Digital elevation three-dimensional terrain enhancement method and device

Abstract

The invention discloses a digital elevation three-dimensional terrain enhancement method and device. The method comprises the following steps: acquiring digital elevation model data; performing feature extraction on the digital elevation model data to obtain terrain elevation features; generating multi-scale noise according to the terrain elevation features; wherein the multi-scale noise is composed of a plurality of noises in different frequency intervals; performing fusion processing on the multi-scale noise to generate a terrain elevation result; and rendering the terrain elevation result, and constructing a digital elevation three-dimensional terrain. According to the technical scheme, terrain detail enhancement can be efficiently, safely and stably carried out at low cost, and the visual performance effect of the digital elevation three-dimensional terrain is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and apparatus for digital elevation three-dimensional terrain enhancement. Background Technology

[0002] Digital Elevation Models (DEMs), as physical ground models representing ground elevation in the form of ordered numerical arrays, are core data sources for constructing wide-area basic 3D terrain data in fields such as Geographic Information Systems (GIS), City Information Modeling (CIM), and digital twins. In practical applications, DEMs are often used in conjunction with texture data sources such as digital orthophotos. By fusing elevation information with texture details, they achieve a realistic reconstruction of 3D terrain, providing fundamental support for various spatial analyses and visualizations. Their applications cover multiple important fields such as urban planning, engineering construction, and emergency management. However, DEM data faces the critical problem of a mismatch between accuracy and application requirements. Therefore, there is an urgent need for a technical solution that can enhance the details of 3D terrain while ensuring the accuracy of basic elevation data. Summary of the Invention

[0003] This invention provides a method and apparatus for enhancing three-dimensional terrain using digital elevation modeling (DEM), which can efficiently, cost-effectively, safely, and stably enhance terrain details and significantly improve the visual performance of three-dimensional terrain using DEM.

[0004] According to one aspect of the present invention, a digital elevation three-dimensional terrain enhancement method is provided, the method comprising:

[0005] Obtain digital elevation model data;

[0006] Feature extraction is performed on the digital elevation model data to obtain terrain elevation features;

[0007] Based on the terrain elevation features, multi-scale noise is generated; wherein, the multi-scale noise is composed of noise in multiple different frequency ranges;

[0008] The multi-scale noise is fused to generate terrain elevation results;

[0009] The terrain elevation results are rendered to construct a digital elevation 3D terrain.

[0010] According to another aspect of the present invention, a digital elevation three-dimensional terrain enhancement device is provided, the device comprising:

[0011] The digital elevation model data acquisition module is used to acquire digital elevation model data.

[0012] The terrain elevation feature acquisition module is used to extract features from the digital elevation model data to obtain terrain elevation features;

[0013] A multi-scale noise generation module is used to generate multi-scale noise based on the terrain elevation features; wherein the multi-scale noise is composed of noise in multiple different frequency ranges;

[0014] The terrain elevation result generation module is used to fuse the multi-scale noise to generate terrain elevation results.

[0015] The Digital Elevation 3D Terrain Construction Module is used to render the terrain elevation results and construct the Digital Elevation 3D Terrain.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a digital elevation three-dimensional terrain enhancement method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement a digital elevation three-dimensional terrain enhancement method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a digital elevation three-dimensional terrain enhancement method as described in any embodiment of the present invention.

[0020] The technical solution of this invention involves acquiring digital elevation model (DEM) data; extracting features from the DEM data to obtain terrain elevation features; generating multi-scale noise based on the terrain elevation features; fusing the multi-scale noise to generate terrain elevation results; and rendering the terrain elevation results to construct a three-dimensional terrain model. This technical solution can efficiently, cost-effectively, safely, and stably enhance terrain details, significantly improving the visual performance of the three-dimensional terrain model.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0023] Figure 1 This is a flowchart of a digital elevation three-dimensional terrain enhancement method according to Embodiment 1 of the present invention;

[0024] Figure 2 A flowchart of the method for real-time rendering detail enhancement of 3D terrain based on multi-scale noise and feature adaptation provided in Embodiment 1 of this application;

[0025] Figure 3 This is a schematic diagram of a digital elevation three-dimensional terrain enhancement process provided in Embodiment 2 of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of a digital elevation three-dimensional terrain enhancement device provided in Embodiment 3 of the present invention;

[0027] Figure 5 This is a schematic diagram of the structure of an electronic device that implements a digital elevation three-dimensional terrain enhancement method according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "initial," "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a digital elevation model (DEM) 3D terrain enhancement method according to Embodiment 1 of the present invention. This embodiment is applicable to situations requiring detail enhancement of digital elevation model data. The method can be executed by a DEM 3D terrain enhancement device, which can be implemented in hardware and / or software and can be configured within a device. For example, the device can be a backend server or other device with communication and computing capabilities. Figure 1 As shown, the method includes:

[0032] S110. Obtain digital elevation model data.

[0033] Among them, the Digital Elevation Model (DEM) is a physical ground model that uses an ordered array of numerical values ​​to represent ground elevation.

[0034] In this approach, digital elevation model data is obtained from a database.

[0035] S120. Extract features from the digital elevation model data to obtain terrain elevation features.

[0036] In this scheme, topographic elevation features include slope, curvature, and flow direction trend. The flow direction trend is used to characterize the flow direction along the elevation gradient.

[0037] In this embodiment, terrain elevation feature extraction is a process of calculating and analyzing the input low-precision digital elevation model data to identify its key terrain features and generate a feature mask. The corresponding mask adopts an RGB three-channel texture design, with the R channel recording slope information, the G channel recording curvature information, and the B channel recording flow trend information.

[0038] Specifically, feature extraction technology is used to extract features from digital elevation model data to obtain terrain elevation features.

[0039] Optionally, the terrain elevation features include slope; feature extraction is performed on the digital elevation model data to obtain terrain elevation features, including:

[0040] Based on the height value and longitude dimension of each pixel in the digital elevation model data, the partial derivative value of each pixel in the longitude direction is calculated; and, using the height value and latitude dimension of each pixel in the digital elevation model data, the partial derivative value of each pixel in the latitude direction is calculated.

[0041] The slope is obtained by calculating the partial derivative values ​​of each pixel in the longitude direction and the partial derivative values ​​of each pixel in the latitude direction.

[0042] In this scheme, the partial derivative value of each pixel in the longitude direction is calculated based on the height value and longitude dimension of each pixel in the digital elevation model data. Specifically, the partial derivative value of each pixel in the longitude direction is calculated using the following formula:

[0043] ;

[0044] in, Here, represents the partial derivative of the longitude direction for each pixel, and height represents the height value of the corresponding pixel in the digital elevation model data. This refers to the dimension along the longitude direction in the digital elevation model data.

[0045] In this embodiment, the partial derivative value of each pixel in the latitude direction is calculated using the height value and latitude dimension of each pixel in the digital elevation model data. Specifically, the partial derivative value of each pixel in the latitude direction is calculated using the following formula:

[0046] ;

[0047] in, Here, represents the partial derivative of each pixel along the latitude direction, and height represents the height value of the corresponding pixel in the digital elevation model data. This refers to the dimension in the latitudinal direction within the digital elevation model data.

[0048] Furthermore, based on the partial derivative values ​​of each pixel in the longitude direction and the partial derivative values ​​of each pixel in the latitude direction, the slope is calculated:

[0049] .

[0050] By extracting the height information of each pixel in the digital elevation model data, and calculating the partial derivative values ​​in the corresponding directions in combination with the size parameters in the longitude and latitude directions, the slope data is further derived based on the two types of partial derivative values. This not only makes full use of the refined spatial information of the elevation model, but also ensures the accuracy and reliability of the slope results through the step-by-step calculation of the directional partial derivatives, thus enabling efficient and accurate quantitative extraction of terrain slope.

[0051] Optionally, the terrain elevation features include curvature; feature extraction is performed on the digital elevation model data to obtain terrain elevation features, including:

[0052] Calculate the first-order and second-order partial derivatives of the height values ​​of each pixel in the digital elevation model data, and combine the first-order and second-order partial derivatives to obtain the curvature.

[0053] In this scheme, sampling is performed on a 5×5 grid, and the approximate second-order partial derivatives of the height values ​​of each pixel in the digital elevation model data are calculated based on the Hessian matrix. The second-order partial derivatives are calculated using the following formula;

[0054] ;

[0055] ;

[0056] ;

[0057] in, This represents the height value of each pixel in the digital elevation model data.

[0058] In this embodiment, the Sobel operator is used to calculate the approximate first-order partial derivatives corresponding to the height values ​​of each pixel in the digital elevation model data. The first-order partial derivatives are calculated using the following formula;

[0059] ;

[0060] ;

[0061] Furthermore, the curvature is obtained by combining the first-order and second-order partial derivatives. The curvature is calculated using the following formula:

[0062] ;

[0063] in, For curvature.

[0064] Curvature is obtained by calculating the first and second partial derivatives of the height values ​​of each pixel in the digital elevation model data and performing combined calculations. This allows for the precise quantification of the curvature and variation characteristics of the terrain surface, providing core parameter support for the refined analysis of terrain morphology.

[0065] Optionally, the terrain elevation features include flow direction trends; the flow direction trends are used to characterize the flow direction along the elevation gradient; feature extraction is performed on the digital elevation model data to obtain terrain elevation features, including:

[0066] For each pixel in the digital elevation model data, determine the first neighborhood corresponding to each pixel;

[0067] The first neighborhood is divided according to a preset partitioning rule to obtain each second neighborhood;

[0068] Using the height values ​​of the pixels in each second neighborhood, the flow trends are calculated; wherein, the flow trends are characterized by direction angles.

[0069] Specifically, for each pixel in the digital elevation model data, the 8-neighborhood method is used to determine its corresponding first neighborhood.

[0070] In this embodiment, the partitioning rule is to classify the eight directions of the first neighborhood into three groups: due north and due south, due east and due west, and the remaining directions. Specifically, the first neighborhood is partitioned according to the partitioning rule to obtain three groups of second neighborhoods.

[0071] Furthermore, the pixel with the largest height value is selected from each group of second neighbors, and its corresponding direction-assigned preset coding value is written into that pixel. The preset coding value is used to characterize the flow trend, and different coding values ​​correspond to different direction angles. For example, the coding values ​​and directions correspond to East 1, Southeast 2, South 4, Southwest 8, West 16, Northwest 32, North 64, and Northeast 128.

[0072] The flow trend calculation employs a custom D8-3 algorithm, an improved version of the D8 hydrological algorithm. The specific process is as follows: First, the height difference between each pixel in the digital elevation model data and its eight neighboring pixels is calculated. Then, the height differences in the eight directions are divided into three groups (north-south, east-west, and other directions). Finally, within each group, the neighboring pixel with the largest height difference is selected and assigned a preset encoding value for that direction. Since the pixel spacing within each group is equal, the height difference here is equivalent to the distance weight difference, while also reducing computational complexity.

[0073] In this embodiment, if multiple directional values ​​are the same within a group, the neighborhood range needs to be gradually expanded until a unique maximum value is found. If a unique maximum value cannot be determined even after exhausting all neighborhoods, then all pixels in the group are not marked. If the values ​​of all neighboring pixels of the center pixel are higher than its own, then the center pixel is marked as 0.

[0074] By optimizing the algorithm logic, the extraction of river flow direction in a single direction is upgraded to a trend analysis that includes the range of influence of the flow direction. This can more accurately reflect the erosive effect of tributaries on the terrain, providing stronger support for the goal of terrain refinement in the proposal.

[0075] S130. Based on the terrain elevation characteristics, generate multi-scale noise; wherein the multi-scale noise is composed of noise in multiple different frequency ranges.

[0076] In this scheme, multi-scale noise generation achieves a composite interpolation effect by superimposing low-frequency, mid-frequency, and high-frequency layers in the target area. This achieves high consistency at the macroscopic shape preservation, mesoscopic structure, and microscopic detail levels, thereby realizing detail enhancement that closely approximates real terrain features. Simultaneously, dynamic adjustment using feature masks further improves the matching degree between enhanced details and terrain features. Specifically, the low-frequency layer corresponds to the first frequency range; the mid-frequency layer corresponds to the second frequency range; and the high-frequency layer corresponds to the third frequency range. The upper limit of the first frequency range is lower than the lower limit of the second frequency range; and the upper limit of the second frequency range is lower than the lower limit of the third frequency range.

[0077] In this scheme, Perlin noise is used in the low-frequency layer to optimize the macroscopic landform outline; Worley-Perlin hybrid noise is used in the mid-frequency layer to simulate topographic features such as erosion gullies. The hybrid method adopts linear superposition with a weight ratio of Perlin=0.7 and Worley=0.3, with Perlin dominating; OpenSimplex2 noise is used in the high-frequency layer to highlight the surface microstructure.

[0078] In this scheme, the initial noise of the low-frequency, mid-frequency, and high-frequency layers is controlled based on the slope, curvature, and flow direction trends in the terrain elevation characteristics, so as to generate target noise of the low-frequency, mid-frequency, and high-frequency layers, i.e., generate multi-scale noise.

[0079] S140. The multi-scale noise is fused to generate terrain elevation results.

[0080] In this scheme, the target noise from the low-frequency layer, mid-frequency layer, and high-frequency layer is fused to generate terrain elevation results.

[0081] S150. Render the terrain elevation results to construct a digital elevation three-dimensional terrain.

[0082] In this scheme, digital elevation model data is only passed to the GPU (Graphics Processing Unit) in read-only texture form, and writing back to CPU memory is prohibited throughout the process; obfuscation processing is performed immediately after the feature mask is generated, and it is converted into an encrypted texture format to ensure the security of rendering calls.

[0083] In this embodiment, a Level of Detail (LOD) strategy is used to render the terrain elevation results and construct a digital elevation model (DEM) 3D terrain. Specifically, the region is first divided into blocks and frustum culling is performed, and then a four-level layered 3D terrain mesh is constructed. The accuracy, from low to high, is as follows: DEM model 3D terrain mesh, DEM model + low-frequency layer noise interpolation 3D terrain mesh, DEM model + low-frequency layer noise interpolation + mid-frequency layer noise interpolation 3D terrain mesh, and DEM model + low-frequency layer noise interpolation + mid-frequency layer noise interpolation + high-frequency layer noise interpolation 3D terrain mesh.

[0084] Furthermore, if it is necessary to increase the number of levels, the accuracy can be refined by modifying the frequency division noise weight function; at the same time, it supports texture mapping Mipmap technology to ensure a consistent visual appearance of the digital elevation 3D terrain.

[0085] In this plan, Figure 2 The flowchart of the real-time rendering detail enhancement method for 3D terrain based on multi-scale noise and feature adaptation provided in Embodiment 1 of this application is as follows: Figure 2 As shown, through four technical steps—terrain elevation feature extraction, multi-scale noise generation, feature adaptive fusion generation, and LOD rendering management—low-precision DEM data is rendered into a three-dimensional terrain with multi-level details, resulting in rich, reasonable, and safe details.

[0086] The technical solution of this invention involves acquiring digital elevation model (DEM) data; extracting features from the DEM data to obtain terrain elevation features; generating multi-scale noise based on the terrain elevation features; fusing the multi-scale noise to generate terrain elevation results; and rendering the terrain elevation results to construct a three-dimensional terrain model. This technical solution can efficiently, cost-effectively, safely, and stably enhance terrain details, significantly improving the visual performance of the three-dimensional terrain model.

[0087] Example 2

[0088] Figure 3 This is a schematic diagram of a digital elevation 3D terrain enhancement process provided in Embodiment 2 of the present invention. The relationship between this embodiment and the above embodiments is a detailed description of the multi-scale noise fusion process. Figure 3 As shown, the method includes:

[0089] S310. Obtain digital elevation model data.

[0090] S320. Extract features from the digital elevation model data to obtain terrain elevation features.

[0091] S330. Obtain the interpolation step size of the first frequency interval, the interpolation step size of the second frequency interval, and the interpolation step size of the third frequency interval; wherein, the upper limit of the first frequency interval is less than the lower limit of the second frequency interval; and the upper limit of the second frequency interval is less than the lower limit of the third frequency interval.

[0092] In this scheme, Perlin noise is used in the low-frequency layer to optimize the macroscopic landform outline; Worley-Perlin hybrid noise is used in the mid-frequency layer to simulate topographic features such as erosion gullies. The hybrid method adopts linear superposition with a weight ratio of Perlin=0.7 and Worley=0.3, with Perlin dominating; OpenSimplex2 noise is used in the high-frequency layer to highlight the surface microstructure.

[0093] The low-frequency layer corresponds to the first frequency range; the mid-frequency layer corresponds to the second frequency range; and the high-frequency layer corresponds to the third frequency range. The upper limit of the first frequency range is less than the lower limit of the second frequency range; and the upper limit of the second frequency range is less than the lower limit of the third frequency range.

[0094] In this embodiment, the interpolation step size of the first frequency range is: step_low = min(10m, DEM resolution / 10), which is the smaller value between 10m and DEM resolution / 10. The first frequency range is 0.1-1Hz.

[0095] The interpolation step size for the third frequency range is: step_high = min(0.2m, texture image resolution (if it exists)), which is the smaller value between 0.2m and the texture image resolution. If there is no corresponding texture image, 0.2m is used by default. The third frequency range is greater than 5Hz.

[0096] In this embodiment, the interpolation step size for the second frequency range is: step_mid = step_low × step_high × 3.16. The second frequency range is 1-5Hz, and this value is calculated from the determined low-frequency layer and high-frequency layer.

[0097] This solution primarily addresses the issue of missing details when generating 3D terrain from low-precision DEMs, especially for scenarios where the corresponding area already has high-precision texture images, which can easily lead to a disconnect between terrain and texture representation. Therefore, it is not suitable for DEMs with a resolution of less than 10m.

[0098] S340. Calculate the initial noise of the first frequency range based on the interpolation step size of the first frequency range; calculate the initial noise of the second frequency range based on the interpolation step size of the second frequency range; and calculate the initial noise of the third frequency range based on the interpolation step size of the third frequency range.

[0099] In this embodiment, the digital elevation model data is traversed, and the noise interpolation of the first frequency interval is calculated based on the interpolation step size of the first frequency interval; the noise interpolation of the second frequency interval is calculated based on the interpolation step size of the second frequency interval; and the noise interpolation of the third frequency interval is calculated based on the interpolation step size of the third frequency interval.

[0100] Specifically, the digital elevation model data obtained by traversal is interpolated based on the interpolation step size of the first frequency interval to obtain the interpolated elevation data; the difference between the original elevation data and the interpolated elevation data is calculated to obtain the noise interpolation of the first frequency interval.

[0101] Specifically, the digital elevation model data obtained through traversal is interpolated based on the interpolation step size of the second frequency interval to obtain the interpolated elevation data; the difference between the original elevation data and the interpolated elevation data is calculated to obtain the noise interpolation of the second frequency interval.

[0102] In this embodiment, the digital elevation model data obtained by traversal is interpolated based on the interpolation step size of the third frequency interval to obtain the interpolated elevation data; the difference between the original elevation data and the interpolated elevation data is calculated to obtain the noise interpolation of the third frequency interval.

[0103] Furthermore, based on the noise interpolation of the first frequency range, the initial noise of the first frequency range is calculated; and based on the noise interpolation of the second frequency range, the initial noise of the second frequency range is calculated; and based on the noise interpolation of the third frequency range, the initial noise of the third frequency range is calculated.

[0104] Specifically, the continuous noise distribution of the first frequency range is obtained by interpolating the noise in the first frequency range; and the initial noise value of the first frequency range is determined based on the mean, median or peak value of the continuous noise distribution.

[0105] In this embodiment, the continuous noise distribution of the second frequency range is obtained by noise interpolation in the second frequency range; the initial noise value of the second frequency range is determined based on the mean, median or peak value of the continuous noise distribution.

[0106] In this scheme, the continuous noise distribution of the third frequency range is obtained by interpolating the noise in the third frequency range; the initial noise value of the third frequency range is determined based on the mean, median or peak value of the continuous noise distribution.

[0107] S350. Combine the initial noise in the first frequency range with the terrain elevation feature to generate target noise in the first frequency range; and combine the initial noise in the second frequency range with the terrain elevation feature to generate target noise in the second frequency range; and combine the initial noise in the third frequency range with the terrain elevation feature to generate target noise in the third frequency range.

[0108] In this scheme, low-frequency layer noise is easily affected by terrain slope. Therefore, the initial noise in the first frequency range is combined with the slope in the terrain elevation characteristics to obtain the target noise in the first frequency range.

[0109] In this embodiment, the mid-frequency layer noise is stretched in the flow direction of the three records. Therefore, the initial noise in the second frequency range is combined with the flow direction trend in the terrain elevation features to generate the target noise in the second frequency range.

[0110] Furthermore, the high-frequency layer noise is controlled by both curvature and slope. Therefore, the initial noise in the third frequency range is combined with the curvature and slope in the terrain elevation features to generate the target noise in the third frequency range.

[0111] Optionally, the initial noise in the first frequency range is combined with the terrain elevation features to generate target noise in the first frequency range, including:

[0112] Based on the slope in the terrain elevation features, determine the slope coefficient corresponding to the noise in the first frequency range.

[0113] The initial noise in the first frequency range is multiplied by the slope coefficient to obtain the target noise in the first frequency range.

[0114] In this scheme, the noise intensity of the low-frequency layer is affected by the slope. That is, the Perlin noise is calculated for each point in the low-frequency layer and then multiplied by the slope coefficient. The slope coefficient is obtained by multiplying the slope in the terrain elevation feature by 0.5 and is limited to the range [0.1,1].

[0115] Specifically, the target noise in the first frequency range is calculated using the following formula:

[0116] ;

[0117] in, For the target noise in the first frequency range, This represents the initial noise in the first frequency range.

[0118] By combining terrain slope features with noise generation, more realistic and natural terrain simulations can be achieved. The introduction of a slope coefficient allows the noise to adaptively adjust according to terrain features, thereby generating a terrain model that better conforms to natural laws.

[0119] Optionally, the initial noise in the second frequency range is combined with the terrain elevation features to generate the target noise in the second frequency range, including:

[0120] The initial noise in the second frequency range is combined with the flow direction trend in the terrain elevation features to generate the target noise in the second frequency range.

[0121] In this scheme, for mid-frequency layer noise, along the three recording directions (i.e., the three direction angles calibrated in the D8-3 improved algorithm, denoted as flow_angle),... The process involves stretching the sampled data. First, a matrix is ​​constructed based on these three directional angles, and the average value is taken. Then, a matrix multiplication operation is performed with the mid-frequency layer coordinates to correct the influence of water flow on the coordinate direction characteristics. Finally, based on the corrected sampling points, the Worley-Perlin mixed noise calculation is completed.

[0122] Specifically, the target noise in the second frequency range is calculated using the following formula;

[0123] ;

[0124] ;

[0125] in, The corrected coordinates of the sampling points. , These are the original sampling point coordinates. , , For flow trend, The target noise is in the second frequency range.

[0126] By weighting and fusing the two types of noise, the mid-frequency layer retains the natural smoothness of Perlin noise while incorporating the graininess and blocky details of Worley noise, thereby generating richer texture features.

[0127] Optionally, the initial noise in the third frequency range is combined with the terrain elevation features to generate the target noise in the third frequency range, including:

[0128] The mask value is determined based on the slope and curvature in the terrain elevation features; wherein the mask value is used to adjust the noise intensity in the third frequency range;

[0129] The initial noise in the third frequency range is combined with the mask value to generate the target noise in the third frequency range.

[0130] In this scheme, the activation state of the high-frequency layer is controlled by both curvature and slope. A mask is used to record the selected feature points with curvature ≥ 0.4 and slope < 1.047, which are the details of relatively flat areas. These points are compressed to 0.02 times after noise calculation using OpenSimplex2 to represent the terrain details without causing a large load.

[0131] Specifically, the target noise in the third frequency range is calculated using the following formula;

[0132] ;

[0133] ;

[0134] in, For mask value, For the target noise in the third frequency range, This represents the initial noise in the third frequency range.

[0135] By combining the slope and curvature characteristics of the terrain elevation to determine the mask value, the noise intensity in the third frequency range is adjusted in a targeted manner. This achieves precise adaptation of noise adjustment to terrain features, retaining effective components in the third frequency range noise that conform to the actual terrain while suppressing redundant noise that does not match the terrain elevation characteristics. This effectively improves the rationality and fit of noise generation, providing more accurate noise data support for subsequent terrain-based data processing.

[0136] S360: Obtain basic terrain data.

[0137] In this scheme, basic topographic data refers to fundamental geographic information data describing the topographic and geomorphological features of the Earth's surface. Basic topographic data is obtained from a database.

[0138] S370. The multi-scale noise is fused with the basic terrain data to generate terrain elevation results.

[0139] In this embodiment, the terrain elevation result is obtained by weighted summation of multi-scale noise and basic terrain data. The terrain elevation result is calculated using the following formula;

[0140] ; ;

[0141] in, For topographic elevation results, Based on basic terrain data, As weight, For multi-scale noise, ; ; .

[0142] S380. Render the terrain elevation results to construct a digital elevation three-dimensional terrain.

[0143] The technical solution of this invention involves: acquiring digital elevation model (DEM) data; extracting features from the DEM data to obtain terrain elevation features; calculating initial noise in a first frequency interval based on an interpolation step size; calculating initial noise in a second frequency interval based on an interpolation step size; calculating initial noise in a third frequency interval based on an interpolation step size; combining the initial noise in the first frequency interval with the terrain elevation features to generate target noise in the first frequency interval; combining the initial noise in the second frequency interval with the terrain elevation features to generate target noise in the second frequency interval; and combining the initial noise in the third frequency interval with the terrain elevation features to generate target noise in the third frequency interval. It also involves acquiring basic terrain data; fusing multi-scale noise with the basic terrain data to generate terrain elevation results; and rendering the terrain elevation results to construct a three-dimensional digital elevation terrain. By implementing this technical solution, terrain detail enhancement can be performed efficiently, at low cost, safely, and stably, significantly improving the visual performance of the three-dimensional digital elevation terrain.

[0144] In this solution, the application scenario is to improve the performance quality of L1 level wide-area terrain in the process of building CIM (City Information Model) scenarios, so that the data at different levels have consistency in performance, and it is feasible in terms of efficiency, economy and security.

[0145] Example 3

[0146] Figure 4 This is a schematic diagram of a digital elevation three-dimensional terrain enhancement device provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes:

[0147] Digital elevation model data acquisition module 410 is used to acquire digital elevation model data;

[0148] The terrain elevation feature acquisition module 420 is used to extract features from the digital elevation model data to obtain terrain elevation features;

[0149] The multi-scale noise generation module 430 is used to generate multi-scale noise based on the terrain elevation features; wherein the multi-scale noise is composed of noise in multiple different frequency ranges.

[0150] The terrain elevation result generation module 440 is used to fuse the multi-scale noise to generate terrain elevation results.

[0151] The digital elevation 3D terrain construction module 450 is used to render the terrain elevation results and construct digital elevation 3D terrain.

[0152] Optionally, the terrain elevation feature includes slope; the terrain elevation feature acquisition module 420 is specifically used for:

[0153] Based on the height value and longitude dimension of each pixel in the digital elevation model data, the partial derivative value of each pixel in the longitude direction is calculated; and, using the height value and latitude dimension of each pixel in the digital elevation model data, the partial derivative value of each pixel in the latitude direction is calculated.

[0154] The slope is obtained by calculating the partial derivative values ​​of each pixel in the longitude direction and the partial derivative values ​​of each pixel in the latitude direction.

[0155] Optionally, the terrain elevation feature includes curvature; the terrain elevation feature obtaining module 420 is specifically used for:

[0156] Calculate the first-order and second-order partial derivatives of the height values ​​of each pixel in the digital elevation model data, and combine the first-order and second-order partial derivatives to obtain the curvature.

[0157] Optionally, the terrain elevation features include flow direction trends; the flow direction trends are used to characterize the flow direction along the elevation gradient; the terrain elevation feature acquisition module 420 is specifically used for:

[0158] For each pixel in the digital elevation model data, determine the first neighborhood corresponding to each pixel;

[0159] The first neighborhood is divided according to a preset partitioning rule to obtain each second neighborhood;

[0160] Using the height values ​​of the pixels in each second neighborhood, the flow trends are calculated; wherein, the flow trends are characterized by direction angles.

[0161] Optional, the multi-scale noise generation module 430 includes:

[0162] The interpolation step size acquisition submodule is used to acquire the interpolation step size of the first frequency range, the interpolation step size of the second frequency range, and the interpolation step size of the third frequency range; wherein, the upper limit of the first frequency range is less than the lower limit of the second frequency range; and the upper limit of the second frequency range is less than the lower limit of the third frequency range.

[0163] An initial noise calculation submodule is used to calculate the initial noise of a first frequency range based on the interpolation step size of the first frequency range; and to calculate the initial noise of a second frequency range based on the interpolation step size of the second frequency range; and to calculate the initial noise of a third frequency range based on the interpolation step size of the third frequency range.

[0164] The target noise generation submodule is used to perform a combination operation on the initial noise in the first frequency range and the terrain elevation feature to generate target noise in the first frequency range; and to perform a combination operation on the initial noise in the second frequency range and the terrain elevation feature to generate target noise in the second frequency range; and to perform a combination operation on the initial noise in the third frequency range and the terrain elevation feature to generate target noise in the third frequency range.

[0165] Optional, the target noise generation submodule is specifically used for:

[0166] Based on the slope in the terrain elevation features, determine the slope coefficient corresponding to the noise in the first frequency range.

[0167] The initial noise in the first frequency range is multiplied by the slope coefficient to obtain the target noise in the first frequency range.

[0168] Optional, the target noise generation submodule is specifically used for:

[0169] The initial noise in the second frequency range is combined with the flow direction trend in the terrain elevation features to generate the target noise in the second frequency range.

[0170] Optional, the target noise generation submodule is specifically used for:

[0171] The mask value is determined based on the slope and curvature in the terrain elevation features; wherein the mask value is used to adjust the noise intensity in the third frequency range;

[0172] The initial noise in the third frequency range is combined with the mask value to generate the target noise in the third frequency range.

[0173] Optional, the terrain elevation result generation module 440 is specifically used for:

[0174] Obtain basic terrain data;

[0175] The multi-scale noise is fused with the basic terrain data to generate terrain elevation results.

[0176] The digital elevation three-dimensional terrain enhancement device provided in this embodiment of the invention can execute the digital elevation three-dimensional terrain enhancement method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0177] Example 4

[0178] Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0179] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0180] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0181] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a digital elevation 3D terrain enhancement method.

[0182] In some embodiments, a digital elevation 3D terrain enhancement method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the digital elevation 3D terrain enhancement method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a digital elevation 3D terrain enhancement method by any other suitable means (e.g., by means of firmware).

[0183] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0184] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0185] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0186] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0187] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0188] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0189] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0190] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0191] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for digital elevation three-dimensional terrain enhancement, characterized by, The method comprises: obtaining digital elevation model data; performing feature extraction on the digital elevation model data to obtain terrain elevation features; generating multi-scale noise according to the terrain elevation features; wherein the multi-scale noise is composed of noise in multiple different frequency intervals; performing fusion processing on the multi-scale noise to generate terrain elevation results; rendering the terrain elevation results to construct a digital elevation three-dimensional terrain.

2. The method of claim 1, wherein, The terrain elevation features include slope; performing feature extraction on the digital elevation model data to obtain terrain elevation features comprises: calculating the partial derivative values of each pixel in the longitude direction according to the height values and the size in the longitude direction of each pixel in the digital elevation model data; and calculating the partial derivative values of each pixel in the latitude direction using the height values and the size in the latitude direction of each pixel in the digital elevation model data; calculating the slope by calculating the partial derivative values of each pixel in the longitude direction and the partial derivative values of each pixel in the latitude direction.

3. The method of claim 1, wherein, The terrain elevation features include curvature; performing feature extraction on the digital elevation model data to obtain terrain elevation features comprises: calculating the first-order and second-order partial derivative values corresponding to the height values of each pixel in the digital elevation model data, and performing combined operations on the first-order and second-order partial derivative values to obtain the curvature.

4. The method of claim 1, wherein, The terrain elevation features include flow direction trends; The flow direction trends are used to represent the flow direction along the elevation gradient; performing feature extraction on the digital elevation model data to obtain terrain elevation features comprises: determining a first neighborhood corresponding to each pixel in the digital elevation model data for each pixel; dividing the first neighborhood according to a preset division rule to obtain second neighborhoods; calculating the flow direction trends using the height values of the pixels in the second neighborhoods; wherein the flow direction trends are represented by direction angles.

5. The method of claim 1, wherein, Generating multi-scale noise according to the terrain elevation features comprises: obtaining an interpolation step for a first frequency interval, an interpolation step for a second frequency interval, and an interpolation step for a third frequency interval; wherein the upper limit value of the first frequency interval is less than the lower limit value of the second frequency interval; the upper limit value of the second frequency interval is less than the lower limit value of the third frequency interval; calculating initial noise in the first frequency interval according to the interpolation step for the first frequency interval, calculating initial noise in the second frequency interval according to the interpolation step for the second frequency interval, and calculating initial noise in the third frequency interval according to the interpolation step for the third frequency interval; performing combined operations on the initial noise in the first frequency interval and the terrain elevation features to generate target noise in the first frequency interval, performing combined operations on the initial noise in the second frequency interval and the terrain elevation features to generate target noise in the second frequency interval, and performing combined operations on the initial noise in the third frequency interval and the terrain elevation features to generate target noise in the third frequency interval.

6. The method of claim 5, wherein, Performing combined operations on the initial noise in the first frequency interval and the terrain elevation features to generate target noise in the first frequency interval comprises: Determine a slope coefficient corresponding to the first frequency interval noise based on the slope in the terrain elevation feature; Multiply the initial noise of the first frequency interval by the slope coefficient to obtain the target noise of the first frequency interval.

7. The method of claim 5, wherein, Combine the initial noise of the second frequency interval with the terrain elevation feature to generate the target noise of the second frequency interval, including: Combine the initial noise of the second frequency interval with the flow trend in the terrain elevation feature to generate the target noise of the second frequency interval.

8. The method of claim 5, wherein, Combine the initial noise of the third frequency interval with the terrain elevation feature to generate the target noise of the third frequency interval, including: Determine a mask value according to the slope and curvature in the terrain elevation feature; wherein the mask value is used to adjust the noise intensity of the third frequency interval; Combine the initial noise of the third frequency interval with the mask value to generate the target noise of the third frequency interval.

9. The method of claim 1, wherein, Fuse the multi-scale noise to generate a terrain elevation result, including: Obtain basic terrain data; Fuse the multi-scale noise with the basic terrain data to generate a terrain elevation result.

10. A digital elevation three-dimensional terrain enhancement device, characterized by, Including: A digital elevation model data acquisition module for obtaining digital elevation model data; A terrain elevation feature obtaining module for feature extraction on the digital elevation model data to obtain a terrain elevation feature; A multi-scale noise generation module for generating multi-scale noise according to the terrain elevation feature; wherein the multi-scale noise is composed of noise of multiple different frequency intervals; A terrain elevation result generation module for fusing the multi-scale noise to generate a terrain elevation result; A digital elevation three-dimensional terrain construction module for rendering the terrain elevation result to construct a digital elevation three-dimensional terrain.

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