CN110298875A - A kind of method that three-dimensional laser point cloud data obtains tree height automatically - Google Patents
A kind of method that three-dimensional laser point cloud data obtains tree height automatically Download PDFInfo
- Publication number
- CN110298875A CN110298875A CN201910571015.6A CN201910571015A CN110298875A CN 110298875 A CN110298875 A CN 110298875A CN 201910571015 A CN201910571015 A CN 201910571015A CN 110298875 A CN110298875 A CN 110298875A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- dimensional laser
- ground
- tree height
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000000605 extraction Methods 0.000 claims abstract description 5
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000000926 separation method Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Geometry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The present invention relates to a kind of methods that three-dimensional laser point cloud data obtains tree height automatically.Firstly, passing through three-dimensional laser scanner with obtaining sample three-dimensional laser point cloud data (X3S)；Secondly, three-dimensional laser point cloud data format to be switched to the TXT text document format of cartesian coordinate system, TXT text document format is switched into general format (Las)；And, based on GIS software by ground point cloud data separating, and with the reference of three-dimensional laser forest point cloud data, classify again by height, extracts and extend to corresponding local peak apart from the highest point cloud data of ground level；Finally, being reference with ground point cloud data, the method for binding site cloud voxelization is fitted away from the highest point cloud data of ground level, will be between the two apart from size as tree height.The present invention realizes the fast automatic extraction height of crop by point cloud data acquisition process, avoids different gradient to the high bring error obtained of tree, provides one more rapidly to carry out the personnel of correlative study from now on, more accurately obtains tree high-tech means.
Description
Technical field
The present invention relates to a kind of methods that three-dimensional laser point cloud data obtains tree height automatically.
Background technique
Currently, point cloud data, due to the needs of research, generally requires quickly to obtain standing forest in field of forestry fast development
Parameter information, how quickly, effectively obtain comprehensive standing forest parameter point cloud data seems especially important.Although by using ground
The research that base three-dimensional laser point cloud data extracts tree information is more universal, but entire standing forest point cloud data is believed as forest is extracted
Often there is the gradient and biggish error generated to the extraction of tree information, can not accurately reflect the actual information of object in breath.
Summary of the invention
The purpose of the present invention is to solve the above problem, and provides a kind of three-dimensional laser point cloud data and obtain forest tree automatically
High method realizes and more rapidly, more accurately sets high obtain.
To achieve the above object, the technical scheme is that a kind of three-dimensional laser point cloud data obtains forest tree automatically
High method, includes the following steps,
Step S1, the laying on sample ground is scanned；
Step S2, the format conversion of point cloud data；
Step S3, the fitting of ground point cloud and corresponding local highest point point cloud data.
In an embodiment of the present invention, in the step S1, the specific implementation of the laying on sample ground is scanned are as follows: pass through cloth
If the different scanning website of multiple height, pass through three-dimensional laser scanner with obtaining sample three-dimensional laser point cloud data.
In an embodiment of the present invention, in the step S2, the specific implementation of the format conversion of point cloud data are as follows: will
The three-dimensional laser point cloud data X3S of the acquisition of three-dimensional laser scanner switchs to the TXT text document format of cartesian coordinate system, will
TXT document format is converted to Las format point cloud data.
In an embodiment of the present invention, in the step S3, ground point cloud is quasi- with corresponding local highest point point cloud data
The specific implementation of conjunction are as follows: by the separation of ARCGIS software realization ground point cloud data, extend to and ground point cloud data phase
Point cloud is carried out voxelization, converts voxel for a cloud by corresponding part highest point point cloud data to improve extraction accuracy, and
Fitting obtains ground voxel to the distance between crown voxel size, to obtain tree height.
Compared to the prior art, the invention has the following advantages: the present invention is realized automatically by point cloud data layering
The height of crop is obtained, slope position is avoided to the high extraction bring error of tree, provides one to carry out the personnel of correlative study from now on
More rapidly, the more acurrate technological means for obtaining the height of crop.
Detailed description of the invention
Fig. 1 is the method for the present invention flow diagram.
Fig. 2 is Ground-based remote sensing Point Cloud Processing figure.
Fig. 3 is point cloud data format transition diagram.
Fig. 4 is ground point cloud datagram.
Fig. 5 is local highest point point cloud data figure.
Fig. 6 is point cloud voxelization schematic diagram.
Specific embodiment
With reference to the accompanying drawing, technical solution of the present invention is specifically described.
As shown in Figure 1, the present invention provides a kind of methods that three-dimensional laser point cloud data obtains tree height automatically, including
Following steps,
Step S1, the laying on sample ground is scanned；
Step S2, the format conversion of point cloud data；
Step S3, the fitting of ground point cloud and corresponding local highest point point cloud data.
Specific the method for the present invention implementation is as follows:
Firstly, obtaining the three-dimensional laser point cloud data of object by three-dimensional laser scanner；According to the matched software of instrument into
The pretreatments such as row format conversion, the denoising of point cloud, splicing, the three-dimensional laser point cloud data (Fig. 2) needed, by the three-dimensional of importing
Laser point cloud data point of use cloud processing software Stonex Reconstructor exports as the format of text document；It uses
The format point cloud data of the soft text document by object of cyclone Point Cloud Processing is converted into Las format, at the later period
It manages (Fig. 3).
Ground point cloud data is separated into (Fig. 4) with the formation of forest point cloud data by different elevations by GIS software；With separation
Ground point cloud data be reference, extend to the point cloud data of height maximum corresponding with ground point cloud data away from (Fig. 5),
Point cloud is subjected to voxelization (Fig. 6) simultaneously, point cloud at random is switched into three-dimensional voxel, ground voxel and canopy highest point voxel
The distance between size be exactly tree height, facilitate researcher to carry out follow-up study in the future.
The above are preferred embodiments of the present invention, all any changes made according to the technical solution of the present invention, and generated function is made
When with range without departing from technical solution of the present invention, all belong to the scope of protection of the present invention.
Claims (4)
1. a kind of method that three-dimensional laser point cloud data obtains tree height automatically, which is characterized in that include the following steps,
Step S1, the laying on sample ground is scanned；
Step S2, the format conversion of point cloud data；
Step S3, the fitting of ground point cloud and corresponding local highest point point cloud data.
2. the method that a kind of three-dimensional laser point cloud data according to claim 1 obtains tree height automatically, feature exist
In, in the step S1, the specific implementation of the laying on scanning sample ground are as follows: pass through and lay the different scanning movement of multiple height
Point passes through three-dimensional laser scanner with obtaining sample three-dimensional laser point cloud data.
3. the method that a kind of three-dimensional laser point cloud data according to claim 1 obtains tree height automatically, feature exist
In, in the step S2, the specific implementation of the format conversion of point cloud data are as follows: by the three of the acquisition of three-dimensional laser scanner
Dimension laser point cloud data X3S switchs to the TXT text document format of cartesian coordinate system, and TXT document format is converted to Las format
Point cloud data.
4. the method that a kind of three-dimensional laser point cloud data according to claim 1 obtains tree height automatically, feature exist
In, in the step S3, the specific implementation of ground point cloud and the fitting of corresponding local highest point point cloud data are as follows: pass through
The separation of ARCGIS software realization ground point cloud data extends to local highest point point cloud number corresponding with ground point cloud data
According to point cloud being carried out voxelization, converts voxel for a cloud, and be fitted and obtain ground voxel to crown to improve extraction accuracy
The distance between voxel size, to obtain tree height.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910571015.6A CN110298875A (en) | 2019-06-28 | 2019-06-28 | A kind of method that three-dimensional laser point cloud data obtains tree height automatically |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910571015.6A CN110298875A (en) | 2019-06-28 | 2019-06-28 | A kind of method that three-dimensional laser point cloud data obtains tree height automatically |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110298875A true CN110298875A (en) | 2019-10-01 |
Family
ID=68029306
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910571015.6A Pending CN110298875A (en) | 2019-06-28 | 2019-06-28 | A kind of method that three-dimensional laser point cloud data obtains tree height automatically |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110298875A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117952766A (en) * | 2024-03-26 | 2024-04-30 | 吉林省林业科学研究院(吉林省林业生物防治中心站) | Directional supervision method for forest data |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150063683A1 (en) * | 2013-08-28 | 2015-03-05 | Autodesk, Inc. | Building datum extraction from laser scanning data |
| CN105894044A (en) * | 2016-04-29 | 2016-08-24 | 武汉大学 | Single-plant tree point cloud automatic extraction method based on vehicle-mounted laser scanning data |
| CN106933786A (en) * | 2017-03-07 | 2017-07-07 | 福建农林大学 | A kind of three-dimensional laser point cloud data rapid voxel processing method |
| CN109002418A (en) * | 2018-06-20 | 2018-12-14 | 厦门大学 | Tree breast-height diameter automatic calculating method based on Voxels growing and ground laser point cloud |
-
2019
- 2019-06-28 CN CN201910571015.6A patent/CN110298875A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150063683A1 (en) * | 2013-08-28 | 2015-03-05 | Autodesk, Inc. | Building datum extraction from laser scanning data |
| CN105894044A (en) * | 2016-04-29 | 2016-08-24 | 武汉大学 | Single-plant tree point cloud automatic extraction method based on vehicle-mounted laser scanning data |
| CN106933786A (en) * | 2017-03-07 | 2017-07-07 | 福建农林大学 | A kind of three-dimensional laser point cloud data rapid voxel processing method |
| CN109002418A (en) * | 2018-06-20 | 2018-12-14 | 厦门大学 | Tree breast-height diameter automatic calculating method based on Voxels growing and ground laser point cloud |
Non-Patent Citations (1)
| Title |
|---|
| 骆钰波: "基于地面激光雷达点云数据的森林树高、胸径自动提取与三维重建", 《遥感技术与应用》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117952766A (en) * | 2024-03-26 | 2024-04-30 | 吉林省林业科学研究院(吉林省林业生物防治中心站) | Directional supervision method for forest data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104020475B (en) | A kind of line of electric force based on on-board LiDAR data extracts and modeling method | |
| CN109409272B (en) | Cable acceptance system and method based on machine vision | |
| KR100935857B1 (en) | System for generating 3d forest geographical information data using aerial lidar data and digital aerial photo data and method therefor | |
| CN112033414A (en) | Unmanned aerial vehicle inspection route generation method, device, equipment and medium | |
| CN105184859B (en) | A kind of substation's three-dimensional modeling method based on laser scanning | |
| CN101672915A (en) | High spatial resolution remote sensing image crown outline delineation system and method | |
| CN110853081B (en) | Ground and airborne LiDAR point cloud registration method based on single-tree segmentation | |
| CN111462134A (en) | Single-tree segmentation method and system for fusing high-resolution remote sensing image and laser radar point cloud | |
| CN105447855B (en) | A kind of Three Dimensional Ground laser scanning point cloud sphere Motion parameters method | |
| CN106446415B (en) | A kind of substation's support insulator center line extraction method based on point cloud data | |
| CN102800052A (en) | Semi-automatic digital method of non-standard map | |
| CN111241615A (en) | Highly realistic multi-source fusion three-dimensional modeling method for transformer substation | |
| CN109000621A (en) | A kind of power grid screen of trees detection method | |
| CN106886988B (en) | Linear target detection method and system based on unmanned aerial vehicle remote sensing | |
| CN104966101A (en) | Solar cell classification method based on LabVIEW | |
| CN107092905B (en) | Method for positioning instrument to be identified of power inspection robot | |
| CN106683105B (en) | Image segmentation method and image segmentation device | |
| CN106803093A (en) | A kind of plant species recognition methods based on blade textural characteristics and iOS platforms | |
| CN104331686A (en) | Manual assisting type recognition system of coverage rate of straw on soil surface | |
| Neubert et al. | Extraction of railroad objects from very high resolution helicopter-borne LiDAR and ortho-image data | |
| CN110298875A (en) | A kind of method that three-dimensional laser point cloud data obtains tree height automatically | |
| CN108680100B (en) | Method for matching three-dimensional laser point cloud data with unmanned aerial vehicle point cloud data | |
| CN108229502B (en) | Method for extracting base points of blades in three-dimensional point cloud data of tree canopy layer | |
| CN103617417A (en) | Automatic plant identification method and system | |
| Dietenberger et al. | Tree Stem Detection and Crown Delineation in a Structurally Diverse Deciduous Forest Combining Leaf-On and Leaf-Off UAV-SfM Data |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191001 |