CN116721228A - Building elevation extraction method and system based on low-density point cloud - Google Patents
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Abstract
The application belongs to the technical field of geospatial information, and particularly relates to a building elevation extraction method and system based on low-density point cloud, wherein the method comprises the following steps: acquiring building point cloud data; calculating a top grid and a gradient grid of the building based on the acquired building point cloud data; and respectively extracting the vector surface top height and the vector surface bottom height of the building according to the obtained top height grid and gradient grid, assigning a value to the vector surface, and calculating the elevation of the building. The application automatically supplements elevation information for the vector surface of the existing building based on the low-density point cloud data, improves the accuracy of elevation extraction, realizes the large-scale production of LOD1.3 level three-dimensional white mold, and effectively avoids the problem of abnormal elevation extraction caused by adjacent building staggered floor and building top accessories.
Description
Technical Field
The application belongs to the technical field of geospatial information, and particularly relates to a building elevation extraction method and system based on low-density point cloud.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The LOD1.3 level three-dimensional white model is suitable for expressing buildings in a large-scale terrain level three-dimensional scene, and the LOD1.3 level three-dimensional white model can be produced by using building vector surface data with elevation information. At present, large-scale, massive and complete covered building vector surface data exist in basic mapping data results, but building elevation information is hardly available, and how to extract elevation information based on the existing building vector surface at low cost is a key for completing the construction of LOD 1.3-level three-dimensional white model in large-scale production.
The inventor knows that when the low-density point cloud is based, because of reasons such as point cloud precision and eave influence, the problem of adjacent building Gao Chengcuo exists by adopting a general method, and the generated white mold is inconsistent with the actual height of the building due to influence of building roof accessories (such as an antenna and a solar water heater), so that the manual modification is carried out, the workload is extremely high, and the mass production cost is increased.
Disclosure of Invention
In order to solve the problems, the application provides a building elevation extraction method and system based on low-density point cloud, which are used for calculating automatic supplementary elevation information of an existing building vector surface based on the low-density point cloud, so that the problem of abnormal elevation extraction caused by adjacent building staggered floors and building top accessories is effectively avoided.
According to some embodiments, the first scheme of the application provides a building elevation extraction method based on low-density point cloud, which adopts the following technical scheme:
a building elevation extraction method based on a low-density point cloud, comprising:
acquiring building point cloud data;
calculating a top grid and a gradient grid of the building based on the acquired building point cloud data;
and respectively extracting the vector surface top height and the vector surface bottom height of the building according to the obtained top height grid and gradient grid, assigning a value to the vector surface, and calculating the elevation of the building.
As a further technical limitation, the process of extracting the vector surface top height of the building is as follows: traversing each vector surface to obtain an outsourcing rectangle of the vector surface; respectively obtaining diagonal coordinates of the outsourcing rectangle, calculating row and column numbers of grids of the diagonal corners according to the coordinates, traversing through the row and column number data to obtain top grid information and gradient grid information of all grid points in the outsourcing rectangle, and storing the top grid values into a list; when the obtained gradient grid value is larger than a preset angle, filtering out a jacking grid corresponding to the gradient grid; according to the grid line number and the grid line number, reversely calculating the grid point coordinates, judging whether the grid point coordinates are in a vector plane, and if not, filtering out the corresponding top grid value; calculating the average value of the obtained top-up grid value list, grouping the top-up grid value list based on a preset interval by taking the average value as a central value, and counting the number of each group; deleting the grouping and grouping values with the median number of the grouping being smaller than the total number threshold, and taking the maximum value in all the values as the vector surface top of the building.
Further, row and column numbers are calculated according to coordinate points of the outsourcing rectangle of the building vector surface, continuous grid block data are rapidly obtained, actual coordinates of grid center points are converted according to row and column numbers of grids, and whether the building vector surface is in the building vector surface is calculated.
As a further technical definition, the process of extracting the vector ground height of the building is as follows: traversing each vector surface to obtain an outsourcing rectangle of the vector surface; obtaining diagonal coordinates of the outsourcing rectangle, calculating row and column numbers of grids of the diagonal corners according to the coordinates, traversing through the row and column number data to obtain jacking grid information of all grid points in the outsourcing rectangle, and storing the obtained elevation values into a list; according to the grid line number, reversely calculating the grid point coordinates, judging whether the grid point coordinates are in a vector plane, and if not, filtering out the corresponding elevation values; and (3) obtaining the average value of the obtained top grid value list, and taking the average value as the vector surface height of the building.
As a further technical limitation, after acquiring building point cloud data, classifying the acquired point cloud data specifically includes: preprocessing the obtained building point cloud data, and then adopting a consistency filtering method to carry out filtering treatment, so that abnormal points and redundant information are reduced, and the quality of the point cloud is improved; carrying out segmentation processing by adopting a clustering-based segmentation algorithm; according to the normal direction, curvature and height extraction algorithm, extracting the characteristics of each surface of the building according to the characteristics of the building, and constructing a building characteristic vector; and classifying the extracted building point cloud data into building layers according to the feature vectors, and realizing automatic classification of the building point cloud data.
As a further technical limitation, the rasterization processing is performed by adopting an interpolation mode according to the acquired building point cloud data, and the building top height raster data is calculated.
As a further technical definition, a gradient algorithm is employed to calculate building gradient raster data from the acquired building point cloud data.
According to some embodiments, the second aspect of the present application provides a building elevation extraction system based on a low-density point cloud, which adopts the following technical scheme:
a low density point cloud based building elevation extraction system comprising:
an acquisition module configured to acquire building point cloud data;
a computing module configured to compute a top grid and a grade grid of a building based on the acquired building point cloud data;
and the extraction module is configured to extract the vector surface top height and the vector surface bottom height of the building according to the obtained top height grid and gradient grid, assign a value to the vector surface and calculate the building elevation.
According to some embodiments, a third aspect of the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the steps in a low-density point cloud based building elevation extraction method according to the first aspect of the present application.
According to some embodiments, a fourth aspect of the present application provides an electronic device, which adopts the following technical solutions:
an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in a low density point cloud based building elevation extraction method according to the first aspect of the application when the program is executed.
Compared with the prior art, the application has the beneficial effects that:
the application automatically supplements elevation information for the vector surface of the existing building based on low-density point cloud data, solves the problems that the elevation value of the adjacent staggered floor building is easy to be misplaced and the extraction error is caused by the top accessory of the building under the general method based on the highest point of the point falling surface, saves the workload of post manual processing and investigation, improves the accuracy of elevation extraction, realizes the large-scale production of LOD1.3 level three-dimensional white mould, and effectively avoids the problem of abnormal elevation extraction caused by the adjacent staggered floor and the top accessory of the building.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification, illustrate and explain the embodiments and together with the description serve to explain the embodiments.
FIG. 1 is a flow chart of a building elevation extraction method based on a low-density point cloud according to a first embodiment of the application;
fig. 2 is a block diagram of a building elevation extraction system based on a low-density point cloud according to a second embodiment of the present application.
Detailed Description
The application will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the application and features of the embodiments may be combined with each other without conflict.
Example 1
The embodiment of the application introduces a building elevation extraction method based on low-density point cloud.
The building elevation extraction method based on the low-density point cloud shown in fig. 1 comprises the following steps:
(1) Acquiring low-density point cloud data, and automatically classifying the acquired point cloud data;
(2) Performing rasterization processing on the classified point cloud data;
(3) Calculating a grid gradient;
(4) Extracting the vector surface elevation;
(5) Extracting vector surface bottom height;
(6) And calculating the building height according to the obtained building top height and the building bottom height, and assigning a value to the vector surface.
The vector surface in this embodiment is the geometric surface of the building with edges in the existing building geographic information data.
As one or more embodiments, the point clouds are automatically classified based on the position, attribute, surrounding point information and other characteristics of the points in the point clouds, and the building point clouds are extracted.
In this embodiment, the classification process of the point cloud classification algorithm is as follows:
importing point cloud data, wherein the data is in a las format;
setting related parameters of a clustering algorithm, such as the number of clusters, a distance threshold value and the like, based on a segmentation algorithm of K-means clustering; the specific process is as follows: firstly, initializing, selecting the number K of cluster clusters, and initializing K cluster centers. The cluster center may be randomly selected from the data or initialized by other heuristics. Then, data points are allocated, and each data point is allocated to the nearest cluster center to form an initial cluster. This step calculates the distance between the data point and the cluster center based on euclidean distance or other distance metric. And updating the cluster centers, and calculating the average value of the data points in each cluster to be used as a new cluster center. The two steps of assigning data points and updating the cluster center are repeated until a termination condition is met, i.e. a maximum number of iterations is reached or the cluster center no longer changes or a predetermined error threshold is reached. And finally, outputting the result to obtain K clustering clusters, wherein each cluster comprises a group of data points. Each cluster can be subjected to subsequent processing according to the requirement;
and extracting feature vectors suitable for clustering according to the building point cloud features. These features may include coordinates of points, normal direction, curvature, etc.; the specific process is as follows: the normal direction is a normal vector for each point calculated using a normal line estimation method, including least squares, principal Component Analysis (PCA), and neighborhood-based methods. The normal vector may describe the orientation information of the point cloud surface, for a building the normal to the ground is typically associated with a wall or roof. Curvature extraction is a method for expressing curvature change of a point cloud surface by using curvature, and the calculation of curvature can be based on normal vector or other geometric attributes, such as distance and angle of a neighborhood point, for extracting edges or protruding parts of a building. The height extraction method is that the vertical information of the building can be extracted by calculating the height of each point or the distance from the ground, and the height characteristics help to identify different layers or floors of the building;
building feature vectors, classifying building points according to the building point cloud data obtained by clustering and post-processing, and dividing the building point cloud into building layers; i.e. the features extracted from each cluster are combined into a feature vector for the building. The normal direction, curvature and height values of each face are taken as characteristics, and the characteristics are combined into a vector representing the characteristics of the building according to a certain sequence or coding mode.
As one or more embodiments, performing rasterization processing based on the extracted building point cloud in an interpolation mode to generate top raster data, wherein the size of a raster unit is 0.6 times of the point cloud distance; firstly, the point cloud data is gridded according to a certain resolution to form raster data. Parameters such as the resolution of the grid (taking 0.6 times of the point cloud distance, the gridding range) and the like are required to be set; then, for the rasterized data, interpolation processing is required to be performed by a triangular mesh interpolation method so as to obtain smoother top raster data.
As one or more embodiments, calculating the gradient of each pixel value based on the generated jacking grid as input, wherein the gradient calculating method is to calculate the average value of the angle values from the central pixel to the surrounding pixels in the pixel 3*3, the value range is 0-90 degrees, and the calculated result is output as a gradient grid in a grid data format; specific:
preparing processing data, wherein the input data is a top grid;
generating unassigned raster data space with the same size as input data as processed data;
traversing each grid pixel of the processed data, wherein the pixel is a pixel to be processed;
according to the row and column numbers of the pixels to be processed, acquiring the peripheral pixels and the central pixels in 3*3 from input data;
calculating the included angle value of each peripheral pixel and the central pixel independently;
calculating an average value according to the included angle values of all the surrounding pixels and the central pixel, and assigning the average value to the pixel to be processed;
after the traversing is completed, all assignment of the processed data is completed, and the processed data is output to obtain the gradient grid.
As one or more embodiments, the jacking data is assigned to each vector surface based on the jacking grid and the gradient grid, specifically:
1) Traversing each vector surface;
2) Obtaining an outsourcing rectangle of the vector surface;
3) Acquiring coordinates of an upper left corner point and a lower right corner point of the outsourcing rectangle, calculating ROW and column numbers of grids of the two points according to the coordinates, traversing through ROW and column number DATA to acquire top grid information and gradient grid information of all grid points in the outsourcing rectangle, and storing the top grid values into a list (the grid DATA are continuously stored structural DATA and can be understood as a two-dimensional array (ROW, COL, DATA); where ROW represents the ROW number, COL represents the column number, and DATA represents the raster DATA value; in the calculation process, firstly calculating row and column numbers according to coordinate points of an outsourcing rectangle of a building vector surface, rapidly acquiring continuous grid block data, reading the continuous grid block data into a memory, and then converting actual coordinates of a grid center point according to row and column numbers of the grids to calculate whether the actual coordinates are in the building vector surface or not, so that the calculation speed is higher);
4) In the step 3), when the obtained gradient grid value is larger than a set threshold value (in the embodiment, 45-70 degrees are generally taken), the corresponding top-up grid value is filtered;
5) In the step 3), back calculating the grid point coordinates according to the grid row and column numbers, judging whether the grid point coordinates are in a vector plane, and if not, filtering out the corresponding top grid value;
6) Solving the average value of the top-up grid value list obtained in the step 3), grouping the top-up grid value list by taking the average value as a central value and taking 3 meters as intervals, and counting the number of each group;
7) Deleting the grouping values of which the grouping median number is smaller than the total number threshold value in the step 6);
in this embodiment, the grouping is to analyze the characteristics of the top of the building by counting the points of the grouping, and generally, the top points should be concentrated in one grouping, and the grouping and grouping values (generally, noise points after gradient filtering are completed) are smaller than the total number threshold;
8) Taking the maximum value of all the values processed in the step 7) as the jacking assignment of the building to the vector surface.
As one or more embodiments, bottom height data is assigned to each vector surface based on existing top height raster data; the method comprises the following steps:
1) Traversing each vector surface;
2) Obtaining an outsourcing rectangle of the vector surface;
3) Acquiring coordinates of an upper left corner point and a lower right corner point of the outsourcing rectangle, calculating row and column numbers of grids of the two points according to the coordinates, traversing through the row and column number data to acquire values of all DEM grid data in the outsourcing rectangle, and storing the acquired elevation values into a list;
4) In the step 3), back calculating the grid point coordinates according to the grid row number, judging whether the grid point coordinates are in a vector plane, and filtering out corresponding elevation values if the grid point coordinates are not in the vector plane;
5) And 3) solving the average value of the top grid value list obtained in the step 3), and assigning the average value as the bottom height of the building to be the vector surface of the building.
According to the method, the elevation information is automatically supplemented for the vector surface of the existing building based on the low-density point cloud data, the problems that the elevation value of the adjacent staggered floor building is easy to be misplaced and the extraction error is caused by the building top accessory under the general method based on the point falling surface highest point are solved, the later-stage manual processing and checking workload is saved, the accuracy of elevation extraction is improved, the large-scale production of the LOD1.3 level three-dimensional white model is realized, and the problem of abnormal elevation extraction caused by the adjacent staggered floor and the building top accessory is effectively avoided.
Example two
The second embodiment of the application introduces a building elevation extraction system based on low-density point clouds.
A low density point cloud based building elevation extraction system as shown in fig. 2, comprising:
an acquisition module configured to acquire building point cloud data;
a computing module configured to compute a top grid and a grade grid of a building based on the acquired building point cloud data;
and the extraction module is configured to extract the vector surface top height and the vector surface bottom height of the building according to the obtained top height grid and gradient grid, assign a value to the vector surface and calculate the building elevation.
The detailed steps are the same as those of the building elevation extraction method based on the low-density point cloud provided in the first embodiment, and will not be described herein.
Example III
The third embodiment of the application provides a computer readable storage medium.
A computer-readable storage medium having stored thereon a program which, when executed by a processor, performs the steps in a low-density point cloud based building elevation extraction method according to an embodiment of the present application.
The detailed steps are the same as those of the building elevation extraction method based on the low-density point cloud provided in the first embodiment, and will not be described herein.
Example IV
The fourth embodiment of the application provides electronic equipment.
An electronic device includes a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor implements the steps in the low-density point cloud based building elevation extraction method according to the first embodiment of the present application when executing the program.
The detailed steps are the same as those of the building elevation extraction method based on the low-density point cloud provided in the first embodiment, and will not be described herein.
The above description is only a preferred embodiment of the present embodiment, and is not intended to limit the present embodiment, and various modifications and variations can be made to the present embodiment by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present embodiment should be included in the protection scope of the present embodiment.
Claims (10)
1. The building elevation extraction method based on the low-density point cloud is characterized by comprising the following steps of:
acquiring building point cloud data;
calculating a top grid and a gradient grid of the building based on the acquired building point cloud data;
and respectively extracting the vector surface top height and the vector surface bottom height of the building according to the obtained top height grid and gradient grid, assigning a value to the vector surface, and calculating the elevation of the building.
2. The building elevation extraction method based on the low-density point cloud as claimed in claim 1, wherein each vector surface is traversed to obtain an outsourcing rectangle of the vector surface; respectively acquiring diagonal coordinates of the outsourcing rectangle and row numbers of grids in which diagonal angles fall, and calculating jacking grid information and gradient grid information of all grid points in the outsourcing rectangle; and calculating the average value of the obtained jacking grid values, and taking the maximum value of the average value as the vector surface jacking of the building.
3. The building elevation extraction method based on the low-density point cloud as claimed in claim 2, wherein a row number is calculated according to coordinate points of an outsourcing rectangle of a building vector surface, continuous grid block data are rapidly obtained, and then actual coordinates of a grid center point are converted according to the row number of the grid, so that whether the building vector surface is in the building vector surface is calculated.
4. The building elevation extraction method based on the low-density point cloud as claimed in claim 1, wherein the process of extracting the vector surface bottom height of the building is as follows: traversing each vector surface to obtain an outsourcing rectangle of the vector surface; obtaining diagonal coordinates of the outsourcing rectangle and row and column numbers of the grid of the diagonal falling into the grid; and reversely calculating grid point coordinates according to grid row numbers, solving the average value of the obtained top-up grid values, and taking the average value as the vector surface height of the building.
5. The building elevation extraction method based on low-density point cloud as claimed in claim 1, wherein after the building point cloud data is acquired, classifying the acquired point cloud data specifically comprises: preprocessing the obtained building point cloud data, extracting the characteristics of each surface of a building, and constructing a building characteristic vector; and automatically classifying the building point cloud data according to the feature vector.
6. The building elevation extraction method based on the low-density point cloud as claimed in claim 1, wherein the rasterization processing is performed by adopting an interpolation mode according to the obtained building point cloud data, and building top height raster data is calculated.
7. A building elevation extraction method based on a low-density point cloud as claimed in claim 1, wherein the building gradient raster data is calculated by a gradient algorithm based on the obtained building point cloud data.
8. A building elevation extraction system based on a low density point cloud, comprising:
an acquisition module configured to acquire building point cloud data;
a computing module configured to compute a top grid and a grade grid of a building based on the acquired building point cloud data;
and the extraction module is configured to extract the vector surface top height and the vector surface bottom height of the building according to the obtained top height grid and gradient grid, assign a value to the vector surface and calculate the building elevation.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the low-density point cloud based building elevation extraction method according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the low density point cloud based building elevation extraction method of any one of claims 1 to 7 when the program is executed.
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