A lake underwater topography dynamic modeling method based on remote sensing inversion and DEM fusion

Abstract

The application provides a lake underwater topography dynamic modeling method based on remote sensing inversion and DEM fusion, and relates to the technical field of data processing.The method comprises the following steps: in a specified lake underwater topography region, remote sensing water depth image data obtained through remote sensing inversion is acquired; the remote sensing water depth image data is input into a DEM model which has been constructed, and whether the spatial alignment degree is qualified is judged through spatial alignment degree analysis; if yes, S3 is entered; otherwise, control point parameter optimization processing is performed; the lake underwater topography region is acquired, and fusion smoothness analysis is performed on the data fusion process of the lake underwater topography region, and whether the fusion smoothness is qualified is judged; if yes, S4 is entered; otherwise, analysis window size optimization processing is performed; feature point alignment degree analysis is performed on the lake underwater three-dimensional topography model to be constructed, and whether the feature point alignment degree is qualified is judged; if yes, the next lake underwater topography dynamic modeling analysis instruction is sent; otherwise, grid rotation angle optimization is performed.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion. Background Technology

[0002] The scarcity of underwater topographic information in lakes has become a significant bottleneck in predicting and monitoring geological leak paths and changes along cross-mountain oil pipelines. To overcome the limitations of traditional monitoring methods in complex mountainous terrain and aquatic environments, remote sensing inversion involves analyzing electromagnetic wave signals acquired by sensors on satellites, aircraft, and other platforms. This is combined with physical models or statistical methods to infer physical, chemical, or biological parameters on the Earth's surface or in the atmosphere. Furthermore, digital elevation model (DEM) fusion technology integrates topographic data of different resolutions, sources, or types (such as satellite DEMs, lidar DEMs, and ground measurement data) through algorithms to generate a more accurate and complete topographic model. By comparing DEMs from different periods, the rate of topographic change can be calculated. Combined with rainfall, runoff, and human activity data, the causes of topographic changes can be analyzed, and regression or machine learning models of topographic evolution and hydrological and climatic parameters can be established.

[0003] Currently, existing technologies determine the central axis based on the lake's morphology and measure the underwater elevation along the axis. Several sampling points are generated along the lake shoreline and connected to the central axis to generate a lake cross-section. Using the measured elevation of the central axis as a constraint, the elevation values ​​of each grid in the underwater area of ​​the cross-section are inferred. Finally, a complete underwater topographic model of the lake is generated through spatial interpolation. By combining central axis control with cross-section extrapolation, the dependence on field measurement data is reduced to a certain extent.

[0004] However, in the process of fusing remote sensing inversion data and DEM data, it is usually necessary to unify data of different resolutions to the same spatial grid. Existing interpolation methods, based on distance weighting or trend surface fitting weight allocation rules, naturally tend to eliminate local abrupt changes, resulting in the excessive smoothing of micro-topographic features such as shoals and reefs in high-resolution remote sensing data. At the same time, the inherent differences between the two types of data in terms of spatial reference and update frequency further exacerbate the distortion of details in the fused terrain. Summary of the Invention

[0005] To address the challenge of unifying data from different resolutions to the same spatial grid during the fusion of remote sensing inversion data and DEM data, existing interpolation methods, based on distance-weighted or trend surface fitting weight allocation rules, naturally tend to eliminate local abrupt changes, leading to excessive smoothing of micro-topographic features such as shoals and reefs in high-resolution remote sensing data. Furthermore, the inherent differences in spatial reference and update frequency between the two types of data exacerbate the technical problem of distorted topographic details after fusion. This invention provides a dynamic modeling method for underwater topography of lakes based on the fusion of remote sensing inversion and DEM.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] The first aspect of this invention provides a method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion, comprising:

[0008] S1: Obtain remote sensing water depth image data retrieved from the underwater topography of the designated lake area;

[0009] S2: Input the remote sensing water depth image data into the constructed DEM model, and determine whether the spatial alignment is qualified through spatial alignment analysis; if yes, proceed to S3; otherwise, perform control point parameter optimization processing.

[0010] S3: Obtain the underwater topographic region of the lake and perform a fusion smoothness analysis on the data fusion process of the underwater topographic region of the lake, and determine whether the fusion smoothness is qualified; if yes, proceed to S4; otherwise, perform analysis window size optimization processing.

[0011] S4: Perform feature point alignment analysis on the constructed underwater 3D terrain model of the lake to determine whether the feature point alignment is qualified; if so, send the next dynamic modeling and analysis command for the underwater terrain of the lake; otherwise, optimize the mesh rotation angle.

[0012] A second aspect of this invention provides a dynamic modeling system for underwater topography of lakes based on remote sensing inversion and DEM fusion, comprising:

[0013] processor;

[0014] The memory stores computer-readable instructions, which, when executed by the processor, implement the dynamic modeling method for underwater topography of lakes based on remote sensing inversion and DEM fusion as described in the first aspect.

[0015] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0016] In this invention, spatial alignment analysis of remote sensing water depth image data and DEM image data is performed. Before data fusion, the differences in spatial resolution and temporal update frequency are quantitatively assessed, and control point parameters are optimized accordingly, significantly improving the spatial consistency of multi-source data. By performing fusion smoothness analysis on the fusion process and dynamically optimizing the analysis window size, the excessive smoothing of micro-topographic features by traditional interpolation methods is effectively suppressed while ensuring a natural connection between the land and water transition zones. Furthermore, feature point alignment analysis is performed during the regular grid division stage, and the grid rotation angle is adaptively optimized, significantly improving the spatial matching accuracy between grid points and original topographic data points. Through this three-level closed-loop feedback mechanism of "analysis-judgment-optimization," the technical problems of low alignment of underwater topographic feature points and severe micro-topographic distortion caused by insufficient spatial detail capture capabilities in existing technologies are systematically solved, thereby achieving a synergistic improvement in the accuracy and realism of 3D modeling of lake underwater topography. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for dynamic modeling of underwater topography in lakes based on remote sensing inversion and DEM fusion, as provided in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of a dynamic modeling system for underwater topography of lakes based on remote sensing inversion and DEM fusion, provided as an embodiment of the present invention. Detailed Implementation

[0020] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0021] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0022] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0023] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0024] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0025] Reference manual attached Figure 1 The diagram illustrates a flowchart of a dynamic modeling method for underwater topography of lakes based on remote sensing inversion and DEM fusion, provided by an embodiment of the present invention.

[0026] This invention provides a method for dynamic modeling of underwater topography in lakes based on remote sensing inversion and DEM fusion. This method can be implemented using a device for dynamic modeling of underwater topography in lakes based on remote sensing inversion and DEM fusion, which can be a terminal or a server. The processing flow of this method may include the following steps:

[0027] S1: Obtain remote sensing water depth image data retrieved from the underwater topography of the designated lake area.

[0028] Remote sensing inversion refers to the process of retrieving physical parameters of the Earth's surface or water body by analyzing electromagnetic wave signals (such as visible light, infrared, microwave, etc.) acquired by sensors carried on platforms such as satellites and aircraft, and combining them with physical models or statistical methods (such as radiative transfer models, single-window algorithms, empirical regression, etc.).

[0029] S2: Input the remote sensing water depth image data into the constructed DEM model, and determine whether the spatial alignment is satisfactory through spatial alignment analysis. If yes, proceed to S3. Otherwise, perform control point parameter optimization.

[0030] Among them, the DEM model, or Digital Elevation Model, refers to the digital representation of the Earth's surface elevation obtained through satellite stereo image pairs, radar interferometry, lidar, or ground surveying, and stored in the form of a regular grid or an irregular triangular network.

[0031] Spatial alignment refers to the degree of geometric registration consistency between remote sensing water depth image data and DEM image data in the same geographic coordinate system at corresponding spatial locations.

[0032] Specifically, the constructed DEM model contains DEM image data of the same area as the remote sensing water depth image data. Spatial alignment analysis is used to quantify the alignment between the remote sensing water depth image data and the DEM image data in the corresponding spatial locations within the constructed DEM model. Control point parameter optimization is used to reduce the interference of the number of control points and the proportion of edge control points on spatial alignment.

[0033] Specifically, the spatial alignment data and the maximum permissible spatial alignment data in the database are compared to calculate the difference. The results of each difference calculation are then summed and averaged to obtain the spatial alignment interference value. The spatial alignment data includes spatial resolution error and temporal resolution error. The spatial resolution error reflects the spatial resolution difference between the remote sensing water depth image data and the DEM image data, representing the absolute value of the difference between the spatial resolution of the remote sensing water depth image data in the DEM model and the spatial resolution of the DEM image data in the DEM model. The spatial resolution is monitored by a high-precision optical sensor. The temporal resolution error reflects the difference in data update frequency between the remote sensing water depth image data and the DEM image data, representing the absolute value of the difference between the data update frequency of the remote sensing water depth image data in the DEM model and the data update frequency of the DEM image data in the DEM model. The data update frequency is monitored by a time interval analyzer. The spatial alignment interference value represents the quantitative data on the degree of spatial alignment interference. The maximum permissible spatial alignment data includes the maximum permissible spatial resolution error value and the maximum permissible temporal resolution error value.

[0034] Furthermore, the results of each difference calculation include spatial resolution error value score and temporal resolution error value score. The spatial alignment interference value represents the summation and averaging of the spatial resolution error value score and the temporal resolution error value score. The spatial resolution error value score represents the ratio of the obtained spatial resolution error value to the maximum allowable spatial resolution error value. The temporal resolution error value score represents the ratio of the obtained temporal resolution error value to the maximum allowable temporal resolution error value. The maximum allowable spatial resolution error value is represented by the summation and averaging of the maximum values ​​of all historical spatial resolution error values ​​in the database. The maximum allowable temporal resolution error value is represented by the summation and averaging of the maximum values ​​of all historical temporal resolution error values ​​in the database.

[0035] It should be noted that the spatial alignment interference value increases with the increase of spatial resolution error and temporal resolution error. Specifically, when the spatial resolution difference between remote sensing water depth image data and DEM image data increases, the resolution of the fused image decreases. This may mask short-term topographic changes, and a longer time interval will amplify this masking effect, causing the alignment analysis to be unable to distinguish between insufficient spatial sampling and actual topographic changes. When the spatial resolution error value increases, the temporal resolution error value also increases. The temporal resolution error will cause dynamic distortion of the spatial reference system, resulting in the "same location" described by the two actually corresponding to different topographic features, thus increasing the spatial resolution error value.

[0036] In this embodiment of the invention, spatial resolution determines the spatial fineness of the data, while temporal resolution reflects the time interval between data acquisitions. Spatial alignment analysis considers both spatial and temporal dimensions to avoid model distortion caused by errors in a single dimension (e.g., insufficient temporal resolution may lead to dynamic terrain being misjudged as static features). By comprehensively considering the impact of spatial resolution error values ​​and temporal resolution error values ​​on spatial alignment interference values, the quality of spatial alignment can be evaluated more comprehensively and accurately. This helps to promptly identify problems in the data, rationally select spatial and temporal resolutions to reduce errors, capture the process and patterns of geospatial changes, and provide a more accurate data foundation for dynamic monitoring and analysis.

[0037] In one possible implementation, spatial alignment analysis specifically includes sub-steps S201 and S202:

[0038] S201: Calculate the difference between the acquired spatial alignment data and the maximum allowed spatial alignment data in the database.

[0039] The spatial alignment data includes spatial resolution error and temporal resolution error. The spatial resolution error reflects the difference in spatial resolution between the remotely sensed water depth image data and the DEM image data. The temporal resolution error reflects the difference in data update frequency between the remotely sensed water depth image data and the DEM image data.

[0040] S202: The results of each difference calculation are summed and averaged to obtain the spatial alignment interference value. The spatial alignment interference value represents the quantitative data on the degree of interference between the spatial alignment data and the spatial alignment.

[0041] In one possible implementation, it is determined whether the spatial alignment is satisfactory. If yes, proceed to S3. Otherwise, the control point parameter optimization process specifically includes:

[0042] If the obtained spatial alignment interference value is not greater than the preset spatial alignment interference value in the database, the spatial alignment is deemed qualified, and the process proceeds to step S3 for fusion smoothness analysis.

[0043] If the obtained spatial alignment interference value is greater than the preset spatial alignment interference value in the database, the spatial alignment is deemed unqualified, and the number of control points is optimized.

[0044] It should be noted that those skilled in the art can set the preset spatial alignment interference value according to actual needs, and this invention does not limit it.

[0045] In one possible implementation, optimizing the number of control points specifically includes:

[0046] The number of control points is adjusted by mapping the spatial alignment interference value deviation to the database. Based on the adjusted number of control points, the GNSS receiver is prompted to improve the geometric correction accuracy of the spatial position.

[0047] Among them, a GNSS receiver, or Global Navigation Satellite System receiver, refers to a high-precision positioning device that can receive navigation satellite signals such as BeiDou, GPS, GLONASS, and Galileo, and obtain the three-dimensional coordinates of points on the ground through carrier phase measurement or pseudorange positioning technology.

[0048] After optimizing the number of control points, it is determined whether the deviation of the reacquired spatial alignment interference value is greater than zero. If so, the edge control point proportion adjustment value is mapped from the database based on the deviation of the reacquired spatial alignment interference value. Based on the edge control point proportion adjustment value, the GNSS receiver is prompted to reduce geometric distortion and local mismatch. Otherwise, the edge control point proportion optimization is completed and fusion smoothness analysis is performed.

[0049] After optimizing the proportion of edge control points, the newly acquired remote sensing water depth image data is input into the polynomial correction model in the DEM model for image smoothness correction. The deviation of the spatial alignment interference value obtained after image smoothness correction is then checked to see if it is greater than zero. If so, a warning is issued for the spatial alignment process. Otherwise, control point parameter optimization is completed, and fusion smoothness analysis is performed.

[0050] Specifically, based on the acquired spatial alignment interference value and the preset spatial alignment interference value in the database, it is determined whether there is a need for control point number optimization: if the acquired spatial alignment interference value is not greater than the preset spatial alignment interference value in the database, the spatial alignment is deemed acceptable (i.e., there is no need for control point number optimization), and fusion smoothness analysis is performed. If the acquired spatial alignment interference value is greater than the preset spatial alignment interference value in the database, the spatial alignment is deemed unacceptable (i.e., there is a need for control point number optimization), and control point number optimization is performed. The preset spatial alignment interference value is represented by the sum and average of historical spatial alignment interference values ​​in the database.

[0051] Furthermore, based on the acquired spatial alignment interference value deviation, a control point quantity adjustment value is mapped into the database. This prompts the GNSS (Global Navigation Satellite System) receiver to improve the geometric correction accuracy of spatial position based on the acquired control point quantity adjustment value. The spatial alignment interference value deviation represents the difference between the acquired spatial alignment interference value and the preset spatial alignment interference value. After the control point quantity optimization, if the reacquired spatial alignment interference value deviation is greater than 0, an edge control point proportion adjustment value is mapped into the database based on the acquired spatial alignment interference value deviation. This prompts the GNSS receiver to reduce geometric distortion and local mismatch based on the acquired edge control point proportion adjustment value. Otherwise, edge control point proportion optimization is completed and fusion smoothness analysis is performed. After edge control point proportion optimization, the reacquired remote sensing water depth image data is input into the polynomial correction model in the DEM model for image smoothness correction. If the reacquired spatial alignment interference value deviation is greater than 0 after image smoothness correction, a spatial alignment process warning is issued; otherwise, control point parameter optimization is completed and fusion smoothness analysis is performed.

[0052] Furthermore, by using the particle swarm optimization algorithm to take the adjustment values ​​of the number of control points and the adjustment values ​​of the proportion of edge control points as inputs, each particle is regarded as an adjustment scheme of control point parameters. By continuously updating the velocity and position of the particles, the current fitness value of each particle is compared with its individual historical best fitness value, and the optimal control point parameter values ​​are searched in the solution space. Finally, the optimized increase value of the number of control points and the decrease value of the proportion of edge control points are output.

[0053] In this embodiment of the invention, through spatial alignment analysis and control point parameter optimization, the invention significantly improves the consistency between remote sensing water depth data and DEM data in terms of spatial reference and geometric details, effectively suppresses registration errors and edge distortions before multi-source data fusion, and provides a reliable data foundation for subsequent high-precision underwater terrain modeling.

[0054] S3: Acquire the underwater topographic region of the lake and perform a fusion smoothness analysis on the data fusion process of the underwater topographic region of the lake, and determine whether the fusion smoothness is qualified. If yes, proceed to S4. Otherwise, optimize the analysis window size.

[0055] Among them, fusion smoothness refers to the smoothness and naturalness of the transition between the elevation gradients of the two in the water-land interface zone and the data overlap area when the spatially aligned remote sensing inversion water depth data and DEM data are fused in the underwater topographic region of the lake.

[0056] Specifically, if the spatial alignment is deemed satisfactory, the process proceeds to the shoreline generation preprocessing stage. Simultaneously, the underwater topographic region of the lake is acquired, and a fusion smoothness analysis is performed on the data fusion process to determine whether analysis window size optimization is necessary. The shoreline generation preprocessing stage involves inputting the spatially aligned remote sensing inversion water depth data and DEM data into GIS software, and using the GIS software to generate a shoreline mask (used to extract the lake area or eliminate land interference). The fusion smoothness analysis reflects the degree of connection between the spatially aligned remote sensing inversion water depth data and DEM data in the underwater topographic region of the lake (a gradual transition is a technique that uses spatial location weighting or interpolation methods to achieve a natural connection between the two data sets in the boundary area (such as the water-land transition zone or data overlap area) of the underwater topographic region of the lake). Analysis window size optimization reduces the interference of the analysis window size on the fusion smoothness.

[0057] Furthermore, the difference between the acquired fusion smoothness data and the maximum permissible fusion smoothness data in the database is calculated. The results of each difference calculation are then summed and averaged to obtain the fusion smoothness interference value. The fusion smoothness data includes the transition band width deviation and the average distance deviation of feature points. The transition band width deviation reflects the difference between the transition band width and the preset transition band width, representing the absolute value of the difference. The transition band width is measured using a spectrum analyzer, and the preset transition band width is represented by the summed and averaged results of historical transition band widths in the database. The average distance deviation of feature points reflects the average difference in spatial distances from each sampling point to the feature points, representing the absolute value of the difference between the average spatial distance deviation and the preset average spatial distance deviation. The spatial distance is measured using a 3D laser scanner, and the preset average spatial distance deviation is represented by the summed and averaged results of historical average spatial distance deviations in the database. The fusion smoothness interference value represents the quantitative data on the influence of the fusion smoothness data on the fusion smoothness. The maximum permissible fusion smoothness data includes the maximum permissible transition band width deviation and the maximum permissible average distance deviation of feature points.

[0058] Furthermore, the results of each difference calculation include the transition band width deviation score and the feature point average distance deviation score. The fusion smoothness interference value represents the summation and averaging of the transition band width deviation score and the feature point average distance deviation score. The transition band width deviation score represents the ratio of the obtained transition band width deviation to the maximum allowable transition band width deviation. The data point average distance deviation score represents the ratio of the obtained data point average distance deviation to the maximum allowable data point average distance deviation. The maximum allowable transition band width deviation is represented by the summation and averaging of the maximum values ​​of all historical transition band width deviations in the database. The maximum allowable feature point average distance deviation is represented by the summation and averaging of the maximum values ​​of all historical feature point average distance deviations in the database.

[0059] Furthermore, the fusion smoothness interference value increases with the increase of the transition band width deviation and the feature point average distance deviation. Specifically, when the transition band width deviation increases, the feature point average distance calculation may incorrectly include areas that should belong to a single data source in the transition band, causing the actual distance from some points to the data source to be stretched, thus increasing the feature point average distance deviation. When the feature point average distance deviation increases, the system may automatically expand the transition band width to "fill" the data gaps by using iterative optimization algorithms (such as the least squares method) to increase the transition band width to reduce the average distance deviation, which in turn increases the transition band width deviation.

[0060] In one possible implementation, the data fusion process for underwater topographic regions of lakes includes fusion smoothness analysis, specifically comprising sub-steps S301 and S302:

[0061] S301: Calculate the difference between the acquired fusion smoothness data and the maximum allowable fusion smoothness data in the database. The fusion smoothness data includes the transition band width deviation and the average distance deviation of feature points. The transition band width deviation reflects the difference between the transition band width and the preset transition band width, and the average distance deviation of feature points reflects the average difference in the spatial distance from each sampling point to the feature point.

[0062] S302: The results of each difference calculation are summed and averaged to obtain the fusion smoothness interference value. The fusion smoothness interference value represents the quantitative data on the degree of influence of the fusion smoothness data on the fusion smoothness.

[0063] In one possible implementation, it is determined whether the blending smoothness is satisfactory. If yes, proceed to S4. Otherwise, perform analysis window size optimization processing:

[0064] If the obtained fusion smoothness interference value is not greater than the preset fusion smoothness interference value in the database, the fusion smoothness is deemed qualified, and the process proceeds to step S4 for feature point alignment analysis.

[0065] If the obtained blending smoothness interference value is greater than the preset blending smoothness interference value in the database, the blending smoothness is deemed unqualified, and the window size is optimized.

[0066] It should be noted that those skilled in the art can set the preset spatial position distance deviation, preset transition band width, and preset fusion smoothness interference value according to actual needs, and the present invention does not limit these settings.

[0067] In one possible implementation, window size optimization specifically includes:

[0068] The obtained blending smoothness interference value deviation is mapped to the database to obtain the window size adjustment value. Based on the window size adjustment value, a large-memory graphics processor is suggested to improve the terrain detail capture capability.

[0069] Among them, a large-memory graphics processor refers to a graphics processing unit (GPU) equipped with high-capacity video memory (usually 8GB or more).

[0070] The adjusted window size is input into the GIS software for window size update, generating updated lake underwater topography fusion data. The deviation of the newly acquired fusion smoothness interference value is checked to see if it is greater than zero. If so, an early warning is issued for the fusion smoothing process. Otherwise, window size optimization is completed and feature point alignment analysis is performed.

[0071] Among them, GIS software, or Geographic Information System software, refers to professional software platforms (such as ArcGIS, QGIS, SuperMap, etc.) used for geospatial data acquisition, storage, management, analysis, modeling, and visualization.

[0072] Specifically, based on the acquired spatial alignment interference value and the preset blending smoothness interference value in the database, it is determined whether there is a need for window size optimization: if the acquired blending smoothness interference value is not greater than the preset blending smoothness interference value in the database, the blending smoothness is deemed acceptable (i.e., there is no need for window size optimization), and feature point alignment analysis is performed. If the acquired blending smoothness interference value is greater than the preset blending smoothness interference value in the database, the blending smoothness is deemed unacceptable (i.e., there is a need for window size optimization), and window size optimization is performed. The preset blending smoothness interference value is represented by the average of the summation of historical blending smoothness interference values ​​in the database.

[0073] Furthermore, based on the acquired fusion smoothness interference value deviation, a window size adjustment value is mapped in the database. This prompts the large-memory graphics processor to improve its terrain detail capture capability based on the acquired window size adjustment value. The fusion smoothness interference value deviation represents the difference between the acquired fusion smoothness interference value and the preset fusion smoothness interference value. The adjusted window size is input into the GIS software for window size update, generating updated lake underwater terrain fusion data. If the newly acquired fusion smoothness interference value deviation is greater than 0, an early warning for the fusion smoothing process is issued; otherwise, window size optimization is completed and feature point alignment analysis is performed.

[0074] In this embodiment of the invention, by combining smoothness analysis and window size optimization, the invention significantly suppresses elevation abrupt changes and splicing traces in the water-land transition zone. While preserving micro-topographic details such as shoals and reefs, it achieves natural gradual transitions between multi-source data, greatly improving the geometric continuity and topographic realism of the underwater topographic fusion model.

[0075] S4: Perform feature point alignment analysis on the constructed underwater 3D terrain model of the lake to determine if the feature point alignment is satisfactory. If yes, send the next dynamic modeling and analysis command for the underwater terrain of the lake. Otherwise, optimize the mesh rotation angle.

[0076] Among them, grid rotation angle optimization refers to the optimization process in the regular grid division stage, which adjusts the angle between the grid coordinate system and the main axis of the terrain features to achieve the best match between the spatial distribution of grid points and the spatial position of natural feature points (such as deep channel edges, reef apex, abrupt slope change points, etc.) in the underwater terrain data.

[0077] Specifically, if the obtained regular grid resolution is greater than the preset regular grid resolution, it indicates that the resolution of the regular grid meets the requirements for obtaining the average distance of data points and the interpolation search radius, and the average distance of data points and the interpolation search radius, i.e., the feature point alignment data, are obtained. Otherwise, a grid resolution deviation alarm command is sent to the preset staff. The regular grid resolution represents the side length of the regular grid cell, which is measured by a single-beam echo sounder. The preset regular grid resolution is represented by the result of summing and averaging the historical grid resolutions in the database.

[0078] Furthermore, the relative difference between the acquired feature point alignment data and the preset feature point alignment data in the database is calculated. The results of each relative difference calculation are then summed and averaged to obtain the feature point alignment interference value. The average distance between data points represents the average distance between adjacent data points, reflecting the sparsity of the data point distribution, and is measured using a laser scanner. The interpolation search radius represents the area extending outward relative to the target grid point, also measured using a laser scanner. The feature point alignment interference value represents the quantified degree of interference of the feature point alignment data on the feature point alignment. The preset feature point alignment data includes the preset average distance between data points and the preset interpolation search radius. The preset average distance between data points is represented by the summed and averaged result of the average distances of historical data points in the database, and the preset interpolation search radius is represented by the summed and averaged result of the historical interpolation search radii in the database.

[0079] Furthermore, the results of each relative difference calculation include the average distance score of data points and the interpolation search radius score. The feature point alignment interference value represents the summation and averaging of the average distance score of data points and the interpolation search radius score. The average distance score of data points represents the ratio of the absolute value of the difference between the obtained average distance of data points and the preset average distance of data points to the preset average distance of data points. The interpolation search radius score represents the ratio of the absolute value of the difference between the obtained interpolation search radius and the preset interpolation search radius to the preset interpolation search radius.

[0080] It should be noted that the feature point alignment interference value increases with the increase of the average distance deviation of data points (i.e., the absolute value of the difference between the average distance of the acquired data points and the preset average distance of data points) and the interpolation search radius deviation (i.e., the absolute value of the difference between the acquired interpolation search radius and the preset interpolation search radius). When the average distance of data points increases, since there are enough data points in the neighborhood, a smaller one can meet the interpolation requirements. At this time, the interpolation search radius is reduced to avoid interference from distant data. In adaptive Kriging interpolation, the interpolation search radius can be dynamically changed locally according to the average distance of data points. When the average distance of data points decreases, the data is dense, and the interpolation search radius automatically shrinks to avoid over-smoothing. When the average distance of data points increases, the data is sparse, and the interpolation search radius automatically expands to prevent data loss.

[0081] In one possible implementation, the feature point alignment analysis of the underwater 3D terrain model of the lake to be constructed specifically includes:

[0082] S401: Determine if the acquired regular grid resolution is greater than the preset regular grid resolution. If yes, acquire the average distance between data points and the interpolation search radius. Otherwise, send a grid resolution deviation alarm command to the preset staff.

[0083] S402: Calculate the relative difference between the acquired feature point alignment data and the preset feature point alignment data in the database. Then, sum and average the results of each relative difference calculation to obtain the feature point alignment interference value.

[0084] In one possible implementation, the alignment of feature points is determined to be satisfactory. If so, the next dynamic modeling and analysis command for the underwater topography of the lake is sent. Otherwise, mesh rotation angle optimization is performed, specifically including:

[0085] If the obtained feature point alignment interference value is not greater than the preset feature point alignment interference value in the database, the feature point alignment is deemed qualified and the next lake underwater terrain dynamic modeling and analysis command is sent.

[0086] If the obtained feature point alignment interference value is greater than the preset feature point alignment interference value in the database, the feature point alignment is deemed unqualified and the mesh rotation angle is optimized.

[0087] It should be noted that those skilled in the art can set the preset rule grid resolution, preset feature point alignment interference value, and preset feature point alignment data size according to actual needs, and this invention does not limit these settings.

[0088] In one possible implementation, the mesh rotation angle optimization specifically includes:

[0089] The mesh rotation angle is adjusted by mapping the obtained feature point alignment interference value deviation to the database. Based on the mesh rotation angle adjustment, the GPU's stream processor is prompted to improve the uniformity of feature point distribution in the regular mesh.

[0090] In this context, the stream processor of a GPU refers to the core computing unit (also known as the shader core or CUDA core) integrated within the graphics processing unit (GPU) for parallel processing of massive vector and matrix operations.

[0091] The optimized mesh rotation angle is input into the underwater 3D terrain model of the lake to be constructed for feature point distribution verification. The system then checks whether the alignment interference value deviation of the newly acquired feature points after the feature point distribution verification is greater than zero. If so, an alert is issued for the feature point alignment process. Otherwise, the next dynamic modeling and analysis command for the underwater terrain of the lake is sent.

[0092] Specifically, based on the acquired feature point alignment interference value and the preset feature point alignment interference value in the database, it is determined whether there is a need for mesh rotation angle optimization: if the acquired feature point alignment interference value is not greater than the preset feature point alignment interference value in the database, the feature point alignment is deemed acceptable (i.e., there is no need for mesh rotation angle optimization), and the next lake underwater terrain dynamic modeling analysis command is sent. If the acquired feature point alignment interference value is greater than the preset feature point alignment interference value in the database, the feature point alignment is deemed unacceptable (i.e., there is a need for mesh rotation angle optimization), and mesh rotation angle optimization is performed. The preset feature point alignment interference value is represented by the sum and average of historical feature point alignment interference values ​​in the database.

[0093] Furthermore, based on the acquired feature point alignment interference value deviation, a mesh rotation angle adjustment value is mapped in the database. This prompts the GPU (Graphics Processing Unit) stream processor to adjust the mesh rotation angle to improve the uniformity of feature point distribution within the regular mesh. The feature point alignment interference value deviation represents the difference between the acquired feature point alignment interference value and the preset feature point alignment interference value. The optimized mesh rotation angle is input into the underwater 3D terrain model of the lake to be constructed for feature point distribution verification. If the re-acquired feature point alignment interference value deviation after verification is greater than 0, a feature point alignment process warning is issued; otherwise, the next dynamic modeling and analysis command for the underwater terrain of the lake is sent.

[0094] In this embodiment of the invention, by analyzing the alignment of feature points and optimizing the mesh rotation angle, the invention significantly improves the spatial matching accuracy between regular mesh points and underwater terrain feature points, effectively solving the problem of feature information attenuation and position drift caused by the incoordination between the mesh direction and the main axis of the terrain, thereby greatly improving the geometric fidelity and spatial positioning accuracy of the three-dimensional terrain model for underwater micro-topography.

[0095] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0096] In this invention, spatial alignment analysis of remote sensing water depth image data and DEM image data is performed. Before data fusion, the differences in spatial resolution and temporal update frequency are quantitatively assessed, and control point parameters are optimized accordingly, significantly improving the spatial consistency of multi-source data. By performing fusion smoothness analysis on the fusion process and dynamically optimizing the analysis window size, the excessive smoothing of micro-topographic features by traditional interpolation methods is effectively suppressed while ensuring a natural connection between the land and water transition zones. By performing feature point alignment analysis during the regular grid division stage and adaptively optimizing the grid rotation angle, the spatial matching accuracy between grid points and original topographic data points is significantly improved. Through the aforementioned three-level closed-loop feedback mechanism of "analysis-judgment-optimization," the technical problems of low alignment of underwater topographic feature points and severe micro-topographic distortion caused by insufficient spatial detail capture capabilities in existing technologies are systematically solved, thereby achieving a synergistic improvement in the accuracy and realism of 3D modeling of lake underwater topography.

[0097] Reference manual attached Figure 2 The diagram shows a schematic of the structure of a dynamic modeling system for underwater topography of lakes based on remote sensing inversion and DEM fusion provided by the present invention.

[0098] This invention also provides a lake underwater topography dynamic modeling system 20 based on remote sensing inversion and DEM fusion, applied to the aforementioned lake underwater topography dynamic modeling method based on remote sensing inversion and DEM fusion, comprising:

[0099] Processor 201.

[0100] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, they implement the dynamic modeling method for underwater topography of lakes based on remote sensing inversion and DEM fusion as described in the method embodiment.

[0101] The lake underwater topography dynamic modeling system 20 based on remote sensing inversion and DEM fusion provided by the present invention can execute the above-mentioned lake underwater topography dynamic modeling method based on remote sensing inversion and DEM fusion and achieve the same or similar technical effects. To avoid duplication, the present invention will not elaborate further.

[0102] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0103] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0105] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0106] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0107] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0108] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0109] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0110] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0111] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0112] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0113] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a dynamic modeling method for underwater topography of lakes based on remote sensing inversion and DEM fusion, as described in the method embodiment.

[0115] The present invention provides a computer-readable storage medium that can implement the steps and effects of the above-described method embodiment of the dynamic modeling method for underwater topography of lakes based on remote sensing inversion and DEM fusion. To avoid repetition, the present invention will not repeat the details.

[0116] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0117] The following points need to be explained:

[0118] (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0119] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0120] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0121] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion, characterized in that, include: S1: Obtain remote sensing water depth image data retrieved from the underwater topography of the designated lake area; S2: Input the remote sensing water depth image data into the constructed DEM model, and determine whether the spatial alignment is qualified through spatial alignment analysis; if yes, proceed to S3; otherwise, perform control point parameter optimization processing. S3: Obtain the underwater topographic region of the lake and perform a fusion smoothness analysis on the data fusion process of the underwater topographic region of the lake, and determine whether the fusion smoothness is qualified; if yes, proceed to S4; otherwise, perform analysis window size optimization processing. S4: Perform feature point alignment analysis on the constructed underwater 3D terrain model of the lake, and determine whether the feature point alignment is qualified; if so, send the next underwater terrain dynamic modeling analysis command. Otherwise, optimize the mesh rotation angle.

2. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 1, characterized in that, The spatial alignment analysis specifically includes: S201: Calculate the difference between the acquired spatial alignment data and the maximum allowed spatial alignment data in the database; S202: The results of each difference calculation are summed and averaged to obtain the spatial alignment interference value; wherein, the spatial alignment interference value represents the quantitative data of the degree of interference of the spatial alignment data on the spatial alignment.

3. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 1, characterized in that, The determination is made as to whether the spatial alignment is qualified. If so, proceed to S3; Otherwise, the control point parameter optimization process specifically includes: If the obtained spatial alignment interference value is not greater than the preset spatial alignment interference value in the database, the spatial alignment is determined to be qualified, and the process proceeds to step S3 for fusion smoothness analysis. If the obtained spatial alignment interference value is greater than the preset spatial alignment interference value in the database, the spatial alignment is determined to be unqualified, and the number of control points is optimized.

4. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 3, characterized in that, The optimization of the number of control points specifically includes: The control point number adjustment value is obtained by mapping the spatial alignment interference value deviation to the database; based on the control point number adjustment value, the GNSS receiver is prompted to improve the geometric correction accuracy of the spatial position. After optimizing the number of control points, it is determined whether the deviation of the reacquired spatial alignment interference value is greater than zero. If so, the edge control point ratio adjustment value is obtained by mapping the reacquired spatial alignment interference value deviation to the database. Based on the edge control point ratio adjustment value, the GNSS receiver is prompted to reduce geometric distortion and local mismatch. Otherwise, the edge control point ratio optimization is completed and fusion smoothness analysis is performed. After optimizing the proportion of edge control points, the newly acquired remote sensing water depth image data is input into the polynomial correction model in the DEM model for image smoothness correction. It is then determined whether the deviation of the spatial alignment interference value obtained after image smoothness correction is greater than zero. If so, a warning is issued for the spatial alignment process; otherwise, the control point parameters are optimized and fusion smoothness analysis is performed.

5. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 1, characterized in that, The data fusion process for the underwater topographic region of the lake specifically includes the following: S301: Calculate the difference between the acquired fusion smoothness data and the maximum allowable fusion smoothness data in the database; wherein, the fusion smoothness data includes the transition band width deviation and the feature point average distance deviation, the transition band width deviation is used to reflect the difference between the transition band width and the preset transition band width, and the feature point average distance deviation is used to reflect the average difference in the spatial distance from each sampling point to the feature point. S302: The results of each difference calculation are summed and averaged to obtain the fusion smoothness interference value; wherein, the fusion smoothness interference value represents the quantitative data of the degree of influence of the fusion smoothness data on the fusion smoothness.

6. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 5, characterized in that, The process involves determining whether the fusion smoothness is satisfactory; if yes, proceed to step S4; otherwise, optimize the analysis window size. If the obtained fusion smoothness interference value is not greater than the preset fusion smoothness interference value in the database, the fusion smoothness is determined to be qualified, and the process proceeds to step S4 to perform feature point alignment analysis. If the obtained blending smoothness interference value is greater than the preset blending smoothness interference value in the database, the blending smoothness is determined to be unqualified, and the window size is optimized.

7. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 6, characterized in that, The window size optimization specifically includes: The obtained fusion smoothness interference value deviation is mapped to the database to obtain the window size adjustment value; based on the window size adjustment value, the large video memory graphics processor is prompted to improve the terrain detail capture capability; The adjusted window size is input into the GIS software to update the window size and generate updated underwater topographic fusion data of the lake. It is then determined whether the deviation of the newly acquired fusion smoothness interference value is greater than zero. If so, an early warning is issued for the fusion smoothing process; otherwise, the window size is optimized and feature point alignment analysis is performed.

8. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 6, characterized in that, The feature point alignment analysis of the underwater 3D terrain model of the lake to be constructed specifically includes: S401: Determine whether the acquired regular grid resolution is greater than the preset regular grid resolution; if so, acquire the average distance of data points and the interpolation search radius; otherwise, send a grid resolution deviation alarm command to the preset staff. S402: Calculate the relative difference between the acquired feature point alignment data and the preset feature point alignment data in the database; and sum and average the results of each relative difference calculation to obtain the feature point alignment interference value.

9. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 8, characterized in that, The step is to determine whether the alignment of the feature points is satisfactory. If so, then send the next command for dynamic modeling and analysis of the underwater topography of the lake; Otherwise, optimizing the mesh rotation angle specifically includes: If the obtained feature point alignment interference value is not greater than the preset feature point alignment interference value in the database, the feature point alignment is determined to be qualified and the next lake underwater terrain dynamic modeling analysis command is sent. If the obtained feature point alignment interference value is greater than the preset feature point alignment interference value in the database, the feature point alignment is determined to be unqualified and the mesh rotation angle is optimized.

10. The method for dynamic modeling of underwater topography of lakes based on remote sensing inversion and DEM fusion as described in claim 9, characterized in that, The optimization of the grid rotation angle specifically includes: The obtained feature point alignment interference value deviation is mapped to the database to obtain the grid rotation angle adjustment value; based on the grid rotation angle adjustment value, the GPU's stream processor is prompted to improve the uniformity of feature point distribution in the regular grid; The optimized mesh rotation angle is input into the underwater 3D terrain model of the lake to be constructed for feature point distribution verification. It is determined whether the deviation of the feature point alignment interference value obtained after feature point distribution verification is greater than zero. If so, a warning is issued for the feature point alignment process; otherwise, the next dynamic modeling and analysis command for the underwater terrain of the lake is sent.

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