CN112381940A - Processing method and device for generating digital elevation model from point cloud data and terminal equipment - Google Patents
Processing method and device for generating digital elevation model from point cloud data and terminal equipment Download PDFInfo
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Abstract
The invention discloses a processing method, a device and terminal equipment for generating a digital elevation model from point cloud data, wherein the method comprises the following steps: performing quality detection on the laser point cloud data, and screening out initial laser point cloud data meeting the standard; carrying out point cloud data loading operation on the initial laser point cloud data; denoising the loaded initial laser point cloud data to obtain denoised laser point cloud data; performing ground point classification on the denoised laser point cloud data to obtain classified target point cloud data, performing quality detection on the target point cloud data again, and judging whether the quality is qualified; and if so, generating a digital elevation model based on the target point cloud data. The invention improves the precision of point cloud data processing, guarantees the validity of the point cloud data and guarantees the subsequent execution effect of the digital elevation model.
Description
Technical Field
The invention relates to the technical field of point cloud data processing, in particular to a processing method and device for generating a digital elevation model from point cloud data and terminal equipment.
Background
Laser radar (LiDAR) is a ground observation technology which directly acquires three-dimensional coordinates of surface points of an object through observation data such as positions, distances, angles and the like to realize surface information extraction and three-dimensional scene reconstruction.
The LiDAR is used for target detection, belongs to an active remote sensing mode, has small dependence on weather, and is not easily influenced by shadows and sun angles. Compared with the traditional photogrammetry technology, the laser radar scanning technology avoids information loss caused by projection (from three dimensions to two dimensions), greatly improves the accuracy of elevation acquisition, and has obvious advantages. The large-scale production of topographic products such as Digital Elevation Models (DEMs), Digital Surface Models (DSMs), contour lines and the like can be rapidly completed by utilizing the LiDAR data. For example, chinese patent publication No. CN110570466A, publication No. 2019.12.13: according to the method and the device for generating the three-dimensional live-action point cloud model, the digital elevation model is established through LiDAR data, however, extracted cloud point data are not strictly processed, the obtained digital elevation model is low in quality and precision, errors are easily caused, and the effectiveness of the point cloud data cannot be guaranteed.
Disclosure of Invention
In view of the above, the invention provides a processing method, a processing device and a terminal device for generating a digital elevation model from point cloud data.
The specific technical scheme of the invention is as follows:
a processing method for generating a digital elevation model from point cloud data comprises the following steps:
performing quality detection on the acquired laser point cloud data, and screening out initial laser point cloud data meeting the standard;
carrying out point cloud data loading operation on the initial laser point cloud data;
denoising the loaded initial laser point cloud data to obtain denoised laser point cloud data;
performing ground point classification on the denoised laser point cloud data to obtain classified target point cloud data; performing quality detection on the target point cloud data again, and judging whether the quality is qualified or not; and if so, generating a digital elevation model based on the target point cloud data.
Preferably, the quality detection includes detection of point cloud density information and elevation precision information of the laser point cloud data, and specifically includes the following operation steps:
acquiring point cloud density information of the laser point cloud data, identifying to obtain the point cloud density of the current laser point cloud data, judging whether the point cloud density of the current laser point cloud data is greater than a standard point cloud density, and if so, judging that the quality detection of the point cloud density is qualified;
acquiring elevation precision information of the laser point cloud data, identifying and obtaining an error in elevation of the current laser point cloud data, judging whether the error in elevation of the current laser point cloud data is smaller than a standard error or not, and judging that the detection of the elevation precision quality is qualified if the error in elevation of the current laser point cloud data is smaller than the standard error;
and when the detection of the point cloud density information and the elevation precision information is qualified, the current laser point cloud data is the initial laser point cloud data which meets the standard.
Preferably, if the initial laser point cloud data is point cloud data in LAS/ASCII/PLY format, a loading operation is performed to convert the initial laser point cloud data into a data point cloud format.
Preferably, denoising the loaded initial laser point cloud data to obtain denoised laser point cloud data, and specifically includes the following steps:
identifying noise points of the high-order gross errors of the initial laser point cloud data, and filtering after identifying and determining the noise points; and simultaneously, identifying noise points of the low-order gross errors of the initial laser point cloud data, and filtering after identifying and determining the noise points.
Preferably, the high-order gross error and the low-order gross error are based on a distance average algorithm to identify the noise point, and the method specifically comprises the following operation steps:
randomly determining a basic point in the initial laser point cloud data, and searching adjacent points with the preset number in a preset neighborhood on the basis of the basic point;
calculating the average distance value from the basic point to the adjacent points, and calculating the median and standard deviation of the average distance value;
judging whether the distance average value of the current basic point is greater than the maximum distance or not, and if so, determining the current basic point as a noise point; wherein, the maximum distance is the median + the multiple of standard deviation.
Preferably, when the noise point is identified for both the high coarse difference and the low coarse difference based on the distance-average algorithm, the method further includes adjusting and modifying the standard deviation multiple and the setting parameter information of the preset number of points.
Preferably, the number of preset points of the preset neighborhood is 10, and the multiple of the standard deviation is 5.
Preferably, ground point classification is performed on the denoised laser point cloud data, wherein the ground point classification means that ground points and non-ground points are separated through point cloud filtering.
Correspondingly, the invention also provides a processing device for generating the digital elevation model from the point cloud data, which comprises a data detection module, a loading module, a denoising module and a generating module;
the data detection module is used for carrying out quality detection on the acquired laser point cloud data and screening out initial laser point cloud data meeting the standard;
the loading module is used for carrying out point cloud data loading operation on the initial laser point cloud data;
the de-noising module is used for de-noising the loaded initial laser point cloud data to obtain de-noised laser point cloud data;
the generating module is used for carrying out ground point classification on the denoised laser point cloud data to obtain classified target point cloud data; performing quality detection on the target point cloud data again, and judging whether the quality is qualified or not; and if so, generating a digital elevation model based on the target point cloud data.
Correspondingly, the invention also provides a terminal device, comprising: a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program to implement the above-described processing method for generating a digital elevation model from point cloud data.
The technical scheme of the invention has the following beneficial effects:
the invention provides a processing method, a processing device and terminal equipment for generating a digital elevation model from point cloud data, which strictly control the input laser point cloud data by detecting the real-time quality of the laser point cloud data; meanwhile, loading and denoising the initial laser point cloud data to obtain denoised laser point cloud data; and carrying out ground point classification on the denoised laser point cloud data to obtain classified target point cloud data, carrying out quality detection again, and generating a digital elevation model only by the qualified target point cloud data. The invention improves the precision of point cloud data processing, guarantees the validity of the point cloud data and guarantees the subsequent execution effect of the digital elevation model.
Drawings
FIG. 1 is a schematic flow chart of a processing method for generating a digital elevation model from point cloud data according to the present invention;
FIG. 2 is a schematic structural diagram of a processing apparatus for generating a digital elevation model from point cloud data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Hereinafter, the terms "including", "having", and their derivatives, which may be used in various embodiments of the present invention, are only intended to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing, and should not be construed as first excluding the existence of, or adding to, one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present invention belong. The terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning that is consistent with their contextual meaning in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in various embodiments of the present invention.
Example 1
Referring to fig. 1, the present embodiment provides a processing method for generating a digital elevation model from point cloud data, which includes the following steps:
step S100: performing quality detection on the acquired laser point cloud data, and screening out initial laser point cloud data meeting the standard;
step S200: carrying out point cloud data loading operation on the initial laser point cloud data;
step S300: denoising the loaded initial laser point cloud data to obtain denoised laser point cloud data;
step S400: performing ground point classification on the denoised laser point cloud data to obtain classified target point cloud data; performing quality detection on the target point cloud data again, and judging whether the quality is qualified or not; and if so, generating a digital elevation model based on the target point cloud data.
The processing method for generating the digital elevation model from the point cloud data provided by the embodiment is different from the traditional processing method, and specifically strictly controls the input laser point cloud data by detecting the real-time quality of the laser point cloud data; meanwhile, loading and denoising the initial laser point cloud data to obtain denoised laser point cloud data; and automatically classifying ground points and manually classifying the denoised laser point cloud data to obtain classified target point cloud data, detecting again, and generating a digital elevation model only by the qualified target point cloud data.
In step S100, the quality detection includes detecting point cloud density information and elevation accuracy information of the laser point cloud data, and specifically includes the following operation steps:
acquiring point cloud density information of the laser point cloud data, identifying to obtain the point cloud density of the current laser point cloud data, judging whether the point cloud density of the current laser point cloud data is greater than a standard point cloud density, and if so, judging that the quality detection of the point cloud density is qualified;
acquiring elevation precision information of the laser point cloud data, identifying and obtaining an error in elevation of the current laser point cloud data, judging whether the error in elevation of the current laser point cloud data is smaller than a standard error or not, and judging that the detection of the elevation precision quality is qualified if the error in elevation of the current laser point cloud data is smaller than the standard error;
and when the detection of the point cloud density information and the elevation precision information is qualified, the current laser point cloud data is the initial laser point cloud data which meets the standard.
In order to produce a topographic product such as a high-precision DEM/DSM/contour line, quality inspection of data is required before formal production, and the method mainly includes the following inspection contents:
1. point cloud density inspection: the acquired laser radar point cloud data density needs to be ensured to meet the demand of DEM interpolation. The data may be inspected using a point density inspection tool, typically to inspect exposed hard straight ground areas. The specific requirements are given in the following table:
| framing scale | Digital elevation model achievement grid distance/meter | Point cloud density/(point/meter)2) |
| 1∶500 | 0.5 | ≥16 |
| 1∶1000 | 1.0 | ≥4 |
| 1∶2000 | 2.0 | ≥1 |
| 1∶5000 | 2.5 | ≥1 |
| 1∶10000 | 5.0 | ≥0.25 |
2. Point cloud data elevation precision inspection: and (3) checking by using data of field control points, wherein the specific required values of errors in the elevation are as follows:
in special difficult areas such as vegetation covered areas and areas with low reflectivity (such as water areas, smooth surfaces and other areas which are easy to form mirror reflection), the error in the point cloud data elevation is 2 times of the error in the upper table.
In step 200, if the initial laser point cloud data is in LAS/ASCII/PLY format, a loading operation is performed to convert the initial laser point cloud data into a data point cloud format.
The initial laser point cloud data generally takes point cloud data in the format of LiData/LAS/ASCII/PLY and the like as an initial data format, and after software is imported into the point cloud data in the format of LAS/ASCII/PLY and the like, the corresponding LiData format is automatically generated for subsequent processing; therefore, the final initial laser point cloud data is subjected to high-efficiency browsing processing of mass data in a LiData point cloud format.
In step S300: denoising the loaded initial laser point cloud data to obtain denoised laser point cloud data, and specifically comprising the following execution steps:
identifying noise points of the high-order gross errors of the initial laser point cloud data, and filtering after identifying and determining the noise points; and simultaneously, identifying noise points of the low-order gross errors of the initial laser point cloud data, and filtering after identifying and determining the noise points.
The method comprises the following steps of:
randomly determining a basic point in the initial laser point cloud data, and searching adjacent points with the preset number in a preset neighborhood on the basis of the basic point;
calculating the average distance value from the basic point to the adjacent points, and calculating the median and standard deviation of the average distance value;
judging whether the distance average value of the current basic point is greater than the maximum distance or not, and if so, determining the current basic point as a noise point; wherein, the maximum distance is the median + the multiple of standard deviation.
It should be noted that, the above algorithm searches each point (i.e., the basic point) for neighboring points that specify the number of neighboring points, calculates the distance averages from the point (i.e., the basic point) to the neighboring points, calculates the median and standard deviation of these distance averages, and if the distance average of this point is greater than the maximum distance (the maximum distance is the median + the multiple of the standard deviation) it is considered as a noise point and will be removed.
The method comprises the steps of identifying noise points on the basis of a distance average algorithm for both high-order gross errors and low-order gross errors, and adjusting and modifying set parameter information of standard deviation multiples and preset point numbers.
In this embodiment, the number of preset points in the preset neighborhood is 10, and the multiple of the standard deviation is 5.
The processing method for generating the digital elevation model from the point cloud data adopted in this embodiment needs to set parameters of the system, and the main setting contents are as follows: 1. inputting data: the input file can be a single point cloud data file or a plurality of data files; the file format is as follows: and LiData. 2. Number of preset points (default to "10"): and the number of points required in the neighborhood is used for calculating the average distance of each point. If not enough points are found, the algorithm will not be executed. 3. Multiple of standard deviation (default to "5"): a factor multiplied by the standard deviation, a point value within the search range. 4. An output path: and outputting a file path, and generating a new file without noise points after the algorithm is executed. When a plurality of files are input, the path is set as a folder.
In step S400, a ground point classification is performed on the denoised laser point cloud data, where the ground point classification refers to separating ground points and non-ground points through point cloud filtering, and belongs to a technical means known by those skilled in the art, and will not be described here.
Example 2
As shown in fig. 2, based on the same inventive concept, the invention further provides a processing apparatus for generating a digital elevation model from point cloud data, which includes a data detection module, a loading module, a denoising module and a generation module;
the data detection module is used for carrying out quality detection on the acquired laser point cloud data and screening out initial laser point cloud data meeting the standard;
the loading module is used for carrying out point cloud data loading operation on the initial laser point cloud data;
the de-noising module is used for de-noising the loaded initial laser point cloud data to obtain de-noised laser point cloud data;
the generating module is used for carrying out ground point classification on the denoised laser point cloud data to obtain classified target point cloud data; performing quality detection on the target point cloud data again, and judging whether the quality is qualified or not; and if so, generating a digital elevation model based on the target point cloud data.
It is to be understood that the processing device for generating the digital elevation model from the point cloud data described above corresponds to the processing method of embodiment 1. Any of the options in embodiment 1 are also applicable to this embodiment, and will not be described in detail here.
Correspondingly, the present embodiment further provides a terminal device, including: a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program to implement the above-described processing method for generating a digital elevation model from point cloud data.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A processing method for generating a digital elevation model from point cloud data is characterized by comprising the following steps:
performing quality detection on the acquired laser point cloud data, and screening out initial laser point cloud data meeting the standard;
carrying out point cloud data loading operation on the initial laser point cloud data;
denoising the loaded initial laser point cloud data to obtain denoised laser point cloud data;
performing ground point classification on the denoised laser point cloud data to obtain classified target point cloud data; performing quality detection on the target point cloud data again, and judging whether the quality is qualified or not; and if so, generating a digital elevation model based on the target point cloud data.
2. The processing method according to claim 1, wherein the quality detection comprises detection of point cloud density information and elevation accuracy information of the laser point cloud data, and specifically comprises the following operation steps:
acquiring point cloud density information of the laser point cloud data, identifying to obtain the point cloud density of the current laser point cloud data, judging whether the point cloud density of the current laser point cloud data is greater than a standard point cloud density, and if so, judging that the quality detection of the point cloud density is qualified;
acquiring elevation precision information of the laser point cloud data, identifying and obtaining an error in elevation of the current laser point cloud data, judging whether the error in elevation of the current laser point cloud data is smaller than a standard error or not, and judging that the detection of the elevation precision quality is qualified if the error in elevation of the current laser point cloud data is smaller than the standard error;
and when the detection of the point cloud density information and the elevation precision information is qualified, the current laser point cloud data is the initial laser point cloud data which meets the standard.
3. The processing method as claimed in claim 1, wherein if the initial laser point cloud data is point cloud data in LAS/ASCII/PLY format, a loading operation is performed to convert the initial laser point cloud data into the data point cloud format.
4. The processing method according to claim 1, wherein denoising the loaded initial laser point cloud data to obtain denoised laser point cloud data, specifically comprising the following steps:
identifying noise points of the high-order gross errors of the initial laser point cloud data, and filtering after identifying and determining the noise points; and simultaneously, identifying noise points of the low-order gross errors of the initial laser point cloud data, and filtering after identifying and determining the noise points.
5. The processing method according to claim 4, wherein the high-order gross error and the low-order gross error are both based on a distance-average algorithm to identify the noise point, and specifically comprising the following operation steps:
randomly determining a basic point in the initial laser point cloud data, and searching adjacent points with the preset number in a preset neighborhood on the basis of the basic point;
calculating the average distance value from the basic point to the adjacent points, and calculating the median and standard deviation of the average distance value;
judging whether the distance average value of the current basic point is greater than the maximum distance or not, and if so, determining the current basic point as a noise point; wherein, the maximum distance is the median + the multiple of standard deviation.
6. The processing method according to claim 5, wherein the noise point identification based on the distance average algorithm is performed on both the high-order gross error and the low-order gross error, and the adjustment and modification operations are further performed on the setting parameter information of the multiple of the standard deviation and the preset number of points.
7. The processing method of claim 5, wherein the number of the preset points in the preset neighborhood is 10, and the multiple of the standard deviation is 5.
8. The processing method of claim 1, wherein the denoised laser point cloud data is subjected to ground point classification, wherein the ground point classification is to separate ground points and non-ground points by point cloud filtering.
9. A processing device for generating a digital elevation model from point cloud data is characterized by comprising a data detection module, a loading module, a denoising module and a generation module;
the data detection module is used for carrying out quality detection on the acquired laser point cloud data and screening out initial laser point cloud data meeting the standard;
the loading module is used for carrying out point cloud data loading operation on the initial laser point cloud data;
the de-noising module is used for de-noising the loaded initial laser point cloud data to obtain de-noised laser point cloud data;
the generating module is used for carrying out ground point classification on the denoised laser point cloud data to obtain classified target point cloud data; performing quality detection on the target point cloud data again, and judging whether the quality is qualified or not; and if so, generating a digital elevation model based on the target point cloud data.
10. A terminal device, comprising: a processor and a memory, the memory storing a computer program for executing the computer program to perform a method of processing point cloud data to generate a digital elevation model according to any one of claims 1 to 8.
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| CN114689015A (en) * | 2021-11-29 | 2022-07-01 | 成都理工大学 | Method for improving elevation precision of optical satellite stereoscopic image DSM |
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