A river underwater terrain reconstruction method based on single-beam data

Abstract

The present application discloses a method for reconstructing underwater terrain of a river based on single-beam data, which obtains the river channel vector surface, centerline and cross-section data of the target river; fits the cross-section and divides the cross-section points to determine the elevation; divides discrete center points on the river channel centerline between adjacent cross-sections, draws encrypted feature lines perpendicular to the tangent through the center point and divides the feature points. For each feature point, a candidate cross-section is extracted and a weight is determined; the corresponding cross-section point and its two side neighboring points are selected in each candidate cross-section to generate a neighborhood point set and determine the neighborhood point weight, based on which the weighted elevation of the candidate cross-section is calculated, and then the comprehensive weighted elevation is obtained by combining the candidate cross-section weights. The dynamic slope adjustment coefficient is determined according to the rate of change of the neighborhood point elevation, the width adjustment coefficient is determined based on the ratio of the river section width to the average width of the entire river section, the feature point elevation is comprehensively determined, and the river underwater terrain is constructed by combining the elevations of all feature points with the original cross-section data. The present application can accurately reconstruct the underwater terrain of the river.

Description

Technical Field

[0001] The present invention belongs to the technical field of river underwater terrain modeling, and in particular relates to a river underwater terrain reconstruction method based on single-beam data. Background Art

[0002] Underwater topography measurement in rivers is a crucial foundation for hydrological engineering, waterway management, and ecological restoration. Currently, this technology primarily relies on single-beam bathymetry, multi-beam bathymetry, LiDAR (Light Detection and Ranging), and remote sensing inversion. Single-beam bathymetry, due to its low cost and ease of operation, has become the primary method for surveying small and medium-sized rivers. However, its data sparsity poses significant challenges for high-precision topography reconstruction. Reconstructing river underwater topography based on single-beam data faces three key challenges: First, data sparsity and interpolation distortion. Single-beam data is distributed only along sections, with no data between sections. Traditional interpolation methods (such as inverse distance weighting or kriging) are prone to smoothing distortion or false undulations in complex riverbeds (such as deep troughs and sand waves), making it difficult to accurately reflect abrupt topographic changes. Second, morphological features are poorly represented. Rivers, influenced by flow dynamics, often develop nonlinear features such as steep slopes and deep pools. Existing methods lack dynamic coupling with morphological parameters such as channel width and slope, resulting in the loss of critical geomorphological information. Third, single-beam data is easily interfered by aquatic plants, suspended matter or bubbles, resulting in abnormal water depth points and significant noise interference.

[0003] Existing solutions often rely on fixed-parameter spatial interpolation (such as IDW) or manual corrections, which present significant limitations. Traditional interpolation methods only consider distance attenuation and ignore the dynamic influence of river morphology. Furthermore, traditional interpolation methods often lack adaptability and cannot accommodate the diverse needs of different river sections (e.g., narrow channels, wide and shallow channels, and winding channels). These limitations make it difficult for reconstructed terrain to accurately reflect actual landform characteristics, compromising the accuracy of decisions for hydrological engineering, waterway management, and ecological restoration.

[0004] Therefore, there is an urgent need to explore more efficient interpolation methods and data processing technologies to improve the application effect of single-beam data in river underwater topography reconstruction. Summary of the Invention

[0005] The technical purpose of this application is to provide a river underwater terrain reconstruction method based on single-beam data to address the technical problem that the current river underwater terrain modeling technology based on single-beam data lacks adaptive capabilities and cannot take into account the different needs of different river sections, thereby affecting the reconstruction accuracy.

[0006] In order to achieve the above technical objectives, this application adopts the following technical solutions.

[0007] The present application provides a method for reconstructing river underwater terrain based on single-beam data, including:

[0008] Obtain target river channel vector surface data, river channel centerline data, and river channel cross-sectional data based on single-beam measurement;

[0009] According to the river section data, fitting the section and dividing the section points, and determining the elevation of each section point;

[0010] Based on the river channel vector surface data, discrete center points are divided on the river channel center line between two adjacent sections, and straight lines are drawn through these center points along the direction perpendicular to the tangent line of the river channel center line at the center points to obtain encrypted feature lines, and feature points are divided on the encrypted feature lines;

[0011] For each of the feature points, the following steps are performed to determine the elevation of the feature point: according to the spatial position of the feature point, a set number of sections upstream and downstream are extracted as candidate sections, and the weight of each candidate section is determined; for each candidate section, the section point corresponding to the feature point and the section points within the neighborhood radius on both sides thereof are selected from the candidate section as neighborhood points to generate a neighborhood point set of the candidate section, and the weight of each neighborhood point in the neighborhood point set is determined; according to the elevations of all neighborhood points in the neighborhood point set and the neighborhood point weights, the weighted elevation of the candidate section is determined; according to the weights of all candidate sections and the weighted elevations of all candidate sections, the comprehensive weighted elevation of the feature point is obtained; according to the elevation change rate of all neighborhood points in all neighborhood point sets, the dynamic slope adjustment coefficient is determined; the width adjustment coefficient is determined based on the ratio of the width of the river section where the feature point is located to the average width of the entire river section; the elevation of the feature point is comprehensively determined according to the comprehensive weighted elevation of the feature point, the dynamic slope adjustment coefficient and the width adjustment coefficient;

[0012] The underwater topography of the river is constructed based on the elevations of all feature points and combined with the original river section data.

[0013] Furthermore, according to the river section data, fitting the section and dividing the section points include:

[0014] NURBS is used to fit the cross section and the order of the curve is set. The number of control points is determined according to the complexity of the underwater terrain of the river. After the initial weight of the control points is set to 1, the positions and weights of the control points are optimized by the least squares method to minimize the mean square error between the fitted cross section and the original cross section points. The fitted curve is then parameterized and the parameter interval is divided into equal intervals to obtain cross section points.

[0015] Furthermore, the weight of the mth candidate section is determined as follows:

[0016] ;

[0017] in, , f is the total number of sections, is the weight of the mth candidate section, λ represents the distance attenuation coefficient, d m Indicates the length of the river centerline between the encrypted feature line where the feature point is located and the mth candidate section.

[0018] Furthermore, the calculation expression of the neighborhood point weight is as follows:

[0019] ;

[0020] in is the pth neighboring point c on the mth candidate section m,p The weight of k is the sequence number of the feature point on the encrypted feature line.

[0021] Furthermore, the weighted elevation h on the mth candidate section m,k It is determined by the elevation of all neighboring points in the neighborhood point set and the neighborhood point weight. The calculation formula is as follows:

[0022] ;

[0023] Among them, h m,p represents the pth neighboring point c on the mth candidate section m,p The elevation, is the pth neighboring point c on the mth candidate section m,p The weight of k is the sequence number of the feature point on the encrypted feature line.

[0024] Furthermore, the expression for determining the comprehensive weighted elevation of the feature point is as follows:

[0025] ;

[0026] Where f is the total number of sections, is the weight of the mth candidate section, h m,k represents the weighted elevation of the mth candidate section, h k Indicates the comprehensive weighted elevation of the feature point with sequence number k.

[0027] Furthermore, the dynamic slope adjustment coefficient is determined according to the elevation change rate of all neighboring points in all neighboring point sets, including: determining the average elevation difference Δh of all neighboring points in the neighboring point set of the feature point on all candidate sections, which is expressed as follows:

[0028] ;

[0029] Among them, σ h is the standard deviation of the water depth values ​​of the neighboring points of the feature point in all the neighboring point sets; L represents the average water depth of the neighboring points of the feature point in all the neighboring point sets;

[0030] Calculate the dynamic slope adjustment coefficient through the tanh function , the expression is as follows:

[0031] ;

[0032] Where S represents the slope between the first candidate section and the encrypted feature line where the feature point is located; S0 represents the slope threshold; To prevent the parameter from dividing by zero; β is the maximum adjustment amplitude.

[0033] Furthermore, the elevation H of the feature point is determined using the following formula:

[0034] ;

[0035] Among them, h k represents the comprehensive weighted elevation of the feature point with sequence number k, α w (W) is the width adjustment coefficient, is the dynamic slope adjustment coefficient.

[0036] Furthermore, the curve order is 3; for a waterway river with regular underwater terrain, the number of control points is 3-4; for a natural river with complex underwater terrain, the number of control points is 5-8.

[0037] Furthermore, the width adjustment coefficient is determined based on the ratio of the width of the river section where the feature point is located to the average width of the entire river section. The expression is as follows:

[0038] ;

[0039] where α w (W) is the width adjustment coefficient, W is the width of the river section where the encrypted characteristic line of the characteristic point is located, W0 is the average width of the entire river section, and γ represents the power exponential parameter.

[0040] Compared with the prior art, the beneficial technical effects achieved by the embodiments of the present application are: obtaining the river vector surface data, centerline data and cross-sectional data based on single-beam measurement of the target river. Various types of data complement each other and describe the characteristics of the river from different dimensions. The curve shape can be flexibly adjusted according to the complexity of the underwater terrain of the river, so that the fitted cross-section is more in line with the actual terrain. The fused comprehensive weighted elevation can scientifically and reasonably reflect the degree of influence of various factors on the target point, and provide a basis for accurately calculating the elevation of the target point. Furthermore, the dynamic slope adjustment coefficient can reflect the change in terrain slope, and the width adjustment coefficient takes into account the differences in river morphology. The combination of the two makes the target point elevation calculation more in line with the actual terrain conditions. The present application can accurately reconstruct the underwater terrain of the river. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present application in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only schematic and are used to help understand the present application. They do not specifically limit the shapes and proportional dimensions of the components of the present application. Those skilled in the art can select various possible shapes and proportional dimensions to implement the present application according to the specific circumstances under the guidance of the present application. In the drawings:

[0042] Figure 1 A schematic flow chart of a method for reconstructing river underwater terrain based on single-beam data provided in an embodiment of the present application;

[0043] Figure 2 This is a schematic diagram of the measurement section and encrypted characteristic lines of the embodiment of the present application;

[0044] Figure 3 This is a schematic diagram of the neighborhood point set of the second feature point (k=2) when the number of candidate sections is 4 and the neighborhood radius is 1 in an embodiment of the present application;

[0045] Figure 4 This is a schematic diagram of calculating the section weight of the second feature point (k=2) when the number of sections is 4 and the neighborhood radius is 1 in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0047] The present application embodiment provides a river underwater terrain reconstruction method based on single beam data, such as Figure 1 As shown, the following steps are included:

[0048] Step 1: Obtain target river channel vector surface data, channel centerline data, and channel cross-sectional data based on single-beam measurement;

[0049] Step 2 includes:

[0050] Step 2-1: Based on the river section data, fit the section and divide the section points to determine the elevation of each section point;

[0051] Step 2-2: Based on the river vector surface data, discrete center points are divided on the river centerline between two adjacent sections. Then, straight lines are drawn through these center points along the direction perpendicular to the tangent line of the river centerline at the center point to obtain encrypted feature lines, and feature points are divided on the encrypted feature lines.

[0052] Step 3 includes: for each feature point, executing the following steps 3-1 to 3-6 to determine the elevation of the feature point:

[0053] Step 3-1: Extract a set number of upstream and downstream sections as candidate sections based on the spatial position of the feature points, and determine the weight of each candidate section;

[0054] Step 3-2: For each candidate section, select the section point corresponding to the feature point on the candidate section, and the section points within the neighborhood radius on both sides of the candidate section as neighborhood points to generate a neighborhood point set, and determine the weight of each neighborhood point in the neighborhood point set;

[0055] Step 3-3: Determine the weighted elevation of the candidate section based on the elevations of all neighboring points in the neighborhood point set and the weights of the neighboring points; obtain the comprehensive weighted elevation of the feature point based on the weights of all candidate sections and the weighted elevations of all candidate sections;

[0056] Step 3-4: Determine the dynamic slope adjustment coefficient based on the elevation change rate of all neighborhood points in all neighborhood point sets;

[0057] Step 3-5: Determine the width adjustment coefficient based on the ratio of the width of the river section where the feature point is located to the average width of the entire river section;

[0058] Step 3-6: Determine the elevation of the feature point based on its comprehensive weighted elevation, dynamic slope adjustment coefficient, and width adjustment coefficient.

[0059] Step 4: Based on the elevations of all feature points and the original river section data, construct the underwater topography of the river.

[0060] In some embodiments, the cross-section points, center points, and feature points are divided at equal intervals. Discrete center points are divided on the center line of the river according to the principle of equal spacing, and then encrypted feature lines are obtained and feature points are divided, which increases the density and details of the terrain data. Equal distance division makes the data acquisition process have clear rules and standards, which is convenient for operation and implementation. In actual measurement and data processing, division according to fixed intervals can reduce human errors and improve the efficiency and accuracy of data acquisition. At the same time, the distribution of data with equal intervals is also conducive to subsequent calculations and analysis, making the algorithm easier to implement and reducing the complexity of data processing. Equal interval division can also ensure that the terrain features are sampled more evenly throughout the river area. Whether in the straight section or the curved section of the river, terrain information can be obtained with the same density, avoiding the situation where the terrain features in certain areas are over-sampling or omitted due to uneven sampling intervals.

[0061] In some embodiments, before step 1, river channel vector surface data, river channel centerline data and river channel cross-sectional data (a total of s cross-sectional data) measured by single-beam measurement can be collected from basic water resource surveys to complete cross-sectional data preprocessing.

[0062] Single-beam measurement can be carried out onboard ships, portable devices, aircraft, or underwater robots. For example, shipborne single-beam measurement is a measurement technique that uses sound waves as a propagation medium. By transmitting sound waves toward the water bottom, receiving the reflected waves back onboard, and calculating the propagation time and speed of the sound waves, information such as underwater terrain elevation, depth, and material distribution can be determined. This technology is primarily used in underwater topography and marine resource surveying.

[0063] In some embodiments, cross-sectional data preprocessing includes operations such as coordinate transformation, noise filtering, and outlier removal to ensure data accuracy and consistency. For example, cross-sectional data noise filtering can employ a sliding window standard deviation method, with a filter window size of 7, to remove data points that deviate from the mean by ±3 standard deviations.

[0064] In some embodiments, step 2-1 may include: first, fitting each section using NURBS, determining key parameters such as the curve order, the number of control points, and the weight of the control points, and optimizing the position and weight of the control points by the least squares method to minimize the mean square error between the fitted section curve and the original section points.

[0065] As an example, the key parameters are set as follows: the curve order determines the smoothness and flexibility of the curve and is generally set to 3; the number of control points is set according to the complexity of the river's underwater terrain. For waterway rivers with regular underwater terrain, the recommended number of control points is 3-4, and for natural rivers with complex underwater terrain, the recommended number of control points is 5-8. The more control points there are, the greater the degree of freedom of the curve, and the more precise the expression of the underwater terrain; the weight assigned to each control point is initially set to 1, and is subsequently adjusted according to the fitting accuracy.

[0066] As an example, based on the NURBS definition, construct the curve equation:

[0067] ;

[0068] Where C(u) represents the coordinate of the cross-section curve at parameter u; N i,k (u) is the i-th k-order B-spline basis function; w i represents the weight of the i-th control point; P i Represents the coordinate vector of the i-th control point, and z is the total number of control points.

[0069] Furthermore, the fitted cross-section curve is parameterized to generate the parameter interval [u min ,u max ], divide the parameter interval into n cross-section points at equal intervals, and calculate each parameter value: , , substitute the parameter value into the NURBS curve equation to obtain the corresponding cross-section point coordinates (x i ,y i ).

[0070] In some embodiments, step 2-2 may specifically include: refining the underwater terrain features between sections by drawing encrypted feature lines. According to the accuracy requirements, the center line of the river between two adjacent sections can be divided into evenly distributed center points according to the principle of equal spacing to capture the morphological changes of the river. Subsequently, for each discrete center point, a vertical line is drawn along the direction perpendicular to the tangent of the center line of the river at that point (i.e., the vertical direction of the river flow direction) to reflect the width changes and local morphological characteristics of the river in the lateral dimension. When the vertical line intersects with the water surface boundary of the entire river (i.e., the river surface), the intersection segment formed is the so-called encrypted feature line. The same as the section point division method, the encrypted feature line is divided into n feature points at equal intervals, and the number of points on each section and the encrypted feature line is the same (n). The section points correspond to the serial numbers of the feature points one by one, so as to facilitate the subsequent generation of the neighborhood point set of the feature points.

[0071] In some embodiments, step 3-1 may specifically include sequentially determining candidate sections and candidate section weights for each feature point. Based on the spatial location of the feature point, a specific number f of sections upstream and downstream of the feature point are extracted as candidate sections, ensuring the spatial correlation between the sections and the feature point and data integrity, and reflecting the impact of the underwater topography along the river flow direction on the interpolated elevation.

[0072] The exponential function has the characteristics of a sharp drop in weight over a short distance and a gradual change over a long distance. According to the spatial relationship between the candidate section and the feature point, the rapid initial decay and long tail effect of the exponential function are used to construct the section weight, and the weight is reasonably allocated to reflect the contribution of the candidate section to the feature point parameter estimation. Determined by distance and global.

[0073] In this embodiment, a total of f sections upstream and downstream of the encrypted feature line where the feature point is located are selected as candidate sections. Figure 2 shown.

[0074] In some embodiments, for the mth ( ) candidate section weights The calculation formula is as follows:

[0075] ;

[0076] Among them, λ represents the distance attenuation coefficient, which controls the speed at which the weight decreases with distance. The larger λ is, the more obvious the advantage of the proximal section is. λ is adjusted according to the section density. For dense sections (section spacing < 50 m), λ can be 0.02-0.03, and for sparse sections (spacing > 100 m), λ can be 0.01-0.02; d m It represents the length of the river centerline between the encrypted characteristic curve and the mth candidate section, which can be found in Figure 4 shown.

[0077] In some embodiments, for the encrypted feature line located between the 1st and f / 2th sections, or the encrypted feature line between the sf / 2th and sth sections, that is, the encrypted feature lines at the beginning and end of the river, the candidate sections are the sections that can be searched within the range of f / 2 sections before and after the encrypted feature line.

[0078] Example: If f=4, for the encrypted feature line between the 1st and 2nd sections, within the range of 4 / 2, the 1st, 2nd and 3rd sections can be searched as candidate sections.

[0079] In some embodiments, step 3-2 may specifically include determining a set of neighborhood points for the feature point on each candidate section and a neighborhood weight for each neighborhood point. The neighborhood weights are used to quantify the influence of different section points within the same section on the feature point. Riverbed elevation typically has spatial continuity, and the influence of single-point measurement errors is smoothed by weighted averaging of multiple neighborhood points.

[0080] Each section and encrypted feature line has n points. Through step 3-1, a total of f candidate sections upstream and downstream of each feature point are determined, and the neighborhood point set of each feature point in each candidate section is determined in turn.

[0081] Assume that the point number of the current feature point to be interpolated is k (k∈(1, 2, ..., n), and r is the neighborhood radius. The neighborhood radius is determined according to the complexity of the river. For natural rivers with many bends and bifurcations, the neighborhood radius is 3-5, and for straight rivers, it is 1-2. Select the kth section point in the mth candidate section (abbreviated as "candidate section m") (m∈(1, 2, ..., f)) and the r points on both sides to generate the neighborhood point set N(m, k). The neighborhood point set includes the section points with serial numbers from kr to k+r, that is, .

[0082] In some embodiments, if the feature point number k∈(1, 2, ..., r) or k∈(nr, n-r+1, ..., n), that is, the feature point located within the neighborhood radius at both ends of the encrypted feature line, its feature point and the section point have a one-to-one relationship, and the neighborhood point set of the candidate section m is { }.

[0083] Example 1 of neighborhood point set selection: For a feature point with sequence number k=6 on a certain encrypted feature line, the neighborhood radius r=3, and four upstream and downstream sections have been determined as candidate sections. The neighborhood point set of this feature point on candidate section 1 (m=1) is N(1, 6), including the section points numbered 3 to 9 on candidate section 1, that is:

[0084] .

[0085] Example 2 of neighborhood point set selection: For a feature point with sequence number k=2 on a certain encrypted feature line, the neighborhood radius r=3, k=2∈(1,2,3), the feature point is located within the neighborhood radius, and 4 upstream and downstream sections have been determined as candidate sections. The neighborhood point set of the feature point on candidate section 1 (m=1) is N(1,2), including the section point with sequence number 2 on candidate section 1, that is, .

[0086] Example 3 of neighborhood point set selection: Figure 3As shown in the figure, for a feature point with sequence number k=2 on a certain encrypted feature line, the neighborhood radius r=1, k=2, the feature point is not located within the neighborhood radius, and four upstream and downstream sections have been determined as candidate sections. The neighborhood point set of the feature point on candidate section 1 (m=1) is N(1, 2), including the section points with sequence numbers 1 and 2 on candidate section 1, that is, {c1,1, c1,2, c1,3}.

[0087] The neighborhood point weight is determined by the points in the neighborhood and is local, which is used to reflect the local continuity of the section data. For the neighborhood point c of point p on candidate section m, m,p , whose elevation value is h m,p , its neighborhood point weight The calculation formula is as follows:

[0088] ;

[0089] Where p represents the sequence number of the neighborhood point in the neighborhood point set on the candidate section m, and the range is [k−r, k+r].

[0090] Then the elevation value h of the candidate section m is m,k It is determined by the elevation of all neighboring points in the neighborhood point set and the neighborhood point weight. The calculation formula is as follows:

[0091] ;

[0092] Among them, h m,p Represents the neighborhood point c m,p Elevation, h m,k Represents the weighted elevation of candidate section m. For each dense feature line, there are n feature points. Each feature point corresponds to f candidate sections and f section weights, generating f neighborhood point sets. Each neighborhood point in each neighborhood point set has a neighborhood point weight. The comprehensive weighted elevation of the feature point is calculated. The comprehensive weighted elevation of the n feature points on each dense feature line is calculated in sequence, reflecting the underwater terrain in more detail.

[0093] In some embodiments, step 3-3 may specifically include: calculating the comprehensive weighted elevation of the feature point by integrating the above-mentioned section weights and neighborhood point weights. This process takes into account the impact of the river flow movement along the flow direction and perpendicular to the river bank on the underwater terrain.

[0094] .

[0095] In some embodiments, steps 3-4 include: the dynamic slope adjustment coefficient reflects the rate of change of river elevation along the direction of water flow, is directly related to the scouring-deposition dynamic process of water flow, and is more consistent with the mechanism than simple geometric indicators. First, the average elevation difference Δh of all neighboring points in the neighborhood point set of the feature point on all candidate sections is calculated to reflect the degree of riverbed undulation perpendicular to the river bank. Δh identifies complex terrain areas and adjusts the model sensitivity to reduce manual parameter debugging. Then, the dynamic slope adjustment coefficient is calculated by the tanh function. .

[0096] When Δh is large, indicating a sudden change in terrain, the denominator is increased to suppress If Δh changes, the slope weight will be weakened; if Δh is small, it means the terrain is flat, which will increase the influence of the slope on the interpolation. The calculation formula is as follows:

[0097] ;

[0098] ;

[0099] Among them, σ h Indicates the standard deviation of the water depth values ​​in the neighborhood of the feature point; represents the average water depth of the neighborhood; S represents the slope between the first candidate section and the encrypted characteristic line where the feature point is located; S0 represents the slope threshold, which is determined by the average slope of the entire river section; It is a minimum value (such as 1e-6) to prevent division by zero; β is the maximum adjustment amplitude.

[0100] In some embodiments, steps 3-5 specifically include using the width ratio (W / W0) to reflect the characteristics of the lateral constraint of the river channel, and calculating the width adjustment coefficient α through a power function. w (W), where γ represents the power exponential parameter, W represents the width of the river section where the encrypted characteristic line of the characteristic point is located, and W0 represents the average width of the entire river section.

[0101] .

[0102] In some embodiments, steps 3-6 specifically include, based on the comprehensive weighted elevation, dynamic slope adjustment coefficient and width adjustment coefficient, finally determining the elevation of the feature point. , the formula is as follows:

[0103] ;

[0104] Right now: .

[0105] Step 4: Combine the cross-section points with the encrypted feature point data to obtain gridded and refined elevation points, which serve as input data for underwater terrain modeling. In ArcGIS, based on the Triangulated Irregular Network (TIN) method, the "Create TIN" tool is used to generate a TIN of the river's underwater terrain. The "TIN to Raster" tool is then used to set the sampling interval parameter (e.g., 10 meters) to perform refined river underwater terrain modeling.

[0106] The above is a detailed introduction to the river underwater terrain reconstruction method based on single-beam data provided by this application. This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the concept of this application and should not be understood as limiting the scope of protection of this application.

Claims

1. A river underwater terrain reconstruction method based on single-beam data, characterized in that: include: Obtain target river channel vector surface data, river channel centerline data, and river channel cross-sectional data based on single-beam measurement; According to the river section data, fitting the section and dividing the section points, and determining the elevation of each section point; Based on the river channel vector surface data, discrete center points are divided on the river channel center line between two adjacent sections, and straight lines are drawn through these center points along the direction perpendicular to the tangent line of the river channel center line at the center points to obtain encrypted feature lines, and feature points are divided on the encrypted feature lines; For each of the feature points, the following steps are performed to determine the elevation of the feature point: a set number of sections upstream and downstream are extracted as candidate sections according to the spatial position of the feature point, and the weight of each candidate section is determined; for each candidate section, a section point corresponding to the feature point and section points within a neighborhood radius on both sides thereof are selected on the candidate section as neighborhood points to generate a neighborhood point set of the candidate section, and the weight of each neighborhood point in the neighborhood point set is determined; Determine the weighted elevation of the candidate section based on the elevations of all neighboring points in the neighborhood point set and the neighborhood point weights; obtain the comprehensive weighted elevation of the feature point based on the weights of all candidate sections and the weighted elevation of all candidate sections; determine the dynamic slope adjustment coefficient based on the elevation change rate of all neighborhood points in all neighborhood point sets; determine the width adjustment coefficient based on the ratio of the width of the river section where the feature point is located to the average width of the entire river section; comprehensively determine the elevation of the feature point based on the comprehensive weighted elevation of the feature point, the dynamic slope adjustment coefficient and the width adjustment coefficient; The underwater topography of the river is constructed based on the elevations of all feature points and combined with the original river section data.

2. The method for river underwater terrain reconstruction based on single-beam data according to claim 1, characterized in that: According to the river section data, the section is fitted and the section points are divided, including: NURBS is used to fit the cross section and the order of the curve is set. The number of control points is determined according to the complexity of the underwater terrain of the river. After the initial weight of the control points is set to 1, the positions and weights of the control points are optimized by the least squares method to minimize the mean square error between the fitted cross section and the original cross section points. The fitted curve is then parameterized and the parameter interval is divided into equal intervals to obtain cross section points.

3. The method for river underwater terrain reconstruction based on single-beam data according to claim 1, characterized in that: The public statement for determining the weight of the mth candidate section is as follows: ； in, , f is the total number of sections, is the weight of the mth candidate section, λ represents the distance attenuation coefficient, d m Indicates the length of the river centerline between the encrypted feature line where the feature point is located and the mth candidate section.

4. The method for reconstructing river underwater terrain based on single-beam data according to claim 1, characterized in that: The calculation expression of neighborhood point weight is as follows: ； in is the pth neighboring point c on the mth candidate section m,p The weight of k is the sequence number of the feature point on the encrypted feature line.

5. The method for river underwater terrain reconstruction based on single beam data according to claim 1, characterized in that: The weighted elevation h of the mth candidate section m,k The calculation formula is as follows: ； Among them, h m,p represents the pth neighboring point c on the mth candidate section m,p The elevation, is the pth neighboring point c on the mth candidate section m,p The weight of k is the sequence number of the feature point on the encrypted feature line.

6. The method for river underwater terrain reconstruction based on single beam data according to claim 1, characterized in that: The expression for determining the comprehensive weighted elevation of the feature point is as follows: ； Where f is the total number of sections, is the weight of the mth candidate section, h m,k represents the weighted elevation of the mth candidate section, h k Indicates the comprehensive weighted elevation of the feature point with sequence number k.

7. The method for river underwater terrain reconstruction based on single beam data according to claim 1, characterized in that: The dynamic slope adjustment coefficient is determined based on the elevation change rate of all neighboring points in the neighborhood point set, including: Determine the average elevation difference Δh of all neighboring points in the neighboring point set of the feature point on all candidate sections. The expression is as follows: ； Among them, σ h is the standard deviation of the water depth values ​​of the neighboring points of the feature point in all the neighboring point sets; L represents the average water depth of the neighboring points of the feature point in all the neighboring point sets; Calculate the dynamic slope adjustment coefficient through the tanh function , the expression is as follows: ； Where S represents the slope of the first candidate section and the encrypted characteristic line where the characteristic point is located; S0 represents the slope threshold; To prevent the parameter from dividing by zero; β is the maximum adjustment amplitude.

8. The method for river underwater terrain reconstruction based on single beam data according to claim 1, characterized in that: Determine the elevation H of the feature point using the following formula: ； Among them, h k represents the comprehensive weighted elevation of the feature point with sequence number k, α w (W) is the width adjustment coefficient, is the dynamic slope adjustment coefficient.

9. The method for river underwater terrain reconstruction based on single beam data according to claim 2, characterized in that: The curve order is 3; for a waterway river with regular underwater terrain, the number of control points is 3-4; for a natural river with complex underwater terrain, the number of control points is 5-8.

10. The method for river underwater terrain reconstruction based on single beam data according to claim 1, characterized in that: The width adjustment coefficient is determined based on the ratio of the width of the river section where the feature point is located to the average width of the entire river section. The expression is as follows: ； where α w (W) is the width adjustment coefficient, W is the width of the river section where the encrypted characteristic line of the characteristic point is located, W0 is the average width of the entire river section, and γ represents the power exponential parameter.

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