River terrain generation method and system, storage medium and electronic equipment

Abstract

The invention belongs to the technical field of water conservancy projects, and provides a river topography generation method and system, a storage medium and electronic equipment, and the method comprises the steps: collecting data needed for generating a river topography, dividing the river topography into a topography above the highest water level, a topography below the lowest water level, and a topography between the highest water level and the lowest water level, extracting a waterline, a center line and an overflowing section; calculating a relative stability index of an overflow section, identifying a stable river reach in the river channel, and dividing the stable river reach into a curved river reach and a straight river reach; the waterline of the curved river reach and the straight river reach is endowed with an elevation value to obtain a contour line; a terrain above the highest water level, a terrain between the lowest water level and the highest water level and a terrain below the lowest water level are constructed, and the three terrains are spliced to form a complete river channel terrain. According to the method, non-contact generation of the river terrain is achieved, a terrain foundation is provided for hydrodynamic analysis, and the method plays an important role in flood simulation, drainage basin management and ecological protection.

Description

Technical Field

[0001] The present invention belongs to the technical field of water conservancy engineering, and in particular relates to a river terrain generation method, system, storage medium and electronic equipment. Background Art

[0002] In watershed management and hydrological research, river topography data is the basis for hydrodynamic analysis, flood simulation, ecosystem protection and flood control decision-making. River topography not only determines the path and velocity of water flow, but also has a profound impact on the habitat formation, sediment transport and water quality maintenance of river ecosystems. At present, the acquisition of river topography mainly relies on manual measurement, terrain construction by Gaofen-7 satellite at low water level and calculation of digital elevation model (DEM). Although manual measurement has high local accuracy, it requires on-site operation and is restricted by terrain, climate and traffic conditions, especially in remote mountainous and canyon areas. The operation is difficult and costly, making it difficult to achieve large-scale and continuous terrain data acquisition. Although terrain generation based on DEM has a certain degree of wide-area coverage, it is usually unable to reflect the fine geomorphic features of the river due to resolution limitations, and it is difficult to accurately express the actual terrain of the river during local water level changes or dynamic changes in the river section. Summary of the invention

[0003] The present invention provides a river terrain generation method, system, storage medium and electronic equipment.

[0004] A method for generating river terrain of the present invention comprises:

[0005] Collect digital elevation model data, remote sensing image data, elevation data, daily water level data and large cross-section data of the river, use edge detection method to divide the river terrain into terrain above the highest water level, terrain below the lowest water level and terrain between the highest and lowest water levels, and extract the waterside line, center line and flow section;

[0006] Calculating a relative stability index of the flow section based on the center line and the flow section, identifying a stable river section in the river channel using the relative stability index of the flow section, and dividing the stable river section into a curved river section and a straight river section;

[0007] Assigning elevation values ​​to the waterside line of the curved river section and the waterside line of the straight river section using the elevation data and the daily water level data to obtain contour lines;

[0008] constructing the terrain above the highest water level using the remote sensing image data;

[0009] Using the elevation data and the daily water level data, constructing the terrain between the lowest water level and the highest water level based on the contour lines and the water edge lines;

[0010] Constructing the terrain below the lowest water level using the daily water level data and the large cross-section data;

[0011] The terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest water level and the highest water level are spliced ​​to form a complete river terrain.

[0012] Furthermore, the method of dividing the river terrain into terrain above the highest water level, terrain below the lowest water level, and terrain between the highest water level and the lowest water level by using the edge detection method includes:

[0013] Extracting water surface feature lines from the digital elevation model data using the edge detection method;

[0014] The edge detection method includes: noise smoothing; calculating gradient amplitude and gradient direction; performing non-maximum suppression along the gradient direction to obtain edge points; using a double threshold algorithm to detect and connect edge points to form edge lines;

[0015] The water surface characteristic lines include water surface lines, shore lines and river valley boundary lines;

[0016] The river channel terrain is divided by using the water surface characteristic line. The river channel terrain below the water surface line is divided into the terrain below the lowest water level, the river channel terrain between the water surface line and the shore line is divided into the terrain between the highest water level and the lowest water level, and the river channel terrain between the shore line and the river valley boundary line is divided into the terrain above the highest water level.

[0017] Further,

[0018] The relative stability index of the flow section is calculated based on the center line and the flow section, and the relative stability index of the flow section is used to identify the stable river section in the river channel, and the stable river section is divided into a curved river section and a straight river section, including:

[0019] Obtaining an intersection point between the flow section and the center line, and recording the intersection point between the flow section and the flow center line as a second intersection point;

[0020] Select the center lines of two adjacent times, merge the center lines of the two adjacent times, obtain the migration distance of the second intersection, record it as the intersection migration distance, and simultaneously obtain the intersection migration speed and the intersection migration acceleration;

[0021] Calculating a relative stability index of the flow section;

[0022] Determining a threshold value based on the relative stability index of the flow section, wherein the threshold value is used to judge whether the river section is stable;

[0023] Determining a relative stability index of a river section based on the relative stability index of the flow section;

[0024] By comparing the relative stability index of the river section with the threshold, the river section is divided into a stable river section and a swinging river section, thereby identifying the stable river section from the river channel;

[0025] The stable river section is divided into a curved river section and a straight river section.

[0026] Further,

[0027] The relative stability index of the flow section is calculated by the following formula:

[0028] RSIRR-G=β1s+β2v+β3a,

[0029] Wherein, RSIRR-G refers to the relative stability index; s refers to the intersection migration distance; v refers to the intersection migration velocity; α refers to the intersection migration acceleration; β1 refers to the coefficient of the intersection migration distance; β2 refers to the coefficient of the intersection migration velocity; β3 refers to the coefficient of the intersection migration acceleration.

[0030] Further,

[0031] The coefficient of the intersection migration distance, the coefficient of the point migration speed and the coefficient of the intersection migration acceleration are calculated by cluster analysis method, including:

[0032] The sections are grouped using a hierarchical clustering method to obtain a plurality of groups;

[0033] Calculating an average value of the intersection migration distances corresponding to the cross sections contained in each of the groups, using the average value of the intersection migration distances to calculate the sum of squares between the groups to obtain a first sum of squares, using the average value of the intersection migration distances to calculate the sum of squares of errors between the groups to obtain a first sum of squares of errors, calculating a ratio of the first sum of squares to the first sum of squares of errors to obtain a first ratio, and using the first ratio as a coefficient of the intersection migration distance;

[0034] Calculating an average value of the intersection migration velocities corresponding to the cross sections contained in each of the groups, calculating the sum of squares between the groups using the average value of the intersection migration velocities to obtain a second sum of squares, calculating the sum of squares of errors between the groups using the average value of the intersection migration velocities to obtain a second sum of squares of errors, calculating a ratio of the second sum of squares to the second sum of squares of errors to obtain a second ratio, and using the second ratio as a coefficient of the intersection migration velocity;

[0035] Calculate the average value of the intersection migration acceleration corresponding to the cross-sections contained in each of the groups, use the average value of the intersection migration acceleration to calculate the sum of squares between the groups to obtain a third sum of squares, use the average value of the intersection migration acceleration to calculate the sum of squares of errors between the groups to obtain a third sum of squares of errors, calculate the ratio of the third sum of squares to the third sum of squares of errors to obtain a third ratio, and use the third ratio as the coefficient of the intersection migration acceleration.

[0036] Further,

[0037] The method of using elevation data and daily water level data to assign elevation values ​​to the waterside lines of curved river sections and the waterside lines of straight river sections to obtain contour lines includes:

[0038] Assigning a first elevation value to the water edge of the curved river section using the elevation data;

[0039] Assigning a second elevation value to the waterside line of the straight river section using the elevation data;

[0040] Performing elevation point interpolation along the waterside line of the curved river section and the waterside line of the straight river section to obtain a third elevation value;

[0041] Using the daily water level data, assigning a fourth elevation value to the waterside line of the curved river section and the waterside line of the straight river section;

[0042] Along the waterside line of the curved river section and the waterside line of the straight river section, elevation points with the same elevation value are identified within the range of the first elevation value, the second elevation value, the third elevation value and the fourth elevation value, and the elevation points with the same elevation value are connected to obtain the contour lines.

[0043] Further,

[0044] The method of constructing the terrain above the highest water level using remote sensing image data includes:

[0045] Acquire stereo image pair data, perform orthorectification and image fusion on the stereo image pair data to generate orthophotos, and extract digital surface models and point cloud data from the orthophoto data.

[0046] Further,

[0047] The method of using elevation data and daily water level data to construct the terrain between the lowest water level and the highest water level based on contour lines and water edge lines includes:

[0048] Using the elevation data and the daily water level data, on the basis of the contour lines and the water edge lines, the terrain between the lowest water level and the highest water level is interpolated at elevation points to form continuous elevation points, thereby forming an elevation surface and obtaining a preliminary terrain construction result;

[0049] Secondary interpolation is performed based on the preliminary terrain construction result to fill the gaps in elevation points between the lowest water level and the highest water level.

[0050] A river channel terrain generation system of the present invention is used to implement the above-mentioned river channel terrain generation method, and the system comprises:

[0051] The collection and processing module is used to collect the digital elevation model data, remote sensing image data, elevation data, daily water level data and large cross-section data of the river, and use the edge detection method to divide the river terrain into the terrain above the highest water level, the terrain below the lowest water level and the terrain between the highest water level and the lowest water level, and extract the waterside line, center line and flow section;

[0052] an identification and division module, used for calculating a relative stability index of the flow section based on the center line and the flow section, identifying a stable river section in the river channel by using the relative stability index of the flow section, and dividing the stable river section into a curved river section and a straight river section;

[0053] A contour line generation module, using the elevation data and the daily water level data to assign elevation values ​​to the waterside line of the curved river section and the waterside line of the straight river section, to obtain contour lines;

[0054] A first construction module is used to construct the terrain above the highest water level using the remote sensing image data;

[0055] A second construction module is used to construct the terrain between the lowest water level and the highest water level based on the contour lines and the water edge lines by using the elevation data and the daily water level data;

[0056] A third construction module constructs the terrain below the lowest water level using the daily water level data and the large section data;

[0057] The splicing module is used to splice the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest water level and the highest water level to form a complete river terrain.

[0058] A computer-readable storage medium of the present invention stores a program or instruction, and when the program or instruction is run on a computer, the computer executes the aforementioned river channel topography generation method.

[0059] An electronic device of the present invention includes a processor coupled to a memory; the processor is used to read and execute a computer program stored in the memory to implement the aforementioned river channel topography generation method.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] Using optical image data to extract water body boundaries, assigning elevation information through altimetry data, and using interpolation to achieve contactless generation of river terrain, this technology not only provides a high-precision terrain foundation for hydrodynamic analysis, but also plays an important role in flood simulation, watershed management, and ecological protection. At the same time, the application of remote sensing technology has enabled the acquisition of river terrain to break through the limitations of geography, time and space, especially for river sections that lack relevant data support, which can realize the construction of terrain, effectively support large-scale hydrological monitoring, disaster warning, and water resources management, and provide a more convenient and accurate solution for hydrological research and environmental management. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 A flow chart of a method for generating river terrain according to the present invention;

[0064] Figure 2 It is a structural schematic diagram of the river terrain generation system of the present invention;

[0065] Figure 3 It is a schematic diagram of the structure of the electronic device of the present invention.

[0066] Description of reference numerals:

[0067] 201 - collection and processing module, 202 - identification and division module, 203 - contour line generation module, 204 - first construction module, 205 - second construction module, 206 - third construction module, 207 - splicing module, 301 - processor, 302 - memory. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] Figure 1 : is a flow chart of a method for generating river terrain according to an embodiment of the present invention. In one embodiment, the method specifically comprises the following steps:

[0070] S101: Collect digital elevation model data, remote sensing image data, elevation data, daily water level data and large cross-section data of the river, and use edge detection method to divide the river terrain into terrain above the highest water level, terrain below the lowest water level and terrain between the highest water level and the lowest water level, and extract the waterside line, center line and flow section.

[0071] S101-1: Collect digital elevation model data, remote sensing image data, elevation data, daily water level data and large cross-section data of the river.

[0072] 1. Digital elevation model data

[0073] The full name of digital elevation model is “Digital Elevation Model”, abbreviated as “DEM”. The resolution of the river channel in the study area at a specified time is 12.5 meters. The DEM data is raster image data.

[0074] 2. Remote sensing image data

[0075] Collect remote sensing image data of the rivers in the study area at different times within a specified time range from satellites such as Landsat series, Sentinel series and Gaofen series.

[0076] 3. Elevation data

[0077] The elevation data of the river channels in the study area at different times within a specified time range were collected from satellites such as TOPEX / Poseidon, Envisat, CryoSat-2, ICESat series, Jason series, Sentinel-3 and Gaofen series.

[0078] Elevation data includes elevation points and elevation values. Multiple elevation points along the direction of satellite orbit movement can be formed within one footprint, and each elevation point has a corresponding elevation value.

[0079] 4. Daily water level data and large cross-section data

[0080] The daily water level data and large-section data of the river in the study area at different times within the specified time range are collected. Both the daily water level data and the large-section data are measured data of the hydrological station.

[0081] S101-2: Use edge detection method to extract water surface feature lines from digital elevation model data, and use the water surface feature lines to divide the river terrain into terrain above the highest water level, terrain below the lowest water level, and terrain between the highest water level and the lowest water level.

[0082] The Canny edge detection method is a multi-stage edge detection method, including noise smoothing, calculation of gradient amplitude and gradient direction, non-maximum suppression, and detection and connection of edges using a dual threshold algorithm. The Canny edge detection method is used to extract the water surface characteristic lines of the river in the study area from the 12.5-meter DEM data. Specifically, the water surface characteristic lines of the river in the study area are extracted from the image. The extraction process is as follows:

[0083] Step 1: Noise smoothing.

[0084] Gaussian filter is a method of image processing using Gaussian kernel. It performs convolution operation on the image through Gaussian kernel to achieve smooth filtering of the image. The Gaussian kernel is calculated by a two-dimensional Gaussian function. The two-dimensional Gaussian function is as follows:

[0085]

[0086] Where (x, y) is the plane coordinate; σ is the standard deviation of the Gaussian distribution, which is used to control the width of the Gaussian function.

[0087] The processing results obtained by using Gaussian kernels of different sizes are different. A Gaussian kernel of size (2k+1)x(2k+1) is selected to process the image and a Gaussian kernel with Gaussian distribution characteristics is obtained. The calculation formula is as follows:

[0088]

[0089] Where (i, j) is the plane coordinate, i and j are the offsets relative to the center of the Gaussian kernel; σ is the standard deviation of the Gaussian distribution; and k is the radius of the Gaussian kernel.

[0090] In order to ensure that the sum of the weights of all pixels in the Gaussian kernel is 1, the Gaussian kernel with Gaussian distribution characteristics needs to be normalized. The processing formula is as follows:

[0091]

[0092] Where K is the normalization constant; H(i,j) is the Gaussian kernel with Gaussian distribution characteristics.

[0093]

[0094] Where G(i,j) is the normalized Gaussian kernel; H(i,j) is the Gaussian kernel with Gaussian distribution characteristics; K is the normalization constant.

[0095] After smoothing the image, the image noise is reduced and the accuracy of edge detection is guaranteed.

[0096] Step 2: Calculate the gradient magnitude and gradient direction.

[0097] The Sobel operator is used to calculate the gradient and direction of the pixel points in the image in the horizontal and vertical directions respectively. The calculation formula is as follows:

[0098]

[0099] Where G is the gradient amplitude; G x is the Sobel operator in the horizontal direction; G y is the Sobel operator in the vertical direction.

[0100] θ=arc tan(G y / G x ) (6),

[0101] In the formula, θ is the gradient direction; G x is the Sobel operator in the horizontal direction; G y is the Sobel operator in the vertical direction.

[0102] In equations (5) and (6), the Sobel operator G in the horizontal direction is x and the vertical Sobel operator G y They are:

[0103]

[0104]

[0105] This step quantizes the gradient direction into one of eight directions, which is convenient for subsequent non-maximum suppression.

[0106] Step 3: Perform non-maximum suppression along the gradient direction to obtain edge points.

[0107] The pixels in the image are traversed along the gradient direction, and each pixel in the image and its two adjacent pixels are divided into a local range, thereby obtaining multiple local ranges.

[0108] Compare the gradient amplitude of each pixel in each local range, obtain the pixel with the largest gradient amplitude in the local range, and record it as the edge point.

[0109] Eliminate non-edge points and keep only edge points.

[0110] This step makes the edges of the image relatively clear.

[0111] Step 4: Use the dual threshold algorithm to detect and connect edge points to form edge lines.

[0112] Set two thresholds, a high threshold and a low threshold, and compare the edge point gradient with the high threshold and the low threshold, respectively. Mark the edge points above the high threshold as strong boundary points, mark the edge points below the low threshold as non-boundary points, and mark the edge points less than or equal to the high threshold and greater than or equal to the low threshold as weak boundary points. For weak boundary points, use the 8-connected regions to further determine their types. Specifically, when the 8-neighborhood of the strong boundary contains a weak boundary point, the weak boundary point is changed to a strong boundary point. Similarly, if there is a weak boundary point in the 8-neighborhood of the non-boundary, the weak boundary point is changed to a non-boundary point.

[0113] Connect all strong boundary points to form a complete edge line.

[0114] The above method can be used to extract water surface characteristic lines at different water levels. The water surface characteristic lines include: water surface line, bank line and valley boundary line. The water surface characteristic lines are used to divide the river terrain. The river terrain below the water surface line is divided into the terrain below the lowest water level, the river terrain between the water surface line and the bank line is divided into the terrain between the highest water level and the lowest water level, and the river terrain between the bank line and the valley boundary line is divided into the terrain above the highest water level.

[0115] S101-3: Process remote sensing image data and elevation data.

[0116] The remote sensing image data is processed by radiation correction and atmospheric correction. Through radiation correction, the pixel value of the remote sensing image can be corrected to the actual reflectivity or brightness, eliminating the influence of the corresponding differences in the sensors; through atmospheric correction, the influence of atmospheric scattering and absorption on the image can be eliminated, ensuring that the spectral information of the image can accurately reflect the real conditions of the surface.

[0117] Align the collection time of the remote sensing image data and the elevation data to ensure that the collection time of the two types of data is consistent; align the spatial positions of the remote sensing image data and the elevation data to ensure that the elevation points in the elevation data are in the remote sensing image data.

[0118] S101-4: Extract water edge line, center line and water surface width from remote sensing image data.

[0119] 1. Waterside

[0120] Extract the river water morphology information from the remote sensing image data, and use the different bands of the remote sensing image data to calculate the water body normalization index through the following formula:

[0121]

[0122] In the formula, NDWI refers to the normalized difference water index; Green refers to the green light band; NIR refers to the near infrared band.

[0123] Based on the normalized water index, multiple water edges at different times (i.e., different water levels) were extracted using geographic information system tools.

[0124] 2. Centerline

[0125] Geographic information system tools are used to extract multiple center lines at different times (i.e. different water levels) from remote sensing image data.

[0126] 3. Flow section and water surface width

[0127] Use geographic information system tools to make a flow section perpendicular to the center line. The flow section intersects with the water edge to obtain the intersection of the flow section and the water edge, which is recorded as the first intersection. There are two first intersections, and the straight-line distance between the two first intersections is the water surface width. Finally, the flow section and water surface width at multiple different times (i.e., different water levels) are obtained.

[0128] S102: Calculate the relative stability index of the flow section based on the center line and the flow section, use the relative stability index of the flow section to identify the stable river section in the river channel, and divide the stable river section into a curved river section and a straight river section.

[0129] The specific process is as follows:

[0130] S102-1: Use geographic information system tools to obtain the intersection of the flow section and the center line, and record it as the second intersection point.

[0131] S102-2: Select the center lines of two adjacent times as needed, merge the center lines of the two adjacent times, obtain the migration distance of the second intersection, called "intersection migration distance", record the intersection migration distance as s, and correspondingly, record the intersection migration speed and intersection migration acceleration as v and a respectively.

[0132] S102-3: Calculate the relative stability index of the flow section.

[0133] The calculation formula is,

[0134] RSIRR-G=β1s+β2v+β3a (8),

[0135] Wherein, RSIRR-G refers to the relative stability index; s refers to the intersection migration distance; v refers to the intersection migration velocity; α refers to the intersection migration acceleration; β1 refers to the coefficient of the intersection migration distance; β2 refers to the coefficient of the intersection migration velocity; β3 refers to the coefficient of the intersection migration acceleration.

[0136] The values ​​of β1, β2, and β3 in formula (8) are determined by cluster analysis as follows:

[0137] Step 1: Use hierarchical clustering method to group the flow sections and obtain multiple groups.

[0138] When grouping the flow sections, the number of flow sections and the number of groups are first preset, and then the flow sections are grouped according to the preset number of groups. Specifically, the number of flow sections is N, and the number of groups is g. Preferably, g=3.

[0139] Step 2: Calculate β1, β2 and β3 by the following formula.

[0140] The calculation formula is,

[0141]

[0142]

[0143]

[0144] In the formula, F refers to the ratio of the sum of squares between groups to the sum of squares of errors between groups; SS(TR) refers to the sum of squares between groups; SSE refers to the sum of squares of errors between groups; g refers to the number of groups; n i Refers to the number of current-carrying sections contained in a single group; Refers to the average index value of the current-carrying sections contained in a single group; Refers to the average index value of the flow sections contained in all groups; Refers to the index value of a current-carrying section in a single group.

[0145] Specifically, the average index value refers to the average value of the intersection migration distance, the average value of the intersection migration speed, or the average value of the intersection migration acceleration. The average value of the intersection migration distance, the average value of the intersection migration speed, and the average value of the intersection migration acceleration are all calculated by the average value.

[0146] Based on formulas (9), (10) and (11), the following processing is performed:

[0147] Substitute the average value of the intersection migration distance into formula (10) to calculate the sum of squares between groups to obtain the first sum of squares. Substitute the average value of the intersection migration distance into formula (11) to calculate the sum of squares of errors between groups to obtain the first sum of squares of errors. Calculate the ratio of the first sum of squares to the first sum of squares of errors according to formula (9) to obtain the first ratio F1. Use the first ratio F1 as the coefficient β1 of the intersection migration distance.

[0148] Substitute the average value of the intersection migration speed into formula (10) to calculate the sum of squares between groups to obtain the second sum of squares. Substitute the average value of the intersection migration speed into formula (11) to calculate the sum of squares of errors between groups to obtain the second sum of squares of errors. Calculate the ratio of the second sum of squares to the second sum of squares of errors according to formula (9) to obtain the second ratio F2. Use the second ratio F2 as the coefficient β2 of the intersection migration speed.

[0149] Substitute the average value of the intersection migration acceleration into formula (10) to calculate the sum of squares between the groups to obtain the third sum of squares. Substitute the average value of the intersection migration acceleration into formula (11) to calculate the sum of squares of errors between the groups to obtain the third sum of squares of errors. Calculate the ratio of the third sum of squares to the third sum of squares of errors according to formula (9) to obtain the third ratio F3. Use the third ratio F3 as the coefficient β3 of the intersection migration acceleration.

[0150] After β1, β2 and β3 are calculated, β1, β2 and β3 are substituted into formula (8) to calculate the relative stability index of the flow section. Since the intersection migration distance, intersection migration speed and intersection migration acceleration corresponding to each flow section are different, the relative stability index of N flow sections is finally calculated.

[0151] S102-4: Determine a threshold based on the relative stability index of the flow section, and the threshold is used to judge whether the river section is stable.

[0152] Through step S103-3, the relative stability indexes of N flow sections are obtained. Thus, for g groups, the maximum relative stability index and the minimum relative stability index in each group can be obtained, which are respectively recorded as RSIRR-G max,i and RSIRR-G min,i , i=1,2,……,g.

[0153] A specific value is manually selected between two adjacent groups as the threshold. Specifically, the threshold is in the interval [RSIRR-G max,i ,RSIRR-G min,i+1 ] is selected. If the interval is small, a constant close to a specific value in the interval is manually selected as the threshold.

[0154] S102-5: Determine the relative stability index of the river section based on the relative stability index of the flow section.

[0155] A river channel includes multiple river sections, and a river section includes multiple flow sections. The average value of the relative stability index of the flow sections contained in a river section is calculated using the following formula:

[0156]

[0157] In the formula, It refers to the average value of the relative stability index of the flow sections contained in a certain river section; n refers to the number of flow sections contained in a certain river section; RSIRR-G m Refers to the relative stability index of a certain flow section.

[0158] S102-6: By comparing the relative stability index of the river section with the threshold, the river section is divided into a stable river section and a swinging river section.

[0159] The average value of the relative stability index of all flow sections of a river section is taken as the relative stability index of the river section, and the size of the relative stability index of the river section and the threshold are judged. If the relative stability index of the river section is greater than or equal to the threshold, the river section is considered to be a swinging river section; if the relative stability index of the river section is less than the threshold, the river section is considered to be a stable river section.

[0160] S102-7: Stable river sections are divided into curved sections and straight sections.

[0161] Obtain the length of the river section curve and the length of the river straight line, calculate the river section meandering, and divide the stable river section into curved river sections and straight river sections based on the river section meandering. The calculation formula is:

[0162]

[0163] In the formula, r refers to the winding degree of the river section; L refers to the curved length of the river section, in meters; l refers to the straight length of the river section, in meters.

[0164] According to the sinuosity classification system, stable river sections are divided into the following categories: if r≤1, it is a straight river section; if 1<r≤1.2, it is a low sinuosity river section; if 1.2<r≤1.5, it is a medium sinuosity river section; if 1.5<r, it is a high sinuosity river section. Low sinuosity river sections, medium sinuosity river sections and high sinuosity river sections are all curved river sections.

[0165] S103: Using the elevation data and the daily water level data, the elevation values ​​are assigned to the waterside lines of the curved river section and the waterside lines of the straight river section to obtain contour lines.

[0166] S103-1: Assign elevation values ​​to the waterside lines of the curved river section and the waterside lines of the straight river section using the elevation data and the daily water level data to obtain the elevation values ​​of the waterside lines.

[0167] The process of assigning elevation values ​​to straight river sections and curved river sections is introduced separately:

[0168] 1. Straight river section

[0169] Set a fixed spacing, draw a vertical line through the center line according to the fixed spacing and intersect with the waterside line, obtain the intersection of the vertical line and the waterside line, record it as the third intersection point, and the elevation value corresponding to each third intersection point is the same as the elevation value corresponding to the elevation point falling on the vertical line. Based on this conclusion, obtain the point where the third intersection point and the elevation point overlap, record it as the first overlapping point, assign the elevation value corresponding to the elevation point to the first overlapping point, and obtain the first elevation value. Since the first overlapping point is located on the waterside line, after assigning values ​​to multiple first overlapping points, the first elevation value is indirectly assigned to the waterside line.

[0170] 2. Bends

[0171] Since the elevation value of the waterside line in a curved river section may be different from the elevation value of the center line, a linear fit is performed on multiple elevation points in a direction perpendicular to the center line to obtain a continuous range of elevation values. At the same time, the points where the elevation points overlap with the waterside line are obtained and recorded as the second overlapping points. The second overlapping points are assigned elevation values ​​in a linear fitting manner to obtain the second elevation value. Similarly, after assigning values ​​to multiple second overlapping points, the second elevation value is indirectly assigned to the waterside line.

[0172] After assigning the first elevation value to the waterside of the straight river section and the second elevation value to the waterside of the curved river section, in order to ensure the integrity and uniformity of the waterside elevation values, multiple waterside ranges are set for the waterside of the straight river section and the waterside of the curved river section, requiring each waterside range to include at least two elevation values, and interpolating elevation points along the waterside at a fixed distance in each waterside range to obtain the third elevation value. This method effectively fills the gaps in elevation points.

[0173] Furthermore, in order to compensate for the discontinuity of the elevation data in the time series, the daily water level data is used to assign a fourth elevation value to the portion of the waterside line that is not assigned an elevation value according to the above method in S103-1.

[0174] S103-2: Extract contour lines based on the water edge elevation values.

[0175] Along the waterside line of the curved river section and the waterside line of the straight river section, elevation points with the same elevation values ​​are identified within the range of the first elevation value, the second elevation value, the third elevation value and the fourth elevation value. These elevation points with the same elevation values ​​are called iso-elevation points. The iso-elevation points are connected to generate contour lines, and finally multiple contour lines are generated.

[0176] Furthermore, the contour lines are smoothed to ensure the smoothness of the contour lines.

[0177] S104: Construct the terrain above the highest water level using remote sensing image data.

[0178] The GF-7 stereo image data is obtained, and the GF-7 stereo image data is orthorectified and image fused through common remote sensing image processing tools to generate high-resolution orthophoto data. The digital surface model (full name: "Digital Surface Model", abbreviated as "DSM") and point cloud data are extracted from the high-resolution orthophoto data through common remote sensing image processing tools. The digital surface model reflects the ups and downs of the river channel.

[0179] Through orthophoto data, digital surface models and point cloud data, the complete terrain above the highest water level can be obtained.

[0180] S105: Using the elevation data and the daily water level data, construct the terrain between the lowest water level and the highest water level based on the contour lines and the water edge lines.

[0181] Using elevation data and daily water level data, on the basis of contour lines and water edges, the terrain between the lowest water level and the highest water level is interpolated to form continuous elevation points, thus forming an elevation surface and obtaining the preliminary construction result of the terrain.

[0182] Based on the preliminary terrain construction results, secondary interpolation is performed using TIN interpolation or Kriging interpolation to fill the gaps in elevation points between the lowest water level and the highest water level to ensure a smooth transition in the boundary area and the integrity of the terrain.

[0183] S106: Construct the terrain below the lowest water level using the daily water level data and the large cross-section data.

[0184] S106-1: Generalize the sections in the large section data into triangular sections, and determine the water depth calculation model based on the daily water level data.

[0185] In order to simplify the calculation and analysis, the section during the transition from the lowest water level to the highest water level is generalized into a triangular section. The generalized triangular section can better reflect the changes in underwater terrain.

[0186] The triangular cross-section at the lowest water level and the triangular cross-section at the highest water level are generalized similar triangles, with the same geometric shapes but different scales.

[0187] By analyzing the generalized triangular section, it can be seen that during the transition from the lowest water level to the highest water level, as the water surface width increases, the centerline offset increases, the water depth increases, and the eccentric angle corresponding to the centerline also increases. From this, it can be determined that the centerline offset, the eccentric angle corresponding to the centerline offset, and the water depth are directly proportional to the water surface width.

[0188] The large section data and daily water level data during the transition period from the lowest water level to the highest water level are selected to obtain the water surface width at different water levels, calculate the cross-sectional area at different water levels, and calculate the water depth at different water levels under the generalized triangular cross-sectional shape. The maximum water surface width and the maximum centerline offset at the highest water level are calculated. The centerline offset is closely related to the morphology and hydrodynamics of the river channel, and the eccentric angle can reflect the offset of the centerline.

[0189] According to the cross-sectional area and maximum water surface width at the highest water level, as well as the cross-sectional area and water surface width at different water levels during the transition from the lowest water level to the highest water level, the centerline offset of the triangular cross-section is analyzed to determine the relationship between the water depth, water surface width and eccentric angle at different water levels during the transition from the lowest water level to the highest water level. The expression of this relationship is the water depth calculation model.

[0190] According to the cross-sectional area and maximum water surface width at the highest water level, as well as the cross-sectional area and water surface width at different water levels during the transition from the lowest water level to the highest water level, the offset angles corresponding to multiple centerline offsets at the highest water level and at different water levels during the transition from the lowest water level to the highest water level are determined.

[0191] Since the cross section is a generalized triangular cross section, the cross-sectional area and maximum water surface width at the highest water level, as well as the cross-sectional area and water surface width at different water levels during the transition from the lowest water level to the highest water level can be calculated using CAD and other software. Then the water depth h at the highest water level can be calculated using the following formula: xun and minimum water level to maximum

[0192]

[0193]

[0194] In the formula, h guo A is the water depth at different water levels during the transition from the lowest water level to the highest water level; guo Refers to the cross-sectional area at different water levels during the transition from the lowest water level to the highest water level; W guo h is the width of the water surface at different water levels during the transition from the lowest water level to the highest water level; xun A is the water depth at the highest water level; xun W is the cross-sectional area at the highest water level; xun It is the maximum water surface width at the highest water level.

[0195] According to the maximum water surface width at the highest water level and the water surface width at different water levels during the transition from the lowest water level to the highest water level, the offset of the centerline of the river channel is calculated by the following formula:

[0196]

[0197] Where a and Δw are the offsets of the center line; W xun W is the maximum water surface width at the highest water level; guo It is the water surface width at different water levels during the transition from the lowest water level to the highest water level.

[0198] Based on the similarity between the centerline offset and the triangular cross section, the eccentric angle corresponding to the centerline offset at different water levels during the transition from the lowest water level to the highest water level is determined by the following formula:

[0199]

[0200]

[0201] In the formula, h xun is the water depth at the highest water level; h guo W is the water depth at different water levels during the transition from the lowest water level to the highest water level; guo W is the width of the water surface at different water levels during the transition from the lowest water level to the highest water level; xun is the maximum water surface width at the highest water level; Δw is the centerline offset; tanα is the eccentricity angle corresponding to the centerline offset.

[0202] The offset angles corresponding to the multiple centerline offsets are averaged, and the average offset angle is determined as a fixed offset angle, as shown in the following formula:

[0203]

[0204] In the formula, is a fixed offset angle.

[0205] The relationship between the water depth, water surface width and fixed offset angle at different water levels during the transition from the lowest water level to the highest water level is determined as the water depth calculation model, as shown in the following formula:

[0206]

[0207] In the formula, h α-guo is the water depth at different water levels corresponding to a fixed eccentric angle; W xun W is the maximum water surface width at the highest water level; guo is the water surface width at different water levels during the transition from the lowest water level to the highest water level; Δw is the centerline offset; tanα is the eccentric angle corresponding to the centerline offset.

[0208] S106-2: Based on the width of the water surface, a water depth calculation model is used to reconstruct the water depth of the underwater topography of the river.

[0209] The maximum water surface width at the highest water level and the water surface width at different water levels during the transition from the lowest water level to the highest water level are obtained, and the water depth calculation model is used to determine the water depth at different water levels corresponding to the fixed eccentric angle.

[0210] Based on the water depth at the lowest water level, the water depth of the terrain below the lowest water level is reconstructed, thereby completing the reconstruction of the terrain below the lowest water level.

[0211] S107: The terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest water level and the highest water level are spliced ​​to form a complete river terrain.

[0212] The TIN interpolation method or Kriging interpolation method is used to splice the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest water level and the highest water level to ensure a smooth transition between the three.

[0213] The embodiment of the present invention also provides a river terrain generation system, such as Figure 2 As shown, including:

[0214] The collection and processing module 201 is used to collect digital elevation model data, remote sensing image data, elevation data, daily water level data and large cross-section data of the river, and use the edge detection method to divide the river terrain into terrain above the highest water level, terrain below the lowest water level and terrain between the highest water level and the lowest water level, and extract the waterside line, center line and flow section.

[0215] The identification and division module 202 is used to calculate the relative stability index of the flow section based on the center line and the flow, use the relative stability index of the flow section to identify the stable river section in the river channel, and divide the stable river section into a curved river section and a straight river section.

[0216] The contour line generation module 203 is used to assign elevation values ​​to the waterside lines of the curved river section and the waterside lines of the straight river section using the elevation data and the daily water level data to obtain contour lines.

[0217] The first construction module 204 is used to construct the terrain above the highest water level using remote sensing image data.

[0218] The second construction module 205 is used to construct the terrain between the lowest water level and the highest water level based on the contour lines and water edge lines by using the elevation data and the daily water level data.

[0219] The third construction module 206 is used to construct the terrain below the lowest water level using the daily water level data and the large cross-section data.

[0220] The splicing module 207 is used to splice the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest water level and the highest water level to form a complete river terrain.

[0221] It should be noted here that the above-mentioned collection and processing module 201, identification and division module 202, contour generation module 203, first construction module 204, second construction module 205, third construction module 206 and splicing module 207 correspond to steps S101 to S107 in the embodiment of the river terrain generation method, and the examples and application scenarios implemented by the above-mentioned modules and corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned embodiments.

[0222] An embodiment of the present invention further provides a computer-readable storage medium storing a program or instruction. When the program or instruction is executed on a computer, the computer executes the river channel topography generation method as described in the above method embodiment.

[0223] like Figure 3 As shown, an embodiment of the present invention further provides an electronic device, comprising: a processor 301, the processor 301 is coupled to a memory 302, and the processor 301 is used to read and execute a computer program stored in the memory 302 to implement a river terrain generation method as described in the above method embodiment.

[0224] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for generating river terrain, characterized in that: include: Collect digital elevation model data, remote sensing image data, elevation data, daily water level data and large cross-section data of the river; use edge detection method to divide the river terrain into terrain above the highest water level, terrain below the lowest water level and terrain between the highest and lowest water levels, and extract the waterside line, center line and flow section; Calculating a relative stability index of the flow section based on the center line and the flow section, identifying a stable river section in the river channel using the relative stability index of the flow section, and dividing the stable river section into a curved river section and a straight river section; Assigning elevation values ​​to the waterside line of the curved river section and the waterside line of the straight river section using the elevation data and the daily water level data to obtain contour lines; constructing the terrain above the highest water level using the remote sensing image data; Using the elevation data and the daily water level data, constructing the terrain between the lowest water level and the highest water level based on the contour lines and the water edge lines; Constructing the terrain below the lowest water level using the daily water level data and the large cross-section data; The terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest water level and the highest water level are spliced ​​to form a complete river terrain.

2. The method according to claim 1, characterized in that The method of dividing the river terrain into terrain above the highest water level, terrain below the lowest water level, and terrain between the highest water level and the lowest water level by using the edge detection method includes: Extracting water surface feature lines from the digital elevation model data using the edge detection method; The edge detection method includes: noise smoothing; calculating gradient amplitude and gradient direction; performing non-maximum suppression along the gradient direction to obtain edge points; using a double threshold algorithm to detect and connect edge points to form edge lines; The water surface characteristic lines include water surface lines, shore lines and river valley boundary lines; The river channel terrain is divided by using the water surface characteristic line. The river channel terrain below the water surface line is divided into the terrain below the lowest water level, the river channel terrain between the water surface line and the shore line is divided into the terrain between the highest water level and the lowest water level, and the river channel terrain between the shore line and the river valley boundary line is divided into the terrain above the highest water level.

3. The method according to claim 1, characterized in that The relative stability index of the flow section is calculated based on the center line and the flow section, and the relative stability index of the flow section is used to identify the stable river section in the river channel, and the stable river section is divided into a curved river section and a straight river section, including: Obtaining an intersection point between the flow section and the center line, and recording the intersection point between the flow section and the center line as a second intersection point; Select the center lines of two adjacent times, merge the center lines of the two adjacent times, obtain the migration distance of the second intersection, record it as the intersection migration distance, and simultaneously obtain the intersection migration speed and the intersection migration acceleration; Calculating a relative stability index of the flow section; Determining a threshold value based on the relative stability index of the flow section, wherein the threshold value is used to judge whether the river section is stable; Determining a relative stability index of a river section based on the relative stability index of the flow section; By comparing the relative stability index of the river section with the threshold, the river section is divided into a stable river section and a swinging river section, thereby identifying the stable river section from the river channel; The stable river section is divided into a curved river section and a straight river section.

4. The method according to claim 3, characterized in that The relative stability index of the flow section is calculated by the following formula: RSIRR-G=β1s+β2v+β3a, Wherein, RSIRR-G refers to the relative stability index; s refers to the intersection migration distance; v refers to the intersection migration velocity; α refers to the intersection migration acceleration; β1 refers to the coefficient of the intersection migration distance; β2 refers to the coefficient of the intersection migration velocity; β3 refers to the coefficient of the intersection migration acceleration.

5. The method according to claim 4, characterized in that The coefficient of the intersection migration distance, the coefficient of the point migration speed and the coefficient of the intersection migration acceleration are calculated by cluster analysis method, including: The sections are grouped using a hierarchical clustering method to obtain a plurality of groups; Calculating an average value of the intersection migration distances corresponding to the cross sections contained in each of the groups, using the average value of the intersection migration distances to calculate the sum of squares between the groups to obtain a first sum of squares, using the average value of the intersection migration distances to calculate the sum of squares of errors between the groups to obtain a first sum of squares of errors, calculating a ratio of the first sum of squares to the first sum of squares of errors to obtain a first ratio, and using the first ratio as a coefficient of the intersection migration distance; Calculating an average value of the intersection migration velocities corresponding to the cross sections contained in each of the groups, calculating the sum of squares between the groups using the average value of the intersection migration velocities to obtain a second sum of squares, calculating the sum of squares of errors between the groups using the average value of the intersection migration velocities to obtain a second sum of squares of errors, calculating a ratio of the second sum of squares to the second sum of squares of errors to obtain a second ratio, and using the second ratio as a coefficient of the intersection migration velocity; Calculate the average value of the intersection migration acceleration corresponding to the cross-sections contained in each of the groups, use the average value of the intersection migration acceleration to calculate the sum of squares between the groups to obtain a third sum of squares, use the average value of the intersection migration acceleration to calculate the sum of squares of errors between the groups to obtain a third sum of squares of errors, calculate the ratio of the third sum of squares to the third sum of squares of errors to obtain a third ratio, and use the third ratio as the coefficient of the intersection migration acceleration.

6. The method according to claim 1, characterized in that The method of using elevation data and daily water level data to assign elevation values ​​to the waterside lines of curved river sections and the waterside lines of straight river sections to obtain contour lines includes: Assigning a first elevation value to the water edge of the curved river section using the elevation data; Assigning a second elevation value to the waterside line of the straight river section using the elevation data; Performing elevation point interpolation along the waterside line of the curved river section and the waterside line of the straight river section to obtain a third elevation value; Using the daily water level data, assigning a fourth elevation value to the waterside line of the curved river section and the waterside line of the straight river section; Along the waterside line of the curved river section and the waterside line of the straight river section, elevation points with the same elevation value are identified within the range of the first elevation value, the second elevation value, the third elevation value and the fourth elevation value, and the elevation points with the same elevation value are connected to obtain the contour lines.

7. The method according to claim 1, characterized in that The method of constructing the terrain above the highest water level using remote sensing image data includes: Acquire stereo image pair data, perform orthorectification and image fusion on the stereo image pair data to generate orthophotos, and extract digital surface models and point cloud data from the orthophoto data.

8. The method according to claim 1, characterized in that The method of using elevation data and daily water level data to construct the terrain between the lowest water level and the highest water level based on contour lines and water edge lines includes: Using the elevation data and the daily water level data, on the basis of the contour lines and the water edge lines, the terrain between the lowest water level and the highest water level is interpolated at elevation points to form continuous elevation points, thereby forming an elevation surface and obtaining a preliminary terrain construction result; Secondary interpolation is performed based on the preliminary terrain construction result to fill the gaps in elevation points between the lowest water level and the highest water level.

9. A river terrain generation system, characterized in that: include: The collection and processing module is used to collect the digital elevation model data, remote sensing image data, elevation data, daily water level data and large cross-section data of the river, and use the edge detection method to divide the river terrain into the terrain above the highest water level, the terrain below the lowest water level and the terrain between the highest water level and the lowest water level, and extract the waterside line, center line and flow section; an identification and division module, used for calculating a relative stability index of the flow section based on the center line and the flow section, identifying a stable river section in the river channel by using the relative stability index of the flow section, and dividing the stable river section into a curved river section and a straight river section; A contour line generation module, using the elevation data and the daily water level data to assign elevation values ​​to the waterside line of the curved river section and the waterside line of the straight river section, to obtain contour lines; A first construction module is used to construct the terrain above the highest water level using the remote sensing image data; A second construction module is used to construct the terrain between the lowest water level and the highest water level based on the contour lines and the water edge lines by using the elevation data and the daily water level data; A third construction module constructs the terrain below the lowest water level using the daily water level data and the large section data; The splicing module is used to splice the terrain below the lowest water level, the terrain above the highest water level, and the terrain between the lowest water level and the highest water level to form a complete river terrain.

10. A computer-readable storage medium, characterized in that: A program or instruction is stored, and when the program or instruction is run on a computer, the computer is caused to execute the river channel topography generation method as described in any one of claims 1 to 8.

11. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is used to read and execute the computer program stored in the memory to implement the river channel topography generation method as described in any one of claims 1-8.

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