CN111551956B - Geological disaster detection and identification method based on airborne laser radar - Google Patents

Abstract

The invention belongs to the technical field of address disaster detection, and particularly relates to a geological disaster detection and identification method based on an airborne laser radar, which comprises the following steps: an acquisition step, acquiring environmental point cloud data; a classification step, classifying the environmental point cloud data according to the surface point cloud and the interference point cloud, and extracting the surface point cloud; a delineating step, namely performing area delineation on the ground surface point cloud by using a preset grid, and performing two-dimensional gridding processing on the ground surface point cloud; encrypting, namely calculating the number of the surface point clouds in each grid, and when the number of the surface point clouds in each grid is less than N, performing surface point cloud density increasing processing on the grids according to a preset algorithm; and a display step, namely outputting and displaying the ground surface point cloud after the density increasing treatment. By using the method, the earth surface state can be detected more accurately.

Description

A Geological Hazard Detection and Identification Method Based on Airborne Lidar

technical field

The invention belongs to the technical field of address disaster detection, in particular to a geological disaster detection and identification method based on airborne laser radar.

Background technique

At present, new technologies are being used more and more widely in geological disaster prevention and control. High-resolution remote sensing technology, UAV oblique photography technology, insar technology, airborne lidar technology and other remote sensing detection technologies are used in geological disaster identification and monitoring. more and more widely.

However, various methods have technical bottlenecks, especially in the southwestern mountainous areas covered by high vegetation, many identification methods have different technical bottlenecks due to the influence of vegetation. The main performances are as follows: (1) High-resolution remote sensing technology mainly solves the problem of two-dimensional identification of planes, and identifies geological hazards by establishing remote sensing interpretation marks. Identify and measure. (2) UAV oblique photogrammetry realizes three-dimensional identification, which can obtain dangerous rock mass and collapse range, and realize identification and measurement of dangerous rock mass and collapse range, but it cannot remove vegetation information and obtain original surface information. (3) The insar technology is mainly affected by the terrain, blocked by the terrain, and has data blind spots. Although it has a certain ability to penetrate vegetation, the main disadvantage is that the spatial resolution is low, and there are data blind spots. (4) UAV airborne lidar technology has strong advantages over the other three technologies. The main disadvantage is that the equipment is expensive, the vegetation still has a certain impact, and there are still some ground points in the high vegetation coverage area, which affects the discrimination. .

In addition, although airborne lidar has acquired a lot of point cloud data, after classification, it is found that many of them are vegetation point clouds, especially in areas with high vegetation coverage, and some areas of surface point clouds are very sparse, especially in summer, broad-leaved vegetation may block points Cloud, resulting in no surface point cloud under vegetation. Affected by vegetation cover, some surface point clouds are blank, which cannot truly reflect the surface information.

Therefore, there is a need for a geological hazard detection and identification method based on airborne lidar, which can detect the surface state more accurately.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a geological disaster detection and identification method based on airborne laser radar, which can detect the surface state more accurately.

The basic scheme provided by the present invention is:

A method for detecting and identifying geological hazards based on airborne lidar, including:

The collection step is to collect environmental point cloud data;

The classification step is to classify the environmental point cloud data according to the surface point cloud and the interference point cloud, and extract the surface point cloud;

The delineation step is to delineate the surface point cloud with a preset grid, and perform two-dimensional grid processing on the surface point cloud;

In the encryption step, the number of surface point clouds in each grid is calculated, and when the number of surface point clouds in the grid is less than N, the grid is processed to increase the density of surface point clouds according to a preset algorithm;

The display step is to output and display the surface point cloud after density increase processing.

The working principle and beneficial effects of the basic scheme:

Since both the surface point cloud and the interference point cloud are three-dimensional coordinates, after collecting the environmental point cloud data, the point cloud data can be divided into the surface point cloud and the interference point cloud (such as trees) according to the characteristics of the point cloud data, and the surface points Cloud extraction. In this way, the collected point cloud data can be sieved, and the truly useful surface point cloud data can be selected.

After that, use the preset grid to delineate the area of the surface point cloud, perform two-dimensional grid processing on the surface point cloud, and calculate the surface point cloud density in each grid. If the number is less than N, it means that there are too few surface point clouds in the area where the grid is located, and the proportion of the area occluded by vegetation is too large. Therefore, according to the preset algorithm, the area where the grid is located is processed to increase the density of the surface point cloud, so that there is enough surface point cloud data in the grid.

Finally, the output of the surface point cloud after the density increase processing will be displayed. In this way, the output picture that the detector sees is the picture after the surface restoration processing has been carried out in the area covered by the vegetation. Such a picture can more realistically and completely reflect the real surface.

Compared with the prior art, using this method, the surface state can be detected more accurately.

Further, in the encryption step, the processing method for increasing the density of the point cloud in the grid is to select the M points closest to the grid distance, use the surface fitting method to perform surface fitting, and then use the surface back-calculation to extract the points in the grid. The coordinate information of the characteristic inflection point is generated, and new surface point cloud information is generated, so that the number of surface point clouds in the grid is not less than the preset number.

In this way, the existing surface point cloud data can be fully utilized, and at the same time, the inflection point coordinate information obtained by using the surface fitting method can more accurately restore the corresponding surface cloud points. In addition, the surface fitting model is more commonly used, its technology is relatively mature, and its operating efficiency is also very high.

Further, in the display step, the surface point cloud data is used to construct a triangulation network by using point cloud processing software, and after the rendering is generated, the output display is performed.

The content displayed in this way is convenient for the staff to intuitively understand the situation of the terrain.

Further, in the display step, the generated renderings include DEM and slope maps.

Through DEM and slope map, the original topography can be better restored, which is convenient for the staff to identify geological hazards.

Further, in the encryption step, if the number of point clouds in the grid is 0, the grid is equidistantly encrypted.

Such an encryption method can better restore the terrain for a grid with 0 point clouds in the grid.

Further, the value of N is greater than 3.

If the value of N is too small, it is difficult for the surface point cloud in the grid to accurately display the complete terrain of the area.

Further, the value of N is less than 10.

Such a number of surface point clouds can already restore the grid area well.

Further, the value of M is greater than 3.

If the value of M is too small, it is difficult to effectively perform surface fitting, and the reliability of the obtained results is also difficult to guarantee.

Further, the size of the grid is 2m*2m.

Such a size of grid can ensure the effective utilization of the collected surface point cloud data while ensuring refinement.

Further, in the collection step, the airborne lidar is used to collect the environmental point cloud.

Compared with other detection methods, airborne lidar has flexible data acquisition, a large amount of data, and high measurement accuracy. The relative accuracy can reach 5cm, and it has a certain ability to penetrate vegetation.

Description of drawings

FIG. 1 is a schematic diagram of Embodiment 1 of a method for detecting and identifying geological hazards based on an airborne laser radar according to the present invention.

Detailed ways

The following is further described in detail by specific embodiments:

Example 1

As shown in Figure 1, a method for detecting and identifying geological hazards based on airborne lidar includes:

The collection step is to collect environmental point cloud data. In this embodiment, the collection tool is airborne lidar. Compared with other detection methods, airborne lidar has flexible data acquisition, a large amount of data, and high measurement accuracy. The relative accuracy can reach 5cm, and it has certain effects on vegetation. penetrating ability.

In the classification step, the environmental point cloud data is classified according to the surface point cloud and the interference point cloud, and the surface point cloud is extracted.

In the delineation step, a preset grid is used to delineate the area of the surface point cloud, and the surface point cloud is processed into a two-dimensional grid. In this embodiment, the size of the grid is 2m*2m. Such a size of grid can ensure the effective utilization of the collected surface point cloud data while ensuring refinement.

In the encryption step, the number of surface point clouds in each grid is calculated. When the number of surface point clouds in the grid is less than N, the grid is processed to increase the density of surface point clouds according to a preset algorithm. The value of N is greater than 3. If the value of N is too small, it is difficult for the surface point cloud in the grid to accurately display the complete terrain of the area. And the value of N is less than 10, because such a number of surface point clouds can already restore the grid area well. In this embodiment, the value of N is 6.

Specifically, the processing method for increasing the density of point clouds in the grid is to select the M points closest to the grid distance, perform surface fitting by means of surface fitting, and then use the surface back calculation to extract the characteristic inflection points in the grid. Coordinate information, generate new surface point cloud information, make the number of surface point clouds in the grid not less than the preset number, and realize point cloud encryption. If the number of point clouds in the grid is 0, the grid is equidistantly refined. Among them, the value of M is greater than 3. If the value of M is too small, it is difficult to effectively perform surface fitting, and the reliability of the obtained results is also difficult to guarantee.

The display step is to output and display the surface point cloud after density increase processing. The surface point cloud data is used to construct a triangular network with point cloud processing software, and after the rendering is generated, the output display is carried out. In this embodiment, the generated effect map includes a DEM and a gradient map.

The specific implementation process is as follows:

Since both the surface point cloud and the interference point cloud are three-dimensional coordinates, after collecting the environmental point cloud data with the airborne lidar, the point cloud data can be divided into the surface point cloud and the interference point cloud (such as trees) according to the characteristics of the point cloud data. , and extract the surface point cloud. In this way, the collected point cloud data can be sieved, and the truly useful surface point cloud data can be selected.

After that, use a 2m*2m grid to delineate the surface point cloud, perform two-dimensional grid processing on the surface point cloud, and calculate the surface point cloud density in each grid. The number of clouds is less than 6, indicating that the surface point clouds in the area where the grid is located are too few, and the proportion of the area occluded by vegetation is too large. Therefore, according to the preset algorithm, the area where the grid is located is processed to increase the density of the surface point cloud, so that there is enough surface point cloud data in the grid. If the number of point clouds in the grid is 0, the grid is equidistantly refined. Such an encryption method can better restore the terrain for a grid with 0 point clouds in the grid.

Finally, the surface point cloud after density increase processing will be used to construct a triangulation network using point cloud processing software. In this way, the output picture seen by the detectors is the picture after the surface restoration processing has been carried out in the area covered by vegetation, and through DEM and slope map, the original topography can be better restored, which is convenient for the staff to identify geological hazards, and can be more A true, complete response to the real surface.

Compared with the prior art, using this method, the surface state can be detected more accurately.

Embodiment 2

Different from the first embodiment, in this embodiment, in the classification step, the interference point cloud is also extracted; in the delineation step, the same grid is used to delineate the area of the interference cloud point, and the interference cloud point is subjected to a two-dimensional grid. processing.

In the encryption step, when the number of surface point clouds in the grid is less than N, the vegetation in the area is also identified according to the interference point cloud in the grid, and the shape characteristics of the vegetation are simulated by combining the interference point cloud data of the surrounding grid. The shape features include species, tree diameters and heights above the ground; the coordinates of the newly generated surface cloud points are also corrected according to the simulated vegetation shape features.

The specific implementation process is as follows:

When vegetation is taller and denser in foliage, it sometimes completely blocks the area at its bottom, which may consist of several meshes. If the growth point of the tree is specific compared to the surrounding area (eg growing in a pit or having a small plateau where the roots are rooted). At this time, using the method of the first embodiment, although the restored terrain can ensure a certain accuracy, it is difficult to restore these special terrains.

Using the method in this embodiment, when encrypting, the vegetation in the area can be identified according to the interference point cloud in the grid, and combined with the interference point cloud data of the surrounding grid, the vegetation shape features can be simulated, and the shape features include types, Tree diameter and height above ground. The coordinates of the newly generated surface cloud points are also corrected according to the simulated vegetation shape characteristics.

Through the shape characteristics of vegetation, combined with the new surface cloud points obtained by encryption, it is possible to effectively analyze whether the accuracy of the surface cloud points is appropriate. For example, if a tree is analyzed and its height above the ground is 25 meters, but according to the corresponding newly generated surface point cloud calculation in the grid, the height of the tree above the ground is only 24 meters, which means that the tree is generated in a pit. Therefore, the newly generated surface point cloud is corrected to make it more in line with the real surface cloud points.

Using this method, even if the growth point of a tree is specific compared to its surroundings, this method can also identify and restore.

The above are only the embodiments of the present invention, and the common knowledge such as the well-known specific structures and characteristics in the scheme has not been described too much here. Those of ordinary skill in the art know that the invention belongs to the technical field before the filing date or the priority date. Technical knowledge, can know all the prior art in this field, and have the ability to apply conventional experimental means before the date, those of ordinary skill in the art can improve and implement this scheme in combination with their own ability under the enlightenment given in this application, Some typical well-known structures or well-known methods should not be an obstacle to those skilled in the art from practicing the present application. It should be pointed out that for those skilled in the art, some modifications and improvements can be made without departing from the structure of the present invention. These should also be regarded as the protection scope of the present invention, and these will not affect the implementation of the present invention. Effectiveness and utility of patents. The scope of protection claimed in this application shall be based on the content of the claims, and the descriptions of the specific implementation manners in the description can be used to interpret the content of the claims.

Claims (7)

1. a kind of geological disaster detection and identification method based on airborne laser radar, is characterized in that, comprises: The collection step is to collect environmental point cloud data; The classification step is to classify the environmental point cloud data according to the surface point cloud and the interference point cloud, and extract the surface point cloud; The delineation step is to delineate the surface point cloud with a preset grid, and perform two-dimensional grid processing on the surface point cloud; In the encryption step, the number of surface point clouds in each grid is calculated. When the number of surface point clouds in the grid is less than N, the grid is processed to increase the density of surface point clouds according to the preset algorithm; The processing method of cloud density is to select M points with the closest distance from the grid, and then perform surface fitting by means of surface fitting, and then use the surface inverse calculation to extract the coordinate information of characteristic inflection points in the grid to generate new surface points. Cloud information, so that the number of surface point clouds in the grid is not less than the preset number; The display step is to output and display the surface point cloud after density increase processing; use the point cloud processing software to construct a triangulation network with the surface point cloud data, and generate the effect map, and then output and display; the generated effect map includes DEM and slope map; Among them, in the classification step, the interference point cloud is also extracted; in the delineation step, the same grid is used to delineate the area of the interference cloud point, and the interference cloud point is subjected to two-dimensional grid processing; In the encryption step, when the number of surface point clouds in the grid is less than N, the vegetation in the area is also identified according to the interference point cloud in the grid, and the shape characteristics of the vegetation are simulated by combining the interference point cloud data of the surrounding grid. The shape features include species, tree diameters and heights above the ground; the coordinates of the newly generated surface cloud points are also corrected according to the simulated vegetation shape features. 2. A kind of geological disaster detection and identification method based on airborne laser radar according to claim 1, is characterized in that: in the encryption step, if the number of point clouds in the grid is 0, then the grid is equidistantly encrypted . 3 . The method for detecting and identifying geological hazards based on airborne laser radar according to claim 1 , wherein the value of N is greater than 3. 4 . 4 . The method for detecting and identifying geological hazards based on airborne laser radar according to claim 1 , wherein the value of N is less than 10. 5 . 5 . The method for detecting and identifying geological hazards based on airborne laser radar according to claim 1 , wherein the value of M is greater than 3. 6 . 6 . The method for detecting and identifying geological hazards based on airborne laser radar according to claim 5 , wherein the size of the grid is 2m*2m. 7 . 7 . The method for detecting and identifying geological hazards based on airborne laser radar according to claim 6 , wherein in the collecting step, the airborne laser radar is used to collect environmental point clouds. 8 .

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