CN111551956B - Geological disaster detection and identification method based on airborne laser radar - Google Patents
Geological disaster detection and identification method based on airborne laser radar Download PDFInfo
- Publication number
- CN111551956B CN111551956B CN202010601466.2A CN202010601466A CN111551956B CN 111551956 B CN111551956 B CN 111551956B CN 202010601466 A CN202010601466 A CN 202010601466A CN 111551956 B CN111551956 B CN 111551956B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- grid
- surface point
- interference
- clouds
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Geophysics And Detection Of Objects (AREA)
- Radar Systems Or Details Thereof (AREA)
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
Technical Field
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.
Background
At present, the new technology is more and more widely applied to geological disaster prevention and control work, and remote sensing technologies such as a high-resolution remote sensing technology, an unmanned aerial vehicle oblique photography technology, an insar technology and an airborne laser radar technology are more and more widely applied to geological disaster identification and monitoring.
However, various methods have technical bottlenecks, and particularly in southwest mountainous areas with high vegetation coverage, many identification methods affected by vegetation have different technical bottlenecks. The main performance is as follows: (1) the high-resolution remote sensing technology mainly aims at solving the problem of plane two-dimensional identification, geological disaster identification is carried out by establishing a remote sensing interpretation mark, dangerous rock collapse is mainly identified by interpreting a collapse range, and dangerous rock bodies cannot be identified and measured. (2) The unmanned aerial vehicle oblique photogrammetry realizes three-dimensional identification, can acquire dangerous rock masses and collapse ranges, realizes identification and measurement of the dangerous rock masses and the collapse ranges, but cannot remove vegetation information and acquire original earth surface information. (3) The insar technology is mainly influenced by terrain and shielded by the terrain, and has a data blind area, although the insar technology has certain vegetation penetrating capacity, the insar technology has the main defects of low spatial resolution and data blind area. (4) The unmanned aerial vehicle machine carries laser radar technique has very strong advantage for remaining three kinds of techniques, and the major disadvantage is that equipment is expensive, and vegetation still has certain influence, and high vegetation coverage still has partial ground point not enough, influences the judgement.
Besides, although the airborne laser radar acquires a plurality of point cloud data, the vegetation point clouds, especially high vegetation coverage areas, are found after classification, partial areas of the surface point clouds are quite sparse, and particularly, the broad-leaved vegetation can block the point clouds in summer, so that the surface point clouds are not found under the vegetation. Due to the influence of the covering vegetation, part of the point cloud of the earth surface is blank, and the earth surface information cannot be truly reflected.
Therefore, a geological disaster detection and identification method based on an airborne laser radar is needed, and the ground surface state can be detected more accurately.
Disclosure of Invention
The invention aims to provide a geological disaster detection and identification method based on an airborne laser radar, which can detect the earth surface state more accurately.
The basic scheme provided by the invention is as follows:
a geological disaster detection and identification method based on an airborne laser radar comprises the following steps:
collecting environmental point cloud data;
classifying, namely classifying the environmental point cloud data according to the earth surface point cloud and the interference point cloud, and extracting the earth surface point cloud;
a delineation 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.
Basic scheme theory of operation and beneficial effect:
because the surface point cloud and the interference point cloud are three-dimensional coordinates, after the environmental point cloud data is collected, 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 point cloud is extracted. By adopting the method, the collected point cloud data can be screened, and really useful ground surface point cloud data can be selected.
And then, performing area delineation on the ground surface point cloud by using a preset grid, performing two-dimensional gridding processing on the ground surface point cloud, and calculating the density of the ground surface point cloud in each grid, wherein if the number of the point clouds in a certain grid is less than N, the situation that the ground surface point cloud in the area where the grid is located is too few, the proportion of the area shielded by vegetation is too large, and the complete terrain is difficult to fully show only by using collected ground surface data is shown. Therefore, according to a preset algorithm, the earth surface point cloud density increasing processing is carried out on the area where the grid is located, and the grid is enabled to have enough earth surface point cloud data.
And finally, outputting and displaying the ground surface point cloud after the density increasing treatment. Thus, the output picture seen by the probe is a picture obtained by performing surface reduction processing on the vegetation-covered area. The picture can reflect the real earth surface more truly and completely.
Compared with the prior art, the method can be used for more accurately detecting the earth surface state.
Further, in the encryption step, the processing method for increasing the density of the point clouds in the grid comprises the steps of selecting M points which are closest to the grid, carrying out surface fitting in a surface fitting mode, extracting characteristic inflection point coordinate information in the grid by utilizing surface inverse calculation, and generating new surface point cloud information to enable the number of the surface point clouds in the grid to be not less than the preset number.
By adopting the method, the existing earth surface cloud point data can be fully utilized, and meanwhile, the inflection point coordinate information obtained by adopting the curved surface fitting method can be relatively accurately restored corresponding earth surface cloud points. Besides, the model of surface fitting is common, the technology is mature, and the operation efficiency is high.
Further, in the displaying step, the earth surface point cloud data is subjected to triangulation network construction by using point cloud processing software, and after an effect diagram is generated, the effect diagram is output and displayed.
The contents displayed in this way are convenient for the staff to visually know the terrain condition.
Further, in the step of displaying, the generated effect map comprises the DEM and the gradient map.
Through DEM and grade map, original topography landform that can be better restores, the staff of being convenient for carries out geological disaster discernment.
Further, in the encryption step, if the number of the point clouds in the grid is 0, the grid is encrypted equidistantly.
Such an encryption method can restore the terrain of a grid with a point cloud number of 0 in the grid well.
Further, the value of N is greater than 3.
If the numerical value of N is too small, the ground surface point cloud in the grid is difficult to accurately show the complete terrain of the area.
Further, the value of N is less than 10.
The number of the earth surface point clouds can well restore the terrain of the grid area.
Further, the value of M is greater than 3.
If the value of M is too small, the curved surface fitting is difficult to be effectively carried out, and the reliability of the obtained result is difficult to ensure.
Further, the size of the grid is 2m by 2 m.
The mesh with the size can guarantee refinement and simultaneously give consideration to the effective utilization rate of the collected earth surface point cloud data.
Further, in the acquisition step, an airborne laser radar is used for acquiring the environmental point cloud.
Compared with other detection means, the airborne laser radar has the advantages of flexible data acquisition, large data volume, high measurement precision, relative precision of 5cm and certain penetration capacity to vegetation.
Drawings
Fig. 1 is a schematic diagram of a first embodiment of a geological disaster detection and identification method based on an airborne laser radar.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
As shown in fig. 1, a geological disaster detection and identification method based on an airborne laser radar includes:
and an acquisition step, wherein environmental point cloud data are acquired. In this embodiment, the instrument of gathering is airborne laser radar, compares with other detection means, and airborne laser radar's data acquisition is nimble, and the data volume is big, and measurement accuracy is high, and relative precision can reach 5cm to have certain penetrability to the vegetation.
And a classification step, classifying the environmental point cloud data according to the earth surface point cloud and the interference point cloud, and extracting the earth surface point cloud.
And a delineation step, namely performing area delineation on the earth surface point cloud by using a preset grid, and performing two-dimensional gridding processing on the earth surface point cloud. In this embodiment, the size of the grid is 2m × 2 m. The mesh with the size can guarantee refinement and simultaneously give consideration to the effective utilization rate of the collected earth surface point cloud data.
And an encryption step, 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. The numerical value of N is more than 3, and if the numerical value of N is too small, the ground surface point cloud in the grid is difficult to accurately display the complete terrain of the area. And the value of N is less than 10, because the number of the earth surface point clouds can well perform terrain reduction on the grid area. In this example, N has a value of 6.
Specifically, the processing method for increasing the density of the point clouds in the grid comprises the steps of selecting M points closest to the grid, performing surface fitting in a surface fitting mode, extracting characteristic inflection point coordinate information in the grid by utilizing surface inverse calculation, generating new surface point cloud information, enabling the number of the surface point clouds in the grid to be not less than the preset number, and achieving point cloud encryption. And if the number of the point clouds in the grids is 0, carrying out equidistant encryption on the grids. Wherein the value of M is greater than 3. If the value of M is too small, it is difficult to perform effective surface fitting, and the reliability of the obtained result is also difficult to guarantee.
And a display step, namely outputting and displaying the ground surface point cloud after the density increasing treatment. And constructing a triangular network by using point cloud processing software for the surface point cloud data, generating an effect picture, and outputting and displaying. In this embodiment, the generated effect map includes the DEM and the gradient map.
The specific implementation process is as follows:
because the ground surface point cloud and the interference point cloud are three-dimensional coordinates, after the airborne laser radar is used for collecting the environmental point cloud data, the point cloud data can be divided into the ground surface point cloud and the interference point cloud (such as trees) according to the characteristics of the point cloud data, and the ground surface point cloud is extracted. By adopting the method, the collected point cloud data can be screened, and really useful ground surface point cloud data can be selected.
And then, carrying out region delineation on the ground surface point cloud by using 2 m-2 m grids, carrying out two-dimensional grid processing on the ground surface point cloud, and calculating the density of the ground surface point cloud in each grid, wherein if the number of the point clouds in a certain grid is less than 6, the situation that the ground surface point cloud in the area where the grid is located is too few, the proportion of the area which is shielded by vegetation is too large, and the complete terrain is difficult to fully show only by using collected ground surface data is shown. Therefore, according to a preset algorithm, the earth surface point cloud density increasing processing is carried out on the area where the grid is located, and the grid is enabled to have enough earth surface point cloud data. And if the number of the point clouds in the grids is 0, carrying out equidistant encryption on the grids. Such an encryption method can restore the terrain of a grid with a point cloud number of 0 in the grid well.
And finally, constructing a triangular network by using point cloud processing software for the surface point cloud after density increasing processing, and outputting and displaying the generated effect graph comprising the DEM and the gradient graph. Therefore, the output picture seen by the detector is the picture after the earth surface reduction treatment is carried out on the vegetation sheltered area, and the original landform and landform can be better reduced through the DEM and the gradient map, so that the geological disaster identification can be conveniently carried out by the worker, and the real earth surface can be more truly and completely reflected.
Compared with the prior art, the method can be used for more accurately detecting the earth surface state.
Example two
Different from the first embodiment, in the present embodiment, in the classification step, an interference point cloud is further extracted; in the step of delineating, the same grid is used for carrying out region delineation on the interference cloud points, and two-dimensional gridding processing is carried out on the interference cloud points.
In the encryption step, when the number of the earth surface point clouds in the grid is less than N, the vegetation in the area is identified according to the interference point clouds in the grid, and the appearance characteristics of the vegetation are simulated by combining the interference point cloud data of the peripheral grid, wherein the appearance characteristics comprise the type, the tree diameter and the height above the ground; and correcting the coordinates of the newly generated surface cloud points according to the simulated vegetation appearance characteristics.
The specific implementation process is as follows:
when the vegetation is high and the density of branches and leaves is high, the area at the bottom of the vegetation is sometimes completely covered, and the area may be composed of a plurality of grids. If the growth point of the tree is specific compared with the periphery (such as growing in a pit or having a small plateau at the root-pricking place). In this case, although the reduced topography can ensure a certain accuracy by the method of example one, it is difficult to reduce these particular topographies.
By using the method in the embodiment, during encryption, the vegetation in the area can be identified according to the interference point cloud in the grid, and the vegetation appearance characteristics including species, tree diameter and height above the ground can be simulated by combining the interference point cloud data of the peripheral grid. And according to the simulated vegetation appearance characteristics, carrying out coordinate correction on the newly generated surface cloud points.
Through the appearance characteristics of the vegetation and the combination of the new surface cloud points obtained through encryption, whether the accuracy of the surface cloud points is proper or not can be effectively analyzed. Such as: the height above the ground of a certain tree is analyzed to be 25 meters, and the tree is generated in a pit if the height above the ground of the certain tree is only 24 meters according to the calculation of the corresponding newly generated ground surface point cloud in the grid. Therefore, the newly generated surface cloud point is corrected to be more consistent with the real surface cloud point.
By using the method, even if the growth point of the tree has specificity compared with the surrounding, the method can identify and reduce the growth point.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (7)
1. A geological disaster detection and identification method based on an airborne laser radar is characterized by comprising the following steps:
collecting environmental point cloud data;
classifying, namely classifying the environmental point cloud data according to the earth surface point cloud and the interference point cloud, and extracting the earth surface point cloud;
a delineation 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; the processing method for increasing the density of the point clouds in the grids comprises the steps of selecting M points which are closest to the grids, carrying out surface fitting in a surface fitting mode, extracting characteristic inflection point coordinate information in the grids by utilizing surface inverse calculation, and generating new surface point cloud information to enable the number of the surface point clouds in the grids to be not less than the preset number;
a display step, namely outputting and displaying the ground surface point cloud after the density increasing treatment; constructing a triangular net by using point cloud processing software for the surface point cloud data, generating an effect graph, and outputting and displaying; the generated effect graph comprises the DEM and the gradient graph;
in the classification step, interference point clouds are extracted; in the delineating step, the same grid is used for carrying out region delineation on the interference cloud points, and two-dimensional gridding processing is carried out on the interference cloud points;
in the encryption step, when the number of the earth surface point clouds in the grid is less than N, the vegetation in the area is identified according to the interference point clouds in the grid, and the appearance characteristics of the vegetation are simulated by combining the interference point cloud data of the peripheral grid, wherein the appearance characteristics comprise the type, the tree diameter and the height above the ground; and correcting the coordinates of the newly generated surface cloud points according to the simulated vegetation appearance characteristics.
2. The method for detecting and identifying geological disasters based on airborne lidar according to claim 1, wherein: in the encryption step, if the number of the point clouds in the grid is 0, the grid is encrypted equidistantly.
3. The method for detecting and identifying geological disasters based on airborne lidar according to claim 1, wherein: the value of N is greater than 3.
4. The method for detecting and identifying geological disasters based on airborne lidar according to claim 1, wherein: the value of N is less than 10.
5. The method for detecting and identifying geological disasters based on airborne lidar according to claim 1, wherein: the value of M is greater than 3.
6. The method for detecting and identifying geological disasters based on airborne lidar according to claim 5, wherein the method comprises the following steps: the size of the grid is 2m x 2 m.
7. The method for detecting and identifying geological disasters based on airborne lidar according to claim 6, wherein: in the acquisition step, an airborne laser radar is used for carrying out environmental point cloud acquisition.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010601466.2A CN111551956B (en) | 2020-06-28 | 2020-06-28 | Geological disaster detection and identification method based on airborne laser radar |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010601466.2A CN111551956B (en) | 2020-06-28 | 2020-06-28 | Geological disaster detection and identification method based on airborne laser radar |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111551956A CN111551956A (en) | 2020-08-18 |
| CN111551956B true CN111551956B (en) | 2022-05-20 |
Family
ID=72005377
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010601466.2A Active CN111551956B (en) | 2020-06-28 | 2020-06-28 | Geological disaster detection and identification method based on airborne laser radar |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111551956B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113506203A (en) * | 2021-07-12 | 2021-10-15 | 京创智慧科技有限责任公司 | Data processing method, device, equipment and storage medium |
| CN114266987B (en) * | 2022-03-03 | 2022-05-20 | 水利部长江勘测技术研究所 | Intelligent identification method for high slope dangerous rock mass of unmanned aerial vehicle |
| CN115731361B (en) * | 2022-11-22 | 2024-05-03 | 广东佛山地质工程勘察院 | Geological disaster enhanced display method based on laser LiDAR data |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103268632B (en) * | 2013-01-07 | 2015-12-09 | 河海大学 | A kind of airborne laser radar scanning generates the method for terrain information |
| CN103679655A (en) * | 2013-12-02 | 2014-03-26 | 河海大学 | LiDAR point cloud filter method based on gradient and area growth |
| CN107479045B (en) * | 2017-06-29 | 2020-03-17 | 武汉天擎空间信息技术有限公司 | Method and system for eliminating short vegetation based on full-waveform laser radar point cloud data |
| CN109658477A (en) * | 2017-10-12 | 2019-04-19 | 西南科技大学 | A kind of DEM generating algorithm based on LIDAR data |
| CN109657525A (en) * | 2017-10-12 | 2019-04-19 | 西南科技大学 | A kind of rapid correction method for traditional LiDAR filtering error |
| CN110880202B (en) * | 2019-12-02 | 2023-03-21 | 中电科特种飞机系统工程有限公司 | Three-dimensional terrain model creating method, device, equipment and storage medium |
| CN111077535B (en) * | 2019-12-11 | 2022-03-01 | 中煤能源研究院有限责任公司 | Laser radar data acquisition system and data processing method |
| CN111105496A (en) * | 2019-12-19 | 2020-05-05 | 昆明能讯科技有限责任公司 | High-precision DEM construction method based on airborne laser radar point cloud data |
-
2020
- 2020-06-28 CN CN202010601466.2A patent/CN111551956B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111551956A (en) | 2020-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Moudrý et al. | Comparison of leaf-off and leaf-on combined UAV imagery and airborne LiDAR for assessment of a post-mining site terrain and vegetation structure: Prospects for monitoring hazards and restoration success | |
| CN111551956B (en) | Geological disaster detection and identification method based on airborne laser radar | |
| Steger et al. | The propagation of inventory-based positional errors into statistical landslide susceptibility models | |
| Fanos et al. | A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS | |
| Gigli et al. | Semi-automatic extraction of rock mass structural data from high resolution LIDAR point clouds | |
| Qu et al. | Post-failure landslide change detection and analysis using optical satellite Sentinel-2 images | |
| Borkowski et al. | Landslides mapping in Roznow Lake vicinity, Poland using airborne laser scanning data | |
| Estornell et al. | Study of shrub cover and height using LiDAR data in a Mediterranean area | |
| Scaioni et al. | Change detection and deformation analysis in point clouds | |
| CN117272191B (en) | Urban underground space intelligent mapping method and system | |
| Wan-Mohd-Jaafar et al. | Modelling individual tree aboveground biomass using discrete return lidar in lowland dipterocarp forest of Malaysia | |
| Karantanellis et al. | 3D hazard analysis and object-based characterization of landslide motion mechanism using UAV imagery | |
| Kobal et al. | Lidar processing for defining sinkhole characteristics under dense forest cover: A case study in the Dinaric mountains | |
| Karakas et al. | Landslide susceptibility mapping with random forest model for Ordu, Turkey | |
| CN105975990B (en) | A kind of fine expression of Reclaimed Soil section | |
| Bernat Gazibara et al. | Identification and mapping of shallow landslides in the City of Zagreb (Croatia) using the LiDAR–based terrain model | |
| CN111144340A (en) | Method and system for automatically monitoring human activities in natural reserve area based on night light and high-resolution remote sensing image | |
| Süleymanoğlu et al. | Comparison of filtering algorithms used for DTM production from airborne lidar data: A case study in Bergama, Turkey | |
| Sarıtaş et al. | Enhancing Ground Point Extraction in Airborne LiDAR Point Cloud Data Using the CSF Filter Algorithm | |
| Saputra et al. | The application of structure from motion (SfM) to identify the geological structure and outcrop studies | |
| Miliaresis et al. | Terrain pattern recognition and spatial decision making for regional slope stability studies | |
| Liu et al. | Processing outcrop point clouds to 3D rock structure using open source software | |
| Tian et al. | A process-oriented method for rapid acquisition of canopy height model from RGB point cloud in semiarid region | |
| CN113506203A (en) | Data processing method, device, equipment and storage medium | |
| CN112907567A (en) | SAR image ordered artificial structure extraction method based on spatial reasoning method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230331 Address after: 401120 No. 111 Lanxin Avenue, Airport New Town, Yubei District, Chongqing Patentee after: CHONGQING INSTITUTE OF GEOLOGY AND MINERAL RESOURCES Patentee after: PLA Army Service College Address before: 401121 No. 111, Lanxin Avenue, airport new town, Yubei District, Chongqing Patentee before: CHONGQING INSTITUTE OF GEOLOGY AND MINERAL RESOURCES |
|
| TR01 | Transfer of patent right |