occurrences of each cluster boundary is counted across all cluster radii, quantifying the occurrence of each cluster boundary under different cluster radii. Specifically, for the boundaries of multiple cluster ranges obtained under different radius parameter ε values, the total number of occurrences n of each cluster range boundary j under all radius parameter ε values ​​is counted. jThe occurrence of each cluster range boundary under different cluster radii is quantified.

[0065] Then, under the cluster radius containing the same cluster boundary, the absolute value of the difference between the mean elevation data within the cluster bounded by the cluster boundary and the mean elevation data within its adjacent clusters is calculated to obtain the degree of difference in elevation data on both sides of each cluster boundary. Specifically, for clustering operations with different radius parameter ε values ​​for cluster boundary j, the mean elevation data of all sampling points contained in the cluster bounded by cluster boundary j and its multiple adjacent clusters are calculated separately. Then, the sum of the absolute values ​​of the differences between the mean elevation data of the cluster bounded by cluster boundary j and the mean elevation data of all other adjacent clusters is calculated, denoted as H. j,ε Furthermore, iterate through all radius parameter ε values ​​to obtain the H value corresponding to each radius parameter ε value. j,ε Then calculate H corresponding to all radius parameter ε values. j,ε and (z represents the number of values ​​for the radius parameter ε), thus obtaining the degree of difference in elevation data on both sides of the boundary j of each cluster range.

[0066] Furthermore, by combining the occurrence and degree of difference, the topographic integrity of the cluster range enclosed by the boundary of each cluster range is obtained. Specifically, the more times (nx) different cluster range boundaries j appear under all radius parameter ε values, and the greater the difference between the elevation performance of the cluster range corresponding to the cluster boundary j and the average elevation of other adjacent cluster ranges under different radius parameter ε values, the higher the topographic integrity of the cluster range. The larger the value, the greater the difference between the terrain shown by the cluster range enclosed by the cluster range boundary j and the surrounding terrain, and the more complete the representation of the terrain of a single region by the cluster range. Therefore, the completeness of the terrain of the cluster range enclosed by the cluster range boundary j can be obtained as follows:

[0067]

[0068] In the formula, Q j H represents the degree of topographic integrity of the cluster range enclosed by the cluster range boundary j; j,ε This represents the sum of the absolute values ​​of the differences between the mean elevation of the cluster bounded by cluster boundary j and the mean elevation of all other adjacent clusters under clustering operations with radius parameter ε; z represents the number of values ​​for radius parameter ε; n j n represents the total number of times the cluster boundary j appears under all radius parameter ε values. j ;norm represents the maximum-minimum linear normalization function.

[0069] Finally, based on the terrain integrity of the cluster ranges enclosed by the boundaries of each cluster range, the subspace for elevation interpolation using the Kriging algorithm is obtained. Specifically, a integrity threshold is set, which can be 0.5; it is then determined whether the terrain integrity is greater than the integrity threshold; if so, Q... j If the value is greater than 0.5, then the cluster range enclosed by the cluster range boundary j is used as the subspace for elevation interpolation calculation using the Kriging algorithm. It should be noted that different values ​​of the radius parameter ε can yield corresponding subspaces.

[0070] S400: Performs interpolation on the subspace to obtain the projected location points and their initial elevation values.

[0071] After obtaining the subspace used for elevation interpolation calculations using the Kriging algorithm, interpolation is then performed on the subspace to obtain the projected location points and their initial elevation values. Further steps include:

[0072] First, the subspace is interpolated using the Kriging algorithm to obtain interpolated elevation points. This step is existing technology and will not be described in detail here.

[0073] Then, because the interpolation result of the Kriging algorithm is highly sensitive to the choice of subspace, differences in subspaces will change the spatial structure representation of sample points, affecting weight allocation and model parameters, ultimately leading to differences in the projection position of the same interpolated elevation point in different subspaces. Therefore, in some embodiments of the present invention, for the subspace obtained when the cluster radius ε is minimized, the projection position point corresponding to the interpolated elevation point is obtained by the Kriging interpolation algorithm, and this projection position point is taken as the priority analysis object.

[0074] Finally, for all subspaces m (the subspaces with the smallest cluster radius ε) containing a single projected location point p, obtain the projected location point p corresponding to the interpolated elevation point closest to the projected location point p obtained in each subspace m. m The elevation value is taken as the initial elevation value of the projection point p in each subspace m, and denoted as h. p,m .

[0075] S500: Based on the integrity of the terrain and the projected area of ​​the subspace, analyze the reference value of different subspaces for calculating the elevation interpolation of the projection location point, and then combine the initial elevation value to obtain the reference elevation value of the projection location point.

[0076] Based on the integrity of the terrain and the projected area of ​​the subspace, the reference value of different subspaces for interpolating the elevation of the projected location point is analyzed. Then, combined with the initial elevation value, the reference elevation value of the projected location point is obtained. Further steps include:

[0077] First, based on the integrity of the terrain and the projected area of ​​the subspace, we obtain the reference value of different subspaces for calculating the interpolation elevation of the projected location points.

[0078] Specifically, obtain the terrain integrity Q of all subspaces m. m And obtain the projected area s of all subspaces m. m (The subspace is three-dimensional, and the projection is onto the xy plane; the projected area can be directly obtained from the system.) When the terrain integrity Q of subspace m... m The larger the projected area s, the greater the value of the projected area. m When the size is larger, using existing sampling points in subspace m as a reference for the Kriging algorithm results in less interference from other terrain features, and the projected area s... m The larger the value, the more existing sampling points there are, and the more meaningful the elevation reference for the interpolation point becomes. Therefore, the initial elevation value h of the projected position point p obtained from subspace m can be obtained. p,m The reference value of the interpolated elevation of the projected location point p is as follows:

[0079] W p,m =norm(Q) m ×s m )

[0080] In the formula, W p,m This indicates the reference value of subspace m for calculating the interpolated elevation of the projected position point p, that is, the initial elevation value h of the projected position point p obtained from subspace m. p,m The reference value of interpolated elevation for the projected location point p; Q m The degree of terrain integrity of subspace m; s m denoted by , where represents the projected area of ​​subspace m; norm represents the maximum-minimum linear normalization function.

[0081] When W p,m When the size is larger, the initial elevation value h of the projection position point p obtained from subspace m is... p,m The greater the reference value of the interpolated elevation of the projection location point p, the greater the reference value.

[0082] Then, based on the reference value and the initial elevation value, the weighted elevation value of the projected location point in each subspace is obtained. This weighted elevation value is then summed across all subspaces containing the projected location point to obtain the reference elevation value of the projected location point. Specifically, for a single projected location point p, the weighted elevation value is calculated across all subspaces m (with potential integrity Q) containing p. m The initial elevation value h of the projection location point p obtained by (>0.5) p,m Its corresponding reference value W p,mMultiply the values ​​to obtain the weighted elevation values ​​of the projected location point p in each subspace m; iterate through all subspaces m containing the projected location point p, sum the weighted elevation values, and obtain the reference elevation value of a single projected location point p, denoted as h. p .

[0083] S600: Analyze the distance intervals of elevation data within the same region in different subspaces, and adjust the coordinate positions of the projection points based on reference value to obtain a corrected DEM spatial model.

[0084] The elevation value is adjusted by using the cluster range obtained when the cluster radius ε is the smallest as the projection position of the interpolated elevation point obtained by the subspace. The correspondence between the obtained reference elevation value and the projection position point may not be accurate enough. The projection position of the adjusted interpolated elevation point (reference elevation value of the projection position point) can be reasonably corrected by considering the reference value of the interpolated elevation of the projection position point and the distance relationship between the projection position point and the projection position point through different subspaces.

[0085] Based on the above analysis, in some embodiments of the present invention, the distance intervals of elevation data within the same region in different subspaces are analyzed. Combined with reference value, the coordinate positions of the projection points are adjusted to obtain a corrected DEM spatial model. Further aspects include:

[0086] First, for a subspace m containing the projected position point p, calculate the projected position point p and its corresponding projected position point p in different subspaces m. m (The projection point p corresponding to the interpolated elevation point closest to the projection point p obtained from subspace m) m The second Euclidean distance between ) is denoted as l. p,m Then, obtain the reference value W of different subspaces m containing the projection point p for calculating the interpolated elevation of the projection point p. p,m When the reference value is W p,m The larger the value, the greater the second Euclidean distance l. p,m When the size is larger, the corresponding projection position point p in subspace m is p m The greater the contribution of a point to the projection location p, the more it should be directed towards the corresponding projection location p in the subspace m. m The more the direction is moved, the greater the distance; therefore, the second Euclidean distance l corresponding to subspace m. p,m With reference value W p,m By combining these methods, we can obtain the projection positions p of the projection point p in different subspaces m. m The movement vector, where the direction of the movement vector is the corresponding projection position point p in subspace m. m The direction, the magnitude of the movement vector (movement length) is the second Euclidean distance l corresponding to subspace m. p,nWith reference value W p,m The product of.

[0087] Then, the movement vectors corresponding to all subspaces m containing the projected position point p are summed to obtain the total reference vector for the movement of the projected position point p, such as... Figure 2 As shown. Then, based on the moving reference vector, the coordinates of the projected position point (the coordinates of the interpolated elevation point corresponding to the projected position point p in the xy plane) are adjusted. And based on the adjusted coordinates, the corrected DEM spatial model is obtained.

[0088] S700: Analyze the difference between the water flow trend and the actual terrain in the DEM spatial model, adjust the reference elevation values ​​of some projection points, and obtain the secondary corrected DEM spatial model.

[0089] The difference between the terrain in the corrected DEM spatial model obtained in step S600 and the actual terrain may cause discrepancies between the water flow path obtained based on the DEM spatial model and the actual terrain. To make the terrain in the DEM spatial model more consistent with the actual terrain, the reference elevation values ​​of different elevation points should be adjusted using the simulated hydrological information.

[0090] Based on the above analysis, in the embodiments of the present invention, by analyzing the difference between the water flow trend and the actual terrain in the corrected DEM spatial model, the reference elevation values ​​of some projection points are adjusted to obtain a secondary corrected DEM spatial model. Further, this includes: analyzing the degree of difference between the water area in the corrected DEM spatial model and the actual water area, and analyzing the degree of difference between the water flow velocity in the corrected DEM spatial model and the actual water flow velocity, adjusting the reference elevation values ​​of some projection points to obtain a secondary corrected DEM spatial model. Wherein:

[0091] The analysis of the difference between the water area in the corrected DEM spatial model and the actual water area is carried out as follows: First, contour lines are generated using the corrected DEM spatial model (such as the Contour List tool), and converted into polygonal model water areas according to the elevation step size. The area of ​​the model water areas is then calculated to obtain the area of ​​the model water areas in the corrected DEM spatial model. At the same time, the area of ​​the actual water areas corresponding to the model water areas is obtained (through existing data records). Finally, the difference between the model water area and the actual water area (model water area - actual water area) is calculated to obtain the degree of difference between the water area in the corrected DEM spatial model and the actual water area.

[0092] The analysis of the difference between the flow velocity in the corrected DEM spatial model and the actual flow velocity is carried out as follows: First, based on the corrected DEM spatial model, a flow direction matrix is ​​calculated using a flow direction algorithm (such as D8 or a single-flow direction algorithm). Then, a flow accumulation matrix is ​​generated by combining the slope. The model flow velocity in the corrected DEM spatial model is obtained by using the accumulation amount of the flow accumulation matrix and the slope. Simultaneously, the actual flow velocity corresponding to the model flow is obtained (through existing data records). Furthermore, the first Euclidean distance between the projection position point corresponding to the interpolated elevation point on the model flow terrain and the center point of the model water area is obtained, where the model flow and the model water area are connected. Finally, the difference between the model flow velocity and the actual flow velocity is calculated, and combined with the first Euclidean distance, the degree of difference between the flow velocity in the corrected DEM spatial model and the actual flow velocity is obtained.

[0093] The degree of difference between the water area in the corrected DEM spatial model and the actual water area, and the degree of difference between the water flow velocity in the corrected DEM spatial model and the actual water flow velocity are obtained. Then, the degree of adjustment of the projection position points corresponding to the interpolated elevation points is calculated by combining the degree of area difference and the degree of flow velocity difference:

[0094] U k =tanh{exp(s d -S d )×exp[(V b -v b )×l k,b,d ])

[0095] In the formula, U k This indicates the degree of elevation adjustment at the projection location point k; s d S represents the area of ​​the model water body d in the corrected DEM spatial model; d This indicates the actual water area corresponding to the actual water body d in the DEM spatial model; v b This indicates the model flow velocity of model flow b in the DEM spatial model; V b This indicates the actual flow velocity corresponding to the actual flow in the model flow b in the DEM spatial model; l k,b,d denoted by k, represents the first Euclidean distance between the projected location point k on the terrain of model water flow b and the center point of model water area d (model water flow b and model water area d are connected); tanh represents the hyperbolic tangent function, and exp represents the exponential function with the natural constant e as the base.

[0096] s d -S dThis indicates the degree of difference between the area of ​​the water body in the DEM spatial model and the actual water body area. When this difference is positive and larger, the elevation value of the interpolated elevation point near the source of the water flow in the DEM spatial model should be larger, thereby reducing the area of ​​the model water body. (V) b -v b )×l k,b,d This indicates the degree of difference between the flow velocity in the corrected DEM spatial model and the actual flow velocity. When the velocity difference (V) b -v b When the value U is positive and larger, to increase the flow velocity of the water flow in the DEM spatial model and improve the steepness of the terrain, the elevation values ​​of interpolation elevation points farther from the model water area should be increased even more. Furthermore, when the elevation value adjustment degree U... k The larger the value, the greater the elevation value of the interpolation elevation point should be.

[0097] Therefore, the elevation adjustment value of the projection location point k can be obtained as follows:

[0098] h k '=h k ×(1+U k )

[0099] In the formula, h k 'Indicates the elevation adjustment value of the projection location point k; h k U represents the reference elevation value of the projected location point k; k This indicates the degree of elevation adjustment at the projection location point k.

[0100] Similarly, the interpolated elevation points of all areas where the model water area does not match the actual water area are corrected, and the elevation adjustment values ​​of the projected location points are used to finally obtain the secondary corrected DEM spatial model.

[0101] The above methods are used to accurately obtain interpolation elevation points at different locations, thereby constructing an accurate DEM spatial model. The resulting DEM spatial model and the corresponding data of multiple interpolation elevation points are then stored in a database.

[0102] The locations of different interpolated elevation points in the DEM spatial model are visualized in tabular form using SQL query statements, as shown in the table below.

[0103] Interpolation elevation points Location / m 001 (25,45,36) 002 (25,67,36) … …

[0104] 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.

[0105] 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.

Claims

1. A method for automatically extracting elevation points from a DEM spatial model, characterized in that, The method includes: Obtain the elevation data of the sampling points; By setting different clustering radii, the elevation data is clustered to obtain the clustering ranges corresponding to different clustering radii; By analyzing the occurrence of the boundary of each cluster range under different cluster radii and the difference in elevation data on both sides of each cluster range boundary, the terrain integrity of each cluster range is determined, and multiple subspaces are obtained. Interpolation is performed on the subspace to obtain the projected position points and their initial elevation values; Based on the integrity of the terrain and the projected area of ​​the subspace, the reference value of different subspaces for calculating the elevation interpolation of the projection location point is analyzed. Then, combined with the initial elevation value, the reference elevation value of the projection location point is obtained. By analyzing the distance intervals of elevation data within the same region in different subspaces and combining the aforementioned reference value, the coordinate positions of the projection points are adjusted to obtain a corrected DEM spatial model. The differences between the water flow trend and the actual terrain in the modified DEM spatial model are analyzed, and the reference elevation values ​​of some projection points are adjusted to obtain the secondary modified DEM spatial model.

2. The method for automatically extracting elevation points from a DEM spatial model according to claim 1, characterized in that, Analyze the occurrence of each cluster boundary under different cluster radii and the differences in elevation data on both sides of each cluster boundary to determine the topographic integrity of each cluster, including: Count the total number of times each cluster boundary appears across all cluster radii, and quantify the occurrence of each cluster boundary under different cluster radii; Under the cluster radius containing the same cluster range boundary, calculate the absolute value of the difference between the mean elevation data within the cluster range enclosed by the cluster range boundary and the mean elevation data within its adjacent cluster range to obtain the degree of difference in elevation data on both sides of each cluster range boundary. By combining the occurrence and the degree of difference, the topographic integrity of the cluster range enclosed by the boundary of each cluster range is obtained.

3. The method for automatically extracting elevation points from a DEM spatial model according to claim 2, characterized in that, This results in multiple subspaces, including: Set a completeness threshold; Determine whether the integrity of the terrain is greater than the integrity threshold; If so, the cluster range enclosed by the cluster range boundary is used as the subspace for elevation interpolation calculation using the Kriging algorithm, resulting in multiple subspaces.

4. The method for automatically extracting elevation points from a DEM spatial model according to claim 1, characterized in that, Interpolation is performed on the subspace to obtain the projected position points and their initial elevation values, including: The interpolated elevation points are obtained by interpolating the subspace using the Kriging algorithm. For the subspace obtained when the cluster radius value is minimized, the projection position point corresponding to the interpolated elevation point is obtained by using the Kriging interpolation algorithm; For each subspace containing a single projection location point, obtain the elevation value of the interpolated elevation point closest to the projection location point in each subspace, and obtain the initial elevation value of the projection location point in each subspace.

5. The method for automatically extracting elevation points from a DEM spatial model according to claim 1, characterized in that, Based on the integrity of the terrain and the projected area of ​​the subspace, the reference value of different subspaces for calculating the interpolated elevation of the projected location point is analyzed. Then, combined with the initial elevation value, the reference elevation value of the projected location point is obtained, including: Based on the integrity of the terrain and the projected area of ​​the subspace, the reference value of different subspaces for calculating the interpolation elevation of the projected location point is obtained. Based on the reference value and the initial elevation value, the weighted elevation value of the projected position point in each subspace is obtained. The weighted elevation value of the projected position point is obtained by summing all subspaces containing the projected position point.

6. The method for automatically extracting elevation points from a DEM spatial model according to claim 1, characterized in that, Analyzing the distance intervals of elevation data within the same region in different subspaces, and combining this with the aforementioned reference value, the coordinate positions of the projection points are adjusted to obtain a corrected DEM spatial model, including: Calculate the second Euclidean distance between the projected position point and its corresponding projected position points in different subspaces, and combine it with the reference value to obtain the movement vector of the projected position point to its corresponding projected position points in different subspaces; Summing up the movement vectors obtained from all the subspaces, we obtain the total reference vector for the movement of the projected position point; Based on the moving reference vector, the coordinates of the projected location points are adjusted to obtain the corrected DEM spatial model.

7. The method for automatically extracting elevation points from a DEM spatial model according to claim 1, characterized in that, The analysis of the differences between the water flow trend and the actual terrain in the modified DEM spatial model includes: The study analyzes the degree of difference between the water area in the modified DEM spatial model and the actual water area, and analyzes the degree of difference between the water flow velocity in the modified DEM spatial model and the actual water flow velocity.

8. The method for automatically extracting elevation points from a DEM spatial model according to claim 7, characterized in that, The analysis includes assessing the degree of difference between the water area in the modified DEM spatial model and the actual water area, including: Contour lines are generated using the modified DEM spatial model, converted into polygonal model water bodies according to the elevation step size, and the area of ​​the model water bodies is calculated to obtain the area of ​​the model water bodies in the modified DEM spatial model. Obtain the actual water area corresponding to the model water area; The difference between the model water area and the actual water area is calculated to obtain the degree of area difference between the water area in the modified DEM spatial model and the actual water area.

9. The method for automatically extracting elevation points from a DEM spatial model according to claim 8, characterized in that, The analysis of the velocity difference between the water flow velocity in the modified DEM spatial model and the actual water flow velocity includes: Based on the modified DEM spatial model, the flow direction matrix is ​​calculated using the flow direction algorithm, and then the flow accumulation matrix is ​​generated by combining the slope. The model flow velocity of the model water flow in the modified DEM spatial model is obtained by using the accumulation amount of the flow accumulation matrix and the slope. Obtain the actual water flow velocity corresponding to the model water flow; Obtain the first Euclidean distance between the interpolated elevation point on the model water flow terrain and the center point of the model water area, wherein the model water flow and the model water area are connected; Calculate the difference between the model water flow velocity and the actual water flow velocity, and combine it with the first Euclidean distance to obtain the degree of velocity difference between the water flow velocity in the modified DEM spatial model and the actual water flow velocity.

10. The method for automatically extracting elevation points from a DEM spatial model according to claim 1, characterized in that, The sampling points are arranged as follows: The sampling points were arranged using the feature point priority method. Sampling points were required to be placed every 5-10 meters along ridges and valleys, with the density increased to 1-3 meters at locations of abrupt changes in slope, and every 20-50 meters in flat areas.

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