CN-121982233-A - Point cloud topography feature intelligent extraction method based on regional geoid constraint

CN121982233ACN 121982233 ACN121982233 ACN 121982233ACN-121982233-A

Abstract

The invention relates to the technical field of point cloud extraction, in particular to an intelligent point cloud topographic feature extraction method based on regional geoid constraint. The method comprises the steps of obtaining point cloud of an airborne laser radar and global navigation satellite observation data, constructing an area geomorphic level refinement model, realizing unification of point cloud elevation references, calculating a terrain inclination angle and a linear expression error of a triangular surface based on a constrained point cloud height Cheng Goujian ground point cloud triangular network model, adaptively determining feature extraction parameters, traversing point cloud data in a triangular surface projection range, extracting obvious terrain feature points meeting relative altitude conditions, optimizing a triangular network structure through iterative subdivision until termination conditions are met, and outputting a terrain feature extraction result. According to the invention, through the synergistic effect of the regional geoid constraint and the self-adaptive feature discrimination mechanism, the unified elevation reference of the point cloud of the complex terrain, the effective suppression of the pseudo feature and the synchronous improvement of the extraction precision and stability of the terrain feature are realized.

Inventors

ZOU ZHENHUA
SUN SIRUI
LIU FUPING
ZHANG LINGYUAN
ZHANG XIAOMENG
HUO JINFENG
SUN YUANYUAN
GUO ZHIJIN
YANG YANG
SUN YATING
WU HAO
LEI KANG

Assignees

长江水利委员会水文局长江中游水文水资源勘测局(长江水利委员会水文局长江中游水环境监测中心)

Dates

Publication Date: 20260505
Application Date: 20260112

Claims (10)

1. The intelligent point cloud topographic feature extraction method based on the regional geoid constraint is characterized by comprising the following steps of: step S1, acquiring airborne laser radar point cloud data of a region to be detected and corresponding global navigation satellite observation data, and constructing a regional geodetic level refinement model based on the global navigation satellite observation data; S2, carrying out elevation reference conversion on ellipsoid height in the airborne laser radar point cloud data according to the regional geodetic level refinement model to obtain point cloud elevation data constrained to the regional geodetic level; s3, constructing a ground point cloud triangular net model based on the point cloud elevation data, and calculating the terrain inclination angle and linear expression error of each triangular surface by taking the triangular surface as a basic unit of terrain expression; S4, adjusting a gradient threshold value and a scale factor based on the terrain inclination angle and the linear expression error to confirm a feature extraction parameter system; Step S5, traversing the airborne laser radar point cloud data in the projection range of each triangular surface according to the characteristic extraction parameter system, extracting the characteristic points of the remarkable topography meeting the relative height threshold, and adding the characteristic points of the remarkable topography into a triangular network model of the ground point cloud for iterative subdivision; And S6, repeatedly executing the step S5 until a preset iteration termination condition is met, and outputting a point cloud topographic feature extraction result.
2. The intelligent extraction method of point cloud topographic features based on regional geodesic level constraints according to claim 1, wherein the calculating of the topographic dip angle and the linear expression error of each triangular surface in step S3 is specifically: For each triangular surface, calculating a corresponding original terrain inclination angle according to the space included angle between the normal vector of the triangular surface and the horizontal plane; Confirming the distribution condition of the terrain types of the region to be detected according to the original terrain inclination angle, and counting the linear expression errors corresponding to the distribution of different terrain types; Probability weighting is carried out on the linear expression errors corresponding to different terrain types, and the linear expression errors corresponding to triangular surfaces are obtained; and extracting geometric dimension parameters of the triangular surfaces, and correcting the original terrain inclination angle by combining the linear expression errors to obtain the corrected terrain inclination angle of each triangular surface.
3. The intelligent extraction method of the point cloud topographic features based on the regional geodetic level constraints according to claim 1, wherein the step S4 comprises the steps of: step 41, acquiring a gradient threshold value, calculating the elevation precision of the area where the triangular surface is located according to the terrain inclination angle of each triangular surface and a preset grid digital elevation precision model, and confirming gradient deviation according to the elevation precision; Step S42, adjusting a gradient threshold value through gradient deviation amount to obtain a corrected gradient threshold value; step S43, dividing the triangular surface into flat ground, gentle slope or abrupt slope types according to the corrected gradient threshold value, and setting corresponding scale factors; And S44, creating a characteristic extraction parameter system through the corrected gradient threshold value and the gradient factor.
4. The intelligent extraction method of the point cloud topographic features based on the regional geodetic level constraints according to claim 1, wherein the step S5 comprises the steps of: Step S51, confirming the extraction range of the triangular surface according to the characteristic extraction parameter system; Step S52, traversing the point cloud data of the airborne laser radar of each triangular surface in the horizontal projection range in the extraction range of the triangular surface, and calculating the vertical distance of the point cloud corresponding to the triangular surface; step S53, selecting a point with the largest vertical distance as a candidate terrain feature point in each triangular surface; step S54, judging whether the candidate terrain feature points meet the preset relative height threshold value condition according to the relative heights of the candidate terrain feature points and the linear expression errors of the corresponding terrain types; And S55, when the candidate terrain feature points meet the preset relative height threshold condition, determining the candidate terrain feature points as effective terrain feature points, and adding the effective terrain feature points into a ground point cloud triangulation model to split the triangulation surface by fission.
5. The intelligent extraction method of point cloud topographic features based on the regional geodetic constraints as set forth in claim 4, wherein the step S51 includes: Extracting a terrain inclination angle, a gradient threshold value and a scale factor corresponding to each triangular surface through a ground point cloud triangular network model; determining the type of the terrain of the current triangular surface according to the corresponding relation between the inclination angle of the terrain and the gradient threshold value, and selecting a scale factor matched with the type of the terrain as an extraction control parameter of the triangular surface; taking the horizontal projection area of the triangular surface as a basic extraction area, and expanding or contracting the basic extraction area by utilizing extraction control parameters to obtain a candidate extraction range; and (3) verifying the accuracy and the effectiveness of the candidate extraction range, and removing the region without the feature extraction meaning, thereby obtaining the extraction range of the triangular surface.
6. The intelligent extraction method of point cloud topographic features based on regional geodetic constraints according to claim 4, wherein the relative height threshold condition preset in step S54 is specifically: the vertical distance between the candidate terrain characteristic point and the triangular surface where the candidate terrain characteristic point is located is larger than the allowable elevation error of the digital elevation model under the inclination angle of the triangular surface corresponding to the terrain, and the allowable elevation error of the digital elevation model is used for representing the reasonable error range of the point cloud elevation data under the current terrain condition and is corrected by a safety coefficient, wherein the safety coefficient is used for inhibiting pseudo-characteristic point interference caused by measurement noise or data errors.
7. The intelligent extraction method of point cloud topographic features based on the regional geodetic constraints as set forth in claim 4, wherein the step S55 includes: When the candidate terrain feature points meet the preset relative height threshold condition, judging the candidate terrain feature points as effective terrain feature points; for the effective topography feature points, the space coordinate information is recorded, and the association relation between the effective topography feature points and the corresponding triangular surfaces is established; And adding the effective terrain feature points serving as new nodes into a ground point cloud triangular net model, and executing fission subdivision operation on the triangular surface to which the effective terrain feature points belong by taking the effective terrain feature points as segmentation nodes so as to divide the triangular surface into a plurality of new sub-triangular surfaces.
8. The intelligent extraction method of point cloud topography features based on regional geodetic constraints of claim 4, wherein step S55 further comprises: When the candidate terrain feature points do not meet the preset relative height threshold condition, judging and marking the candidate terrain feature points as invalid feature points, and terminating the invalid feature points to perform fission subdivision processing of triangular surfaces; And maintaining the topological structure of the triangular surface to which the invalid feature points belong and executing newly added nodes or fission operation on the intermediate ground point cloud triangular network model.
9. The intelligent extraction method of the point cloud topographic features based on the regional geodetic level surface constraint of claim 8, wherein the acquisition method of the topological structure of the triangular surface to which the invalid feature points belong comprises the following steps: Determining a target triangular surface where the invalid feature points are located according to the space positions of the invalid feature points in the ground point cloud triangular network model; extracting node index information of a target triangular surface in a ground point cloud triangular network model, and obtaining vertex coordinates forming the target triangular surface; Extracting adjacent triangular face identification information adjacent to the target triangular face based on the vertex coordinates so as to establish an adjacent relation between the target triangular face and the peripheral triangular face; And obtaining the topological structure of the triangular surface to which the invalid characteristic points belong according to the vertex information and the adjacent relation.
10. The intelligent extraction method of point cloud terrain features based on regional geoid constraints according to claim 9, wherein determining the target triangular surface where the invalid feature point is located according to the spatial position of the invalid feature point in the ground point cloud triangular network model comprises: according to the three-dimensional space coordinates of the invalid feature points, confirming the corresponding positions of the invalid feature points in the triangular mesh plane projection; Searching a triangular face set intersecting or covered with the projection position in the ground point cloud triangular net model based on the plane projection position to form a candidate triangular face set; in the candidate triangular surface set, determining a triangular surface comprising the projection position of the invalid characteristic point as a target triangular surface according to the geometric inclusion relation between the invalid characteristic point and each candidate triangular surface; when the projection positions of the invalid feature points are located in the common boundary or vertex areas of a plurality of adjacent triangular faces, the corresponding target triangular faces are determined according to the principle that the vertical distance between the invalid feature points and each adjacent triangular face is minimum.

Description

Point cloud topography feature intelligent extraction method based on regional geoid constraint Technical Field The invention relates to the technical field of point cloud extraction, in particular to an intelligent point cloud topographic feature extraction method based on regional geoid constraint. Background In complex water networks and banded watershed areas, terrains are broken and fluctuated frequently and are influenced by hydrologic and geological conditions, traditional section and terrains are measured and are multi-depended on benchmarks or existing control points are used as elevation references, settlement or instability is easy to occur, the elevation references are difficult to keep consistent for a long time, and the connectivity and reliability between different measurement period data are insufficient. Meanwhile, the areas have dense vegetation and wide water distribution, the measurement modes such as global navigation satellites are easily influenced by shielding, the accumulation of measurement errors is obvious, and the high-precision terrain expression requirement is difficult to meet. On the other hand, although the airborne laser radar can rapidly acquire high-density point cloud data, the conventional point cloud processing technology is mostly and directly based on ellipsoidal height or simple correction results to perform terrain analysis, and lacks uniform and high-precision area elevation reference constraint, and the conventional point cloud terrain feature extraction method generally adopts fixed threshold values or single-scale rules, and cannot fully consider error differences under different terrain conditions, so that false features or feature omission are easily generated in complex terrains, and the extraction precision and the automation efficiency are difficult to be compatible. Disclosure of Invention Based on the above, it is necessary to provide an intelligent extraction method for point cloud topographic features based on regional geoid constraints, so as to solve at least one of the above technical problems. In order to achieve the above purpose, the intelligent extraction method of the point cloud topographic features based on the regional geoid constraint comprises the following steps: step S1, acquiring airborne laser radar point cloud data of a region to be detected and corresponding global navigation satellite observation data, and constructing a regional geodetic level refinement model based on the global navigation satellite observation data; S2, carrying out elevation reference conversion on ellipsoid height in the airborne laser radar point cloud data according to the regional geodetic level refinement model to obtain point cloud elevation data constrained to the regional geodetic level; s3, constructing a ground point cloud triangular net model based on the point cloud elevation data, and calculating the terrain inclination angle and linear expression error of each triangular surface by taking the triangular surface as a basic unit of terrain expression; S4, adjusting a gradient threshold value and a scale factor based on the terrain inclination angle and the linear expression error to confirm a feature extraction parameter system; Step S5, traversing the airborne laser radar point cloud data in the projection range of each triangular surface according to the characteristic extraction parameter system, extracting the characteristic points of the remarkable topography meeting the relative height threshold, and adding the characteristic points of the remarkable topography into a triangular network model of the ground point cloud for iterative subdivision; And S6, repeatedly executing the step S5 until a preset iteration termination condition is met, and outputting a point cloud topographic feature extraction result. The method has the advantages that unified constraint and reference conversion are carried out on the elevation of the point cloud of the airborne laser radar by introducing the regional quasi-geoid refinement model, the problems of inconsistent elevation and accumulated error caused by direct adoption of ellipsoidal height or simplification correction in the traditional method are effectively solved, stability, comparability and reliability of elevation data of the point cloud of the complex region are remarkably improved, meanwhile, the land point cloud triangulation network model is taken as a basic terrain expression frame, the slope threshold value and the scale factor are adaptively adjusted by comprehensively considering the triangle surface terrain inclination angle, the linear expression error and the elevation precision, the problem that pseudo-feature or missing feature is easily generated under the complex terrain condition by a fixed threshold value and a single-scale rule is solved, in the feature extraction process, the point with the largest vertical distance is selected in the triangle surface projection range and is used as