A ridge line extraction method, system, device and medium reflecting topographic features

Abstract

This invention provides a method, system, device, and medium for extracting ridgelines reflecting topographic features. The method includes the following steps: S1, extracting elevation data from a digital elevation model of the actual terrain within the study area; S2, resampling the elevation data with double the precision and arranging it into a matrix containing only elevation data; S3, performing wavelet decomposition on the elevation data matrix using different wavelet functions and decomposition levels, and reconstructing the low-frequency terms in the wavelet decomposition results; S4, comprehensively evaluating the low-frequency term reconstruction results based on signal reconstruction error and noise reduction level, and determining the optimal wavelet function and decomposition level; S5, filtering and denoising the elevation data matrix using the optimal wavelet function and decomposition level; S6, extracting topographic ridgelines from the denoised elevation data matrix using an eight-neighborhood relative elevation discrimination method. The method provided by this invention is applicable to better understanding mountainous terrain in the design of mountain transmission lines.

Description

Technical Field

[0001] This invention belongs to the field of digital terrain analysis technology, and in particular relates to a method, system, device and medium for extracting ridgelines that reflect landform features. Background Technology

[0002] The construction of power transmission lines in mountainous areas is becoming increasingly common. However, certain terrain features can accelerate flow fields, threatening the safe operation of power transmission lines. Therefore, it is essential to pay close attention to geomorphological characteristics. However, the winding and complex nature of real terrain, with its numerous local details, makes it difficult for designers to accurately grasp the shape and orientation of the actual terrain in engineering practice. Therefore, it is necessary to extract ridgelines to reflect these geomorphological features, which helps enhance designers' understanding of mountainous terrain.

[0003] From a hydrological perspective, ridgeline extraction is essentially the extraction of watersheds. Watersheds, as the starting point of water flow, have zero runoff accumulation. By calculating surface runoff and extracting the zero-value runoff accumulation raster, the watershed, i.e., the ridgeline in a hydrological sense, can be obtained. However, this method is mainly suitable for large-scale topographic analysis; it may struggle to accurately describe the details of small-scale topography. Furthermore, simply analyzing areas with zero runoff accumulation can easily lead to missing or discontinuous ridgelines.

[0004] Another method, 3D topographic surface flow digital simulation, uses a reverse DEM model to more easily identify ridgelines in the terrain. A drainage network is created based on the accumulated flow rate, and drainage networks with higher flow rates are selected as ridgelines by setting flow rate values. However, this method primarily relies on the hydrological analysis module provided by ArcGIS software, limiting its flexibility. Furthermore, the flow rate needs continuous adjustment during use to highlight the ridgelines required for the study. When a dense ridgeline network is needed, a lower flow rate value can be set, but the primary and secondary ridgelines become mixed, requiring further labeling and identification. When only the primary ridgeline network is needed, a higher flow rate value can be set, but the ridgelines are often broken, requiring refinement based on the terrain. Summary of the Invention

[0005] The first objective of this invention is to address the shortcomings of existing technologies by providing a method for extracting ridgelines that reflects landform features. This method aims to simplify terrain elevation information, highlight the shape and orientation of terrain features, and improve the recognizability between different terrains.

[0006] Therefore, the above-mentioned objective of the present invention is achieved through the following technical solution:

[0007] A method for extracting ridgelines that reflect landform features mainly includes the following steps:

[0008] S1. Determine the mountainous area to be studied and extract the elevation data of the digital elevation model (DEM) of the actual terrain within the mountainous area to be studied;

[0009] S2. Resample the DEM elevation data from step S1 with twice the precision and arrange them into a matrix containing only elevation data.

[0010] S3. Using different wavelet functions and decomposition levels, perform wavelet decomposition on the elevation data matrix in step S2, and reconstruct the low-frequency terms in the wavelet decomposition results.

[0011] S4. Based on the signal reconstruction error and the degree of noise reduction, comprehensively evaluate the reconstruction results of the low-frequency term in step S3, and determine the optimal wavelet function and the number of decomposition layers used in wavelet decomposition.

[0012] S5. Using the optimal wavelet function and decomposition level from step S4, filter and reduce noise in the elevation data matrix from step S2.

[0013] S6. Using the eight-neighbor relative elevation discrimination method, extract the terrain ridgeline from the denoised elevation data matrix in step S5.

[0014] While adopting the above technical solutions, the present invention may also adopt or combine the following technical solutions:

[0015] As a preferred technical solution of the present invention: In step S2, according to the task requirements, the elevation data matrix of the entire region is decomposed into sub-regions of uniform size, and the ridgeline is extracted in the sub-regions.

[0016] As a preferred technical solution of the present invention: in step S3, the elevation data matrix is ​​decomposed in both horizontal and vertical directions using two-dimensional discrete wavelet transform.

[0017] As a preferred embodiment of the present invention: In step S4, reconstruction error and noise reduction degree are important indicators for measuring the success of terrain noise reduction. The reconstruction error d is measured by the chamfer distance. CD The calculation formula is as follows:

[0018]

[0019] In the formula, S1 and S2 represent the elevation point clouds before and after reconstruction, respectively; the first term The second term represents the average of the sum of the least squared distances from any point in the pre-reconstruction elevation point cloud S1 to the post-reconstruction elevation point cloud S2. This represents the average of the sum of the least squared distances from any point in the reconstructed elevation point cloud S2 to the original elevation point cloud S1.

[0020] Secondly, the terrain roughness index (TRI) is used to measure the degree of noise reduction to prevent incomplete terrain noise reduction due to too small a reconstruction error. The calculation formula is as follows:

[0021]

[0022] In the formula: N is the window length, Z0 is the elevation of the center position, and Z k This represents the elevation of the nearby location.

[0023] As a preferred technical solution of the present invention: In step S4, based on the primary denoising criterion of reconstruction error, the db8 wavelet is selected as the optimal wavelet function for terrain denoising; when the denoising degree and reconstruction error reach a balance, the optimal number of wavelet decompositions can be determined; it should be noted that the optimal number of wavelet decompositions is related to the size of the analysis area in step S2.

[0024] As a preferred technical solution of the present invention: In step S6, the relative elevation of the eight neighboring areas around the center point is determined. When at least five neighboring areas are lower than the center point, the center point can be confirmed as the ridgeline position.

[0025] The second objective of this invention is to provide a ridgeline extraction system that reflects landform features, comprising the following modules:

[0026] - Elevation data extraction module, which is used to extract digital elevation model elevation data of the real terrain within the determined study mountain area based on the determined study mountain area;

[0027] - Matrix arrangement module, which is used to resample the elevation data extracted by the elevation data extraction module with multiple times the precision and arrange it into a matrix containing only elevation data;

[0028] - Low-frequency term reconstruction module, which is used to perform wavelet decomposition on the matrix of elevation data arranged by the matrix arrangement module using different wavelet functions and decomposition levels, and reconstruct the low-frequency terms in the wavelet decomposition results;

[0029] - Optimal wavelet function and decomposition level determination module, which is used to comprehensively evaluate the low-frequency term reconstruction results of the low-frequency term reconstruction module based on the signal reconstruction error and the degree of noise reduction, and determine the optimal wavelet function and decomposition level used for wavelet decomposition.

[0030] - Filtering and noise reduction module, which is used to filter and reduce noise on the matrix of elevation data arranged by the matrix arrangement module by using the optimal wavelet function and the optimal decomposition level determination module.

[0031] - Terrain ridgeline extraction module, which uses an eight-neighbor relative elevation discrimination method to extract terrain ridgelines from the elevation data matrix after denoising by the filtering and denoising module.

[0032] A third objective of this invention is to provide an electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, characterized in that:

[0033] The memory, which is used to store computer programs,

[0034] A processor for executing a computer program stored in memory to implement the steps of the ridgeline extraction method reflecting topographic features as described above.

[0035] Another objective of this invention is to provide a computer-readable storage medium, characterized in that: the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the ridgeline extraction method steps reflecting landform features as described above.

[0036] This invention provides a method, system, device, and medium for extracting ridgelines that reflect landform features. The aim is to simplify terrain elevation information and highlight features such as shape and orientation to improve terrain recognizability. The method performs wavelet decomposition on the resampled elevation data matrix and reconstructs the low-frequency terms. The reconstruction results are evaluated by signal reconstruction error and noise reduction level to determine the optimal wavelet function and decomposition level. The optimal wavelet function and decomposition level are then used to filter and reduce noise in the elevation data matrix. Finally, the eight-neighborhood relative elevation discrimination method is used to extract the terrain ridgelines.

[0037] Compared with existing technologies, it has the following beneficial effects:

[0038] (1) The method provided by the present invention is applicable to the design of mountain transmission lines to better understand the mountainous terrain.

[0039] (2) Wavelet decomposition, especially two-dimensional discrete wavelet decomposition, is used to perform high-frequency filtering on real complex terrain elevation data, filtering out the details represented by high-frequency terms and reconstructing the low-frequency trend terms, thereby achieving the effect of denoising terrain details and highlighting the main shape of the terrain.

[0040] (3) After denoising the DEM elevation data of the real terrain, the continuous ridgeline of the terrain is further extracted to highlight the main features of the terrain and improve the terrain identification. Attached Figure Description

[0041] Figure 1This is a flowchart of the ridgeline extraction method reflecting landform features provided by the present invention.

[0042] Figure 2 This is an elevation image of a 5km × 5km terrain.

[0043] Figure 3 This is a schematic diagram of the principle of two-dimensional discrete wavelet decomposition.

[0044] Figure 4a This is a plot of the reconstruction error of the low-frequency term in the Db N wavelet decomposition.

[0045] Figure 4b This is a plot of the reconstruction error of the low-frequency term in the Sym N wavelet decomposition.

[0046] Figure 4c The reconstruction error diagram for the low-frequency term of the CoifN wavelet decomposition.

[0047] Figure 4d This is a plot of the reconstruction error of the low-frequency term in the Bior N.M wavelet decomposition.

[0048] Figure 5 Reconstruction error diagram of low-frequency term for different wavelet functions at different decomposition levels.

[0049] Figure 6 This is a diagram showing the combined impact of the number of decomposition layers on the reconstruction results of low-frequency terms.

[0050] Figure 7 Reconstruct the elevation image from the low-frequency terms of the 5km×5km terrain after five decompositions.

[0051] Figure 8 This is a schematic diagram of the principle of the eight-neighbor relative elevation discrimination method.

[0052] Figure 9 The image shows the ridgeline extraction result before wavelet denoising of a 5km×5km terrain.

[0053] Figure 10 The image shows the ridgeline extraction result after wavelet denoising of a 5km×5km terrain. Detailed Implementation

[0054] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0055] As described in the background section, existing technologies commonly employ hydrological methods for ridgeline extraction, which easily leads to missing or discontinuous ridgelines, resulting in insufficient highlighting of key geomorphic features. To address these technical problems, this invention provides a ridgeline extraction method that reflects geomorphic features, the flowchart of which is shown below. Figure 1 As shown.

[0056] The study area is a mountainous region. Digital elevation model (DEM) elevation data of the actual terrain within the study area is extracted. The data comes from the ASTER dataset.

[0057] Based on the research object, the elevation data is resampled with twice the precision and arranged into a matrix containing only elevation data. Then, according to task requirements, the elevation data matrix of the entire region can be divided into 5km × 5km sections, with windows sliding horizontally and vertically in 2km increments, decomposing it into sub-regions of uniform size. The elevation image of any 5km × 5km terrain is shown below. Figure 2 As shown.

[0058] Multi-scale two-dimensional discrete wavelet decomposition is performed on the elevation data matrix of the sub-region using different wavelet functions and decomposition levels. The signal is passed through low-pass and high-pass filters associated with the wavelet basis, dividing the signal frequency into two equal parts. Each time, only the low-frequency part is further decomposed, and the low-frequency terms in the wavelet decomposition result are reconstructed. The decomposition process based on the Mallat pyramid algorithm is as follows: Figure 3 As shown. Topographic elevation data is an approximate representation of the Earth's surface by assigning height values ​​to regularly distributed raster cells on a two-dimensional plane. Its scale subspace V j and wavelet subspace W j The orthogonal decomposition in Hilbert space can be represented as follows:

[0059]

[0060] In the formula: V j-1 These are approximate coefficients. For horizontal detail coefficients, This is the vertical detail factor. This refers to the diagonal detail factor; This represents the direct sum operation of vectors in two orthogonal subspaces.

[0061] Furthermore, the 2-scale two-dimensional discrete wavelet decomposition can be expressed as a further decomposition of the approximation term on the right-hand side of the above equation. The multi-scale two-dimensional discrete wavelet decomposition follows the same logic. The 2-scale two-dimensional discrete wavelet orthogonal decomposition can be expressed as follows:

[0062]

[0063] Reconstruction error and noise reduction level are important indicators for evaluating the success of terrain noise reduction. Reconstruction error is measured using chamfer distance, and its calculation formula is as follows:

[0064]

[0065] In the formula, S1 and S2 represent the elevation point clouds before and after reconstruction, respectively; the first term The second term represents the average of the sum of the least squared distances from any point in the pre-reconstruction elevation point cloud S1 to the post-reconstruction elevation point cloud S2. This represents the average of the sum of the least squared distances from any point in the reconstructed elevation point cloud S2 to the original elevation point cloud S1.

[0066] Furthermore, the terrain roughness index is used to measure the degree of noise reduction, which can prevent the reconstruction error from being too small and the terrain noise reduction from being incomplete. The calculation formula is as follows:

[0067]

[0068] In the formula: N is the window length, Z0 is the elevation of the center position, and Z k This represents the elevation of the nearby location.

[0069] right Figure 2 The terrain shown is subjected to two-dimensional discrete wavelet decomposition using different wavelet functions. The reconstruction error results for the low-frequency term are shown in [the table / reference needed]. Figures 4a to 4d As shown in the figure, the reconstruction error distributions of the approximate terms for different wavelet functions exhibit similarities, all decreasing rapidly with increasing vanishing distance and then stabilizing. Based on these error distribution characteristics, db8, sym7, coif4, and bior3.5 are selected as the optimal candidate wavelet functions. Figure 5 It can be seen that the reconstruction results of different wavelet functions are very similar, and the reconstruction errors of the approximate terms are very close at different decomposition levels. Considering that the db8 wavelet has stronger bioorthogonality and symmetry, it is suitable for signal compression and denoising. Based on the reconstruction error as the primary denoising criterion, the db8 wavelet is selected as the optimal wavelet function for terrain denoising.

[0070] Using db8 wavelet function to Figure 2 The terrain elevations shown are decomposed into multiple layers, and the approximations are reconstructed. From Figure 6 It can be seen that when the degree of noise reduction (roughness index) and the reconstruction error (bevel distance) are balanced, the optimal wavelet decomposition order can be determined.

[0071] In summary, the db8 wavelet function is used to... Figure 2 The 5km × 5km terrain was decomposed five times, and the results are as follows: Figure 7 As shown.

[0072] Furthermore, an eight-neighbor relative elevation discrimination method is employed to extract topographic ridgelines from the elevation submatrix one by one. The algorithm principle diagram is shown below. Figure 8 As shown. For Figure 2 Ridge lines were extracted from the original terrain, and the results are shown in [the image / document]. Figure 9 .right Figure 7 After denoising, ridgelines were extracted from the terrain. The results are shown in [the original text]. Figure 10 .Depend on Figure 9 and Figure 10 It can be seen that the ridgeline extraction results after wavelet filtering and denoising are more continuous and concise, and the location and shape of the terrain can be obtained intuitively, highlighting the main features of the terrain. The results show that the terrain features after denoising are more distinct, and different terrains have a high degree of recognition.

[0073] The present invention also provides a ridgeline extraction system reflecting landform features, comprising the following modules:

[0074] - Elevation data extraction module, which is used to extract digital elevation model elevation data of the real terrain within the determined study mountain area based on the determined study mountain area;

[0075] - Matrix arrangement module, which is used to resample the elevation data extracted by the elevation data extraction module with multiple times the precision and arrange it into a matrix containing only elevation data;

[0076] - Low-frequency term reconstruction module, which is used to perform wavelet decomposition on the matrix of elevation data arranged by the matrix arrangement module using different wavelet functions and decomposition levels, and reconstruct the low-frequency terms in the wavelet decomposition results;

[0077] - Optimal wavelet function and decomposition level determination module, which is used to comprehensively evaluate the low-frequency term reconstruction results of the low-frequency term reconstruction module based on the signal reconstruction error and the degree of noise reduction, and determine the optimal wavelet function and decomposition level used for wavelet decomposition.

[0078] - Filtering and noise reduction module, which is used to filter and reduce noise on the matrix of elevation data arranged by the matrix arrangement module by using the optimal wavelet function and the optimal decomposition level determination module.

[0079] - Terrain ridgeline extraction module, which uses an eight-neighbor relative elevation discrimination method to extract terrain ridgelines from the elevation data matrix after denoising by the filtering and denoising module.

[0080] The present invention also provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other through the communication bus. The memory is used to store computer programs. The processor is used to execute the computer programs stored in the memory to implement the steps of the ridgeline extraction method reflecting landform features as described above.

[0081] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the ridgeline extraction method reflecting landform features as described above.

[0082] The above specific embodiments are used to explain and illustrate the present invention, and are only preferred embodiments of the present invention, not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. A method for extracting ridgelines reflecting landform features, characterized in that: The method includes the following steps: S1. Determine the mountainous area to be studied and extract the elevation data of the digital elevation model of the actual terrain within the mountainous area to be studied; S2. Resample the elevation data from step S1 with twice the precision and arrange them into a matrix containing only elevation data. S3. Using different wavelet functions and decomposition levels, perform wavelet decomposition on the elevation data matrix in step S2, and reconstruct the low-frequency terms in the wavelet decomposition results. S4. Based on the signal reconstruction error and the degree of noise reduction, comprehensively evaluate the reconstruction results of the low-frequency term in step S3, and determine the optimal wavelet function and the number of decomposition layers used in wavelet decomposition. S5. Using the optimal wavelet function and decomposition level from step S4, filter and reduce noise in the matrix of elevation data from step S2. S6. Using the eight-neighbor relative elevation discrimination method, extract the terrain ridgeline from the denoised elevation data matrix in step S5. In step S4, reconstruction error and noise reduction level are important indicators for evaluating the success of terrain noise reduction. The reconstruction error d is measured using the chamfer distance. CD The calculation formula is as follows: In the formula, S1 and S2 represent the elevation point clouds before and after reconstruction, respectively; the first term The second term represents the average of the sum of the least squared distances from any point in the pre-reconstruction elevation point cloud S1 to the post-reconstruction elevation point cloud S2. This represents the average of the sum of the least squared distances from any point in the reconstructed elevation point cloud S2 to the original elevation point cloud S1. Secondly, the terrain roughness index (TRI) is used to measure the degree of noise reduction to prevent incomplete terrain noise reduction due to too small a reconstruction error. The calculation formula is as follows: In the formula: N is the window length, Z0 is the elevation of the center position, and Z k This represents the elevation of the nearby location.

2. The method for extracting ridgelines reflecting landform features according to claim 1, characterized in that: In step S2, according to the task requirements, the elevation data matrix of the entire region is decomposed into sub-regions of uniform size, and ridgeline extraction is performed in the sub-regions.

3. The method for extracting ridgelines reflecting landform features according to claim 1, characterized in that: In step S3, the elevation data matrix is ​​decomposed in both horizontal and vertical directions using two-dimensional discrete wavelet transform.

4. The method for extracting ridgelines reflecting landform features according to claim 1, characterized in that: In step S4, based on the primary denoising criterion of reconstruction error, the db8 wavelet is selected as the optimal wavelet function for terrain denoising. When the denoising degree and reconstruction error reach a balance, the optimal number of wavelet decompositions can be determined. It should be noted that the optimal number of wavelet decompositions is related to the size of the analysis area in step S2.

5. The method for extracting ridgelines reflecting landform features according to claim 1, characterized in that: In step S6, the relative elevations of the eight neighboring areas around the center point are determined. When at least five neighboring areas are lower than the center point, the center point can be confirmed as the ridgeline location.

6. A ridgeline extraction system reflecting geomorphic features, characterized in that: The system is based on the ridgeline extraction method reflecting landform features as described in claim 1, and includes the following modules: - Elevation data extraction module, which is used to extract digital elevation model elevation data of the real terrain within the determined study mountain area based on the determined study mountain area; - Matrix arrangement module, which is used to resample the elevation data extracted by the elevation data extraction module with multiple times the precision and arrange it into a matrix containing only elevation data; - Low-frequency term reconstruction module, which is used to perform wavelet decomposition on the matrix of elevation data arranged by the matrix arrangement module using different wavelet functions and decomposition levels, and reconstruct the low-frequency terms in the wavelet decomposition results; - Optimal wavelet function and decomposition level determination module, which is used to comprehensively evaluate the low-frequency term reconstruction results of the low-frequency term reconstruction module based on the signal reconstruction error and the degree of noise reduction, and determine the optimal wavelet function and decomposition level used for wavelet decomposition. - Filtering and noise reduction module, which is used to filter and reduce noise on the matrix of elevation data arranged by the matrix arrangement module by using the optimal wavelet function and the optimal decomposition level determination module. - Terrain ridgeline extraction module, which uses an eight-neighbor relative elevation discrimination method to extract terrain ridgelines from the elevation data matrix after denoising by the filtering and denoising module.

7. An electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, characterized in that: The memory, which is used to store computer programs, A processor for executing a computer program stored in a memory to implement the steps of the ridgeline extraction method reflecting geomorphic features as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the ridgeline extraction method reflecting landform features as described in any one of claims 1-5.

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