CN114511546A - Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants - Google Patents

Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants Download PDF

Info

Publication number
CN114511546A
CN114511546A CN202210137082.9A CN202210137082A CN114511546A CN 114511546 A CN114511546 A CN 114511546A CN 202210137082 A CN202210137082 A CN 202210137082A CN 114511546 A CN114511546 A CN 114511546A
Authority
CN
China
Prior art keywords
point
forest
point cloud
points
circle
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
Application number
CN202210137082.9A
Other languages
Chinese (zh)
Inventor
房新玉
刘杰
刘盾
解静
熊伟
姚晓伟
张大龙
李全荣
刘昔
刘彦祥
柯敏
宋维敏
李治朋
赵靓
张玥
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin Water Transport Engineering Survey and Design Institute
Original Assignee
Tianjin Water Transport Engineering Survey and Design Institute
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Tianjin Water Transport Engineering Survey and Design Institute filed Critical Tianjin Water Transport Engineering Survey and Design Institute
Priority to CN202210137082.9A priority Critical patent/CN114511546A/en
Publication of CN114511546A publication Critical patent/CN114511546A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/08—Measuring arrangements characterised by the use of optical techniques for measuring diameters
    • 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
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • 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
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • 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
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture
    • 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

Abstract

The invention discloses a DBSCAN clustering and four-quadrant-based laser point cloud forest breast diameter obtaining method, which comprises the following steps of: firstly), collecting forest laser point cloud data of a measuring area; secondly), acquiring a point cloud with a specified thickness at the breast diameter of the forest in the measurement area to generate a forest breast diameter point cloud file; thirdly) grouping the point clouds of the breast diameters of different trees by adopting a DBSCAN clustering method to obtain a point cloud set of the breast diameter position and a support rod of a single tree; fourthly) classifying by adopting a DBSCAN clustering method, eliminating noise points of supporting rods around the single forest to obtain a point cloud set at the breast diameter of the single forest; fifthly), filtering noise points to obtain an optimal point cloud set of the breast height of a single tree; and sixthly) calculating an optimal circle by adopting a least square calculation method to obtain the tree diameter and the forest center. The method can automatically and quickly separate the point cloud of the breast height of each tree, segment the optimal breast height point set of the single tree, and then calculate, and has accurate calculation, high intelligent degree and high efficiency.

Description

Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants
Technical Field
The invention relates to the technical field of survey and measurement of urban greening trees, in particular to a DBSCAN (direct species radar controller area network) clustering and four-quadrant-based laser point cloud tree breast height diameter obtaining method.
Background
Along with the development of society, the requirements of people on living environment are higher and higher, and the functions of urban greening are gradually known, and the functions are closely related to the physical and mental health of people and the improvement and improvement of living environment quality. Urban landscaping has had an irreplaceable and quantifiable role in cities. The trees play an important role in urban greening, and in order to maintain urban greening resources, urban greening resource investigation must be regularly carried out, and the important point of the urban greening resource investigation is the investigation of urban greening tree resources. The investigation of forest resources is most critical to measuring forest diameter. In urban greening engineering, the diameter of the forest also directly affects the construction cost.
With the development of three-dimensional laser scanning technology, three-dimensional laser scanners are widely applied to acquisition of tree breast diameters, laser point cloud data of tree breast diameters are obtained by performing data processing on three-dimensional laser scanning data, and at present, tree breast diameter extraction work aiming at the laser point cloud mainly adopts manpower to judge the position of the tree breast diameter point cloud in a computer and a graphic device (CAD) thereof, manually eliminates noise points, and then performs individual plant calculation [ a tree breast diameter extraction method based on single-station three-dimensional laser scanning data, Korea and the like, Zhejiang forestry technologies, 2019, 39(2), 87-91], which needs a large amount of manual participation, filters noise points, screens point cloud data, and has low efficiency.
Disclosure of Invention
The invention provides a laser point cloud forest breast diameter acquisition method based on DBSCAN clustering and four quadrants, which can automatically and quickly separate the breast diameter point cloud of each forest, segment the optimal breast diameter point set of a single forest and then perform calculation, and has the advantages of accurate calculation, high intelligent degree and high efficiency.
The technical scheme adopted by the invention for solving the technical problems in the prior art is as follows: a laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants comprises the following steps:
firstly), collecting forest laser point cloud data of a measuring area by adopting a standing three-dimensional laser scanner;
secondly), splicing, cutting and intercepting the laser point cloud data of the forest, acquiring point cloud with the appointed thickness at the breast diameter of the forest in the measuring area, and generating a forest breast diameter point cloud file;
thirdly) grouping the point clouds of the breast diameters of different trees by adopting a DBSCAN clustering method to obtain a point cloud set of the breast diameter position and a support rod of a single tree;
fourthly) classifying by adopting a DBSCAN clustering method, eliminating noise points of supporting rods around the single forest to obtain a point cloud set at the breast diameter of the single forest;
fifthly), filtering noise points to obtain an optimal point cloud set of the breast height of a single tree;
and sixthly) calculating the optimal circle of the point set by taking the optimal point set as the point set, taking a circle equation as a basic equation and adopting a least square calculation method, wherein the radius of the optimal circle is the diameter of the forest, and the center of the optimal circle is the center of the forest.
The method of the fourth step is that, for the point cloud collection of the individual trees and the tree support rods, according to the DBSCAN clustering method, the scanning radius is set to be 0.04 m, the minimum number of scanning points is 7 points, the point cloud data of the individual trees and the support rods around the trees are classified, and when the point cloud is classified into one type, the point collection is the individual tree point collection; when the point sets are divided into two types, the point set with more points is a single tree point set; when the point sets are divided into three types, firstly, judging whether the number of points of one type of point set is more than 5 times of the number of points of the other two types of point sets, if so, the point set is a single forest point set; if not, calculating the central points of the three classification point sets to form a triangle, wherein the point set with the largest angle is the single-plant forest point set; and when the number of the point sets is more than four, calculating the central point of each category point set and the central points of all the category point sets, wherein the point sets close to the central points of all the category point sets are single forest point sets.
The method of the fifth step) comprises the steps of calculating an average value of the single-plant forest point cloud set, obtaining a mean value point of the single-plant forest point cloud set, and dividing the single-plant forest point cloud data into four quadrants by taking the mean value point as a central point; calculating the point which is farthest away from the central point in each quadrant, and constructing a triangle by sequentially connecting the point which is farthest away from the central point and the central point in each quadrant; calculating the number of points in the unit area of each triangle, and determining the triangle with the least number of points in the unit area as the triangle with the center of the breast diameter of the single tree;
calculating the circumscribed circle of the triangle with the least points in the unit area to obtain the center and the radius of the circumscribed circle; taking the center of an circumscribed circle as the center of the circle, enlarging the radius of the circumscribed circle by 0.03 meter to form a circumscribed new circle, calculating points falling into the circumscribed new circle, and then judging whether the proportion of the point set in the breast height point set of the single tree is more than 80 percent or not, if so, the point set is an optimal point set; if not, forming a central circle by taking the central point as the center of the circle and the radius of the external circle as the radius, calculating points which fall into the central circle and the external new circle in the single tree point set, and taking the point set as a new tree point set;
constructing a rectangular area by taking the center of an external circle as an initial vibration center and taking a connecting line of the center of the external circle and the center point as a diagonal line, and moving the rectangular area by 0.003 m in the X/Y direction respectively to form a vibration center set in the rectangular area; when constructing the vibration center set, deducting points 0.01 m away from the center point; constructing a vibration circle by taking the vibration center as the center of a circle and the distance between the vibration center and the center point as the radius, and constructing vibration rings by taking the inner and outer radii of the vibration circle as 0.01 meter respectively;
moving the vibration center in the vibration center set, constructing different vibration rings, calculating points falling into each vibration ring in the new forest point set, and after the whole vibration center set is circulated once, the vibration ring falling into the vibration ring with the largest number of points is the optimal vibration ring, and the point set in the vibration ring is the optimal point set.
And the method of the sixth step) comprises the steps of calculating the median of the optimal point set, solving the difference between the coordinates of all points and the median, and then calculating the optimal fitting circle of the point set according to the difference value and the least square method, thereby obtaining the position and the breast diameter of the single forest.
Loading a tree breast diameter point cloud file, setting the scanning radius to be 0.4 m and the minimum scanning point number to be 30 points by adopting a DBSCAN clustering method, segmenting the whole area tree data, and filtering noise points with the point number less than 30 points, thereby segmenting the point cloud data of each tree and the tree support rod.
In summary, the invention provides a laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants, which includes the steps of firstly collecting tree three-dimensional laser point cloud data, cutting out point cloud data at the breast diameter of a forest, carrying out individual plant segmentation on the forest data based on DBSCAN clustering, separating the individual plant forest data and the forest support rod data to obtain individual tree breast diameter point cloud data, then filtering noise points based on the four quadrants to obtain an optimal point set of the breast diameter of the forest, and finally calculating the position and the breast diameter of the forest by using a least square method.
The invention has the advantages and positive effects that: by applying the DBSCAN clustering method to the classification of the laser point cloud data of the breast diameter of the trees and the acquisition of the laser point cloud data of the breast diameter of a single tree, the breast diameter point cloud of each tree can be automatically and rapidly separated, and the breast diameter calculation efficiency of the trees is improved. The noise points are filtered by adopting a four-quadrant method, the optimal breast height laser point cloud of the single tree is obtained, calculation is carried out, calculation is accurate, the intelligent degree is high, the use is convenient, the efficiency is high, and the position and the breast height numerical value of the tree can be directly obtained.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of forest laser point cloud data scanned by a three-dimensional laser scanner;
FIG. 3 is a schematic diagram of a captured laser point cloud of the breast diameter of the forest;
FIG. 4 is a schematic view of a point cloud collection of different individual trees and their buttresses after the trees are primarily classified;
FIG. 5 is a schematic diagram of a point cloud collection of single trees after fine classification;
FIG. 6 is a schematic view of a diameter at breast height point set after noise points are removed by a four-quadrant method;
fig. 7 is a schematic diagram of an optimal circle fitted according to the least squares method.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings:
referring to fig. 1 to 7, in order to solve the problem of low efficiency of manually interpreting forest breast diameter point clouds in CAD, the invention provides a laser point cloud forest breast diameter acquiring method based on DBSCAN clustering and four quadrants, which includes the following steps:
firstly), a standing three-dimensional laser scanner is adopted to collect forest laser point cloud data of a survey area
And collecting the laser point cloud of the forest by adopting a standing laser scanner so as to obtain the laser point cloud of the forest from the ground to the crown in the whole measuring area.
Before use, the scanning instrument is placed in an observation environment for more than 30 minutes, then is connected and is subjected to power-on test, and the operation is started after the error is confirmed. During scanning operation, the distance between the stations is set to be 50m, and the stations are successively set to scan to obtain data.
Secondly), splicing, cutting and intercepting the laser point cloud data of the forest to obtain the point cloud with the appointed thickness at the breast diameter of the forest and generate a breast diameter point cloud file of the forest
The method comprises the following steps of taking a perpendicular bisector of a connecting line of two adjacent stations as a boundary line for field scanning, taking an enclosed area formed by the border of two sides of an urban greening forest as a single-station point cloud data cutting range, cutting rectangular single-station point cloud data, splicing the point cloud data obtained after cutting at each station by using station coordinates, and obtaining the point cloud data of the whole measuring area; according to the ground laser point cloud data of the whole measuring area, a measuring area DTM model is established, point cloud data of the designated thickness at the breast diameter of the forest is intercepted, and a forest breast diameter point cloud data file is generated.
Thirdly) grouping the point clouds of the breast diameters of different trees by adopting a DBSCAN clustering method to obtain the point cloud collection of the breast diameter position and the supporting rods of the single tree
Loading a forest breast diameter point cloud file, setting the scanning radius to be 0.4 m and the minimum scanning point number to be 30 points by adopting a DBSCAN clustering method, segmenting forest data in the whole area, and filtering noise points with the point number less than 30 points so as to segment point cloud data of each forest and each forest support rod.
Fourthly) classifying by adopting a DBSCAN clustering method, eliminating noise points of supporting rods around the single forest to obtain a point cloud set at the breast diameter of the single forest
And (3) setting the scanning radius to be 0.04 m and the minimum scanning point number to be 7 points for the point cloud sets of the single trees and the supporting rods of the trees according to a DBSCAN clustering method, and classifying the point cloud data of the single trees and the supporting rods around the trees to obtain the point cloud sets at the breast diameter of the single trees.
The point cloud of the single tree and the supporting rods thereof has the characteristics that the point cloud of the breast diameter of the single tree is generally centered, the point cloud of the breast diameter of the single tree is dense, and the number of the point clouds is large; the point cloud of the support rods is positioned at the edge, the number of points is less, and the number of the support rods is generally three and the support rods are symmetrically distributed;
according to the point cloud characteristics of the individual trees and the support rods thereof, when the point clouds are classified into one type, the point set is an individual tree point set; when the point sets are divided into two types, the point set with more points is a single tree point set; when the point sets are divided into three types, firstly, judging whether the number of points of one type of point set is more than 5 times of the number of points of the other two types of point sets, if so, the point set is a single forest point set; if not, calculating the central points of the three classification point sets to form a triangle, wherein the point set with the largest angle is the single-plant forest point set; and when the number of the point sets is more than four, calculating the central point of each category point set and the central points of all the category point sets, wherein the point sets close to the central points of all the category point sets are single forest point sets.
Fifthly), noise points are filtered, and the optimal point cloud set of the breast height of the single forest is obtained
Calculating the mean value of the single tree point cloud set, acquiring the mean value point of the single tree point cloud set, and dividing the single tree point cloud data into four quadrants by taking the mean value point as a central point; calculating the point which is farthest away from the central point in each quadrant, and constructing a triangle by sequentially connecting the point which is farthest away from the central point and the central point in each quadrant; calculating the number of points in the unit area of each triangle, and determining the triangle with the least number of points in the unit area as the triangle with the center of the breast diameter of the single tree;
calculating the circumscribed circle of the triangle with the least points in the unit area to obtain the center and the radius of the circumscribed circle; taking the center of an circumscribed circle as the center of the circle, enlarging the radius of the circumscribed circle by 0.03 meter to form a circumscribed new circle, calculating points falling into the circumscribed new circle, and then judging whether the proportion of the point set in the breast height point set of the single tree is more than 80 percent or not, if so, the point set is an optimal point set; if not, forming a central circle by taking the central point as the center of the circle and the radius of the external circle as the radius, calculating points which fall into the central circle and the external new circle in the single tree point set, and taking the point set as a new tree point set;
constructing a rectangular area by taking the center of an external circle as an initial vibration center and taking a connecting line of the center of the external circle and the center point as a diagonal line, and moving the rectangular area by 0.003 m in the X/Y direction respectively to form a vibration center set in the rectangular area; when constructing the vibration center set, deducting points 0.01 m away from the center point; constructing a vibration circle by taking the vibration center as the center of a circle and the distance between the vibration center and the center point as the radius, and constructing vibration rings by taking the inner and outer radii of the vibration circle as 0.01 meter respectively;
moving the vibration center in the vibration center set, constructing different vibration rings, calculating points falling into each vibration ring in the new forest point set, and after the whole vibration center set is circulated once, the vibration ring falling into the vibration ring with the largest number of points is the optimal vibration ring, and the point set in the vibration ring is the optimal point set.
The method comprises the steps of screening the positions of circle centers of the single trees by adopting a four-quadrant method, and then filtering noise points by adopting an occupation ratio method, an intersection method and a moving circle center method to obtain an optimal point set at the breast diameter of the single trees.
And sixthly) calculating the optimal circle of the point set by taking the optimal point set as the point set, taking a circle equation as a basic equation and adopting a least square calculation method, wherein the radius of the optimal circle is the diameter of the forest, and the center of the optimal circle is the center of the forest.
In order to prevent singular matrixes from appearing in the calculation process, the mean value of the optimal point set is calculated, the difference between the coordinates of all the points and the median value is obtained, and then the optimal fitting circle of the point set is calculated according to the difference value and the least square method, so that the position and the breast diameter of the single forest are obtained.
The urban greening forest survey has the characteristics of large quantity, high density, poor visibility condition, high survey precision and the like. The standing type three-dimensional laser scanner has the characteristics of high scanning precision and long scanning distance. By adopting the standing type three-dimensional laser scanner, the forest laser point cloud data can be quickly acquired.
And performing corresponding cutting, splicing and intercepting according to the scanned laser point cloud data to obtain the required forest laser point cloud data.
Preliminary classification, which step relies on computer program processing. And (3) for the forest breast diameter laser point cloud data, setting the scanning radius to be 0.4 m and the minimum scanning point number to be 30 points according to a DBSCAN clustering method, dividing the data, and segmenting a point cloud set of single trees and tree support rods.
Fine classification, which step depends on computer program processing. Setting the scanning radius to be 0.04 m and the minimum number of scanning points to be 7 points for the point cloud collection of the separated single trees and the tree support rods based on a DBSCAN clustering method, and dividing the data of the single trees and the tree support rods around; and screening out the point cloud data of the breast diameter of the single tree according to the point cloud characteristics of the single tree and the support rods.
And (4) providing a four-quadrant method for filtering noise points and determining an optimal point set for the precisely classified chest diameter point cloud. Calculating a mean value as a central point; dividing the point cloud into four quadrants according to the central point; then, according to the distance between the center point and the farthest point of the four quadrants, triangles are sequentially constructed, the triangle with the minimum point density is determined as the position of the center of the forest, then an external new circle is formed according to a certain radius, and then the ratio judgment, the intersection calculation and the moving circle center are used for filtering noise points to obtain an optimal point set.
Calculating the mean value of the obtained optimal point set, calculating the difference between the optimal point set and the mean value, then calculating the circle center and the diameter by adopting a least square method, and adding the coordinates of the circle center and the mean value to obtain the position of the forest, wherein the diameter is the breast diameter of the forest.
Examples of binding values:
taking a certain road greening area in Tianjin city as an example, the area is located in a new coastal area in Tianjin city, the area is banded, and trees in the area are numerous and densely distributed. Wherein, the west side is the arbor, the east side is the bush, only this time carries out data acquisition to the arbor. The distance between the trees is about 2.5m multiplied by 2.5m, and the tree age of the nursery stock is about 3 a.
Step 1, scanning measurement is carried out by adopting a Maptek I-Site 8820 type three-dimensional laser scanner, and data are collected by adopting a TrimbleR8GPS double-frequency receiver. And (3) placing the scanning instrument in an observation environment for more than 30 minutes, connecting and carrying out a power-on test, and starting operation after confirming no error. The drawing operation steps are as follows:
and acquiring three-dimensional coordinate information of a scanning station and a rear viewpoint by using GPS-RTK. The three-dimensional laser scanner is erected at a design station, the instrument is leveled, a rear view point of the three-dimensional laser scanner is accurately found, scanning station and rear view point coordinate information are input into a three-dimensional laser scanner control handbook, the angle of a scanning area is set to 360 degrees, scanning quality is set to be a middle grade, and scanning measurement is started.
And after the scanning is finished, checking the integrity of the data, supplementing the scanning if the data is missing, and replacing the station if the data is not missing until the whole scanning operation of the measuring area is finished. The forest point cloud effect of the scanned survey area is shown in fig. 2.
Step 2, taking a perpendicular bisector of a connecting line of two adjacent stations for field scanning as a boundary line, taking a closed area formed by the boundary line and the boundary at two sides of an urban greening forest as the cutting width of the point cloud data of the single station, setting the cutting length to be 50 meters, cutting rectangular point cloud data of the single station, and splicing the point cloud data obtained after cutting at each survey station by using coordinates of the set stations to obtain point cloud data of the whole area; according to the laser point cloud data of the ground of the survey area, a DTM model of the survey area is established, the point cloud data with the thickness of 0.04 meter away from the ground 13 meters is intercepted, and a forest breast diameter point cloud data file is generated. As shown in FIG. 3, the schematic diagram shows only the point cloud data of 4 trees for clarity and beauty
And 3, performing primary classification, namely, adopting a DBSCAN clustering algorithm, setting the classification radius to be 0.4 m and the minimum point number to be 30 points according to the distance between the trees to be greater than 0.4 m, segmenting the whole area of the tree data, and filtering noise points with the point number less than 30 points, thereby segmenting point cloud data of each tree and the tree support rods. As shown in fig. 4, 4 forest data after the preliminary classification are shown.
DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise) is a Density-Based Clustering algorithm. Unlike the partitioning and hierarchical clustering method, which defines clusters as the largest set of density-connected points, it is possible to partition areas with sufficiently high density into clusters and find clusters of arbitrary shape in a spatial database of noise.
Step 4, fine classification, setting the scanning radius to be 0.04 m, setting the minimum number of scanning points to be 7 points, and classifying the data of the individual trees and the supporting rods around the trees; and screening point cloud data of each forest. As shown in fig. 5, the point cloud data of the support rods are filtered out, and the point cloud data of the individual trees are obtained.
The point clouds of the single forest and the support rods have the characteristics that the point clouds of the breast diameters of the single forest are generally centered, the point clouds of the breast diameters of the single forest are dense and more in number, the point clouds of the support rods are arranged at the edges, the number of the point clouds is less, and the support rods are generally three or four and are symmetrically distributed; according to the point cloud characteristics of the individual trees and the support rods, when the point clouds are classified into one type, the point set is an individual tree point set; when the point sets are divided into two types, the point sets with a large number are single tree point sets; when the point sets are divided into three types, firstly, judging whether one type of point set is more than 5 times of the other two types, if so, the point set is a single forest point set; otherwise, calculating the central points of the three classification point sets to form a triangle, wherein the point set with the largest angle is the single-plant forest point set; and when the number of the point sets is more than four, calculating the central point of each category point set and the central points of all the category point sets, wherein the point sets close to the central points of all the point sets are single forest point sets.
When calculating the obtuse angle of the triangle, a distance formula between two points and a cosine theorem are needed.
The distance between two points is given by the following formula:
Figure BDA0003505308290000081
the cosine theorem:
Figure BDA0003505308290000082
step 5, averaging the single forest point set obtained in the step 4 to be used as a central point (X)0,Y0) According to the X/Y coordinates of each point in the point set and the central point (X)0,Y0) The distance relationship of (2) divides the point set into a four-quadrant point set.
Finding the mid-range center point (X) of each quadrant0,Y0) And constructing a triangle by the central point and the point of each quadrant farthest from the central point according to the sequence. For example, the center point and the first quadrant farthest point, the second quadrant farthest point construct a triangle, the center point and the second quadrant farthest point, the third quadrant farthest point construct a triangle, and so on. If a certain quadrant has no point, the quadrant is skipped, for example, the second quadrant has no point, the quadrant is skipped, and the central point and the farthest point of the first quadrant and the third quadrant construct a triangle.
Judging whether the three points are collinear according to a slope method, when x2 is x1, x3 is x1, or
Figure BDA0003505308290000083
The three points are collinear and do not form a triangle, otherwise, the triangle is formed.
And calculating the area of each triangle according to a Helen formula. The Helen formula is as follows: assuming that the side lengths of the triangle are a, b and c, respectively, the area S of the triangle can be obtained by the following formula:
Figure BDA0003505308290000084
and calculating the number of points of the single forest point set falling into each triangle according to the position relation of the points and the triangles.
The method for judging the position relation between the point and the triangle is as follows: suppose the three points of the triangle are a, B, C in clockwise (or counterclockwise) order. For a certain point P, three vectors PA, PB, PC are found, and then the following three cross-multiplications (^ indicates the cross-multiplicative sign) are calculated: t1 ^ PA, t2 ^ PB PC, t3 ^ PC PA, if t1, t2, t3 are of the same sign (same positive or same negative), then P is inside the triangle, otherwise outside.
And calculating the point density of each triangle according to the area of each triangle and the number of points falling into the triangle, wherein the triangle with the minimum point density is the position where the center of the breast diameter circle of the single tree is located.
And calculating the circumcircle of the triangle with the minimum point density according to a circumcircle formula.
The formula for calculating the circumscribed circle is as follows: let three vertexes of the triangle be A (X0, Y0), B (X1, Y1) and C (X2, Y2),
let a1 ═ X1-X0, b1 ═ Y1-Y0, c1 ═ a1 ═ a1+ b1 ═ b1)/2,
a2＝X2-X0，b2＝Y2-Y0，c2＝(a2*a2+b2*b2)/2，
d＝a1*b2-a2*b1
then: the center (XC, YC) coordinate is XC ═ x0+ (c1 ═ b2-c2 ═ b1)/d
YC＝y0+(a1*c2-a2*c1)/d
Figure BDA0003505308290000091
With external circle center (X)C,YC) And (3) as the center of a circle, enlarging the radius r of the external circle by 0.03 meter to form a new radius, and constructing a concentric circle.
And calculating the points falling into the concentric circles in the single tree point set according to the distance between the points and the circle center, judging the proportion of the number of the points in the circumscribed new circle to the number of the single tree point set, and if the proportion exceeds 80%, determining that the points in the circumscribed new circle are the optimal point set.
If the ratio is less than 80%, then the central point (X) is used0,Y0) And (3) constructing a central circle by taking the radius r of the circumscribed circle as the radius of the circle center, calculating points which fall into the central circle and the circumscribed new circle simultaneously in the single tree point set, and taking the point set as a new tree point set.
With external circle center (X)C,YC) Is the initial vibration center and is circumscribed by the circle center (X)C,YC) And a central point (X)0,Y0) The connecting line of the vibration center sensor is a diagonal line to construct a rectangular area, the rectangular area moves 0.003 m in the X/Y direction respectively, and a vibration center set is formed in the rectangular area; deduction from the center point (X) when building a set of vibration centers0,Y0)0.01 meter point.
Using the vibration center as the center of a circle, the vibration center and the center point (X)0,Y0) The distance of the vibration ring is the radius to construct a vibration circle, and the vibration ring is constructed by 0.01 meter inside and outside the radius of the vibration circle.
And moving the vibration center in the vibration center set, constructing different vibration rings, and calculating points falling into each vibration ring in the new forest point set. After the whole vibration center set is circulated once, the vibration ring with the largest number of points is the optimal vibration ring, and the point set in the vibration ring is the optimal point set.
And 6, calculating the mean value of the optimal point set according to the optimal point set of the single tree calculated in the step 5, calculating the difference between the optimal point set and the mean value, calculating an optimal circle according to a least square method, and adding the coordinates of the circle center and the mean value to obtain the position of the tree, wherein the diameter is the breast diameter of the tree. As shown in fig. 7.
The principle of the least squares method is as follows. Assuming a circle center coordinate of (x)0,y0) The radius is R, n two-dimensional coordinate points are distributed on the circle, and the position of the coordinate point is (x)i，yi),i＝1,2,...,n；
The basic equation for the circular curve is:
R2＝(x-x0)2+(y-y0)2 (1)
unfolding to obtain:
Figure BDA0003505308290000101
order to
Figure BDA0003505308290000102
Namely, it is
Figure BDA0003505308290000103
After the transformation of the formula (2) is
x2+y2-ax-by+c＝0 (5)
After the formula is popularized to n points, the formula can be obtained by writing the formula into a matrix form:
Figure BDA0003505308290000104
order:
Figure BDA0003505308290000105
then
AX＝L (8)
According to the least square algorithm, the following results are obtained:
X＝(ATA)-1ATL (9)
according to the formula (9), a, b and c can be obtained and substituted into the formula (4), and then the center coordinates and the radius can be obtained.
In summary, the invention relates to a laser point cloud forest breast diameter obtaining method based on dbss clustering and four quadrants, which adopts a three-dimensional laser scanner to scan urban greening forests, then intercepts the urban greening forests to obtain forest breast diameter laser point cloud data, provides an improved method for quickly obtaining the forest breast diameter, and particularly relates to dbss clustering and four quadrant noise filtering points, and specifically comprises the following steps of performing preliminary classification, fine classification, four quadrant noise filtering points and least square calculation: 1) collecting forest laser point cloud data by adopting a standing three-dimensional laser scanner; 2) splicing, cutting and intercepting the laser point cloud data of the trees to obtain point clouds with the appointed thickness at the breast diameter of the trees and generate a breast diameter point cloud file of the trees; 3) based on a DBSCAN clustering method, the point clouds at the breast radiuses of different trees are grouped to obtain a point cloud set at the breast radiuses of the individual trees and at the tree support rods. 4) Based on a DBSCAN clustering method, eliminating noise points of supporting rods around a single forest tree to obtain a point cloud set of the single forest tree; 5) based on a four-quadrant method, filtering noise points to obtain an optimal point cloud set of the breast height of a single tree; 6) and calculating the breast diameter of the forest by adopting a least square calculation method.
Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and those skilled in the art can make many modifications without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims (5)

1. A laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants is characterized by comprising the following steps:
firstly), collecting forest laser point cloud data of a measuring area by adopting a standing three-dimensional laser scanner;
secondly), splicing, cutting and intercepting the laser point cloud data of the forest, acquiring point cloud with the appointed thickness at the breast diameter of the forest in the measuring area, and generating a forest breast diameter point cloud file;
thirdly) grouping the point clouds of the breast diameters of different trees by adopting a DBSCAN clustering method to obtain a point cloud set of the breast diameter position and a support rod of a single tree;
fourthly) classifying by adopting a DBSCAN clustering method, eliminating noise points of supporting rods around the single forest to obtain a point cloud set at the breast diameter of the single forest;
fifthly), filtering noise points to obtain an optimal point cloud set of the breast height of a single tree;
and sixthly) calculating the optimal circle of the point set by taking the optimal point set as the point set, taking a circle equation as a basic equation and adopting a least square calculation method, wherein the radius of the optimal circle is the diameter of the forest, and the center of the optimal circle is the center of the forest.
2. The DBSCAN clustering and four-quadrant-based laser point cloud forest breast diameter obtaining method according to claim 1, wherein the method of the step four) is that for a point cloud set of individual trees and tree support rods, according to the DBSCAN clustering method, a scanning radius is set to be 0.04 m, the minimum number of scanning points is set to be 7 points, point cloud data of the individual trees and support rods around the trees are classified, and when the point cloud is classified into one type, the point set is the individual tree point set; when the point sets are divided into two types, the point set with more points is a single tree point set; when the point sets are divided into three types, firstly, judging whether the number of points of one type of point set is more than 5 times of the number of points of the other two types of point sets, if so, the point set is a single forest point set; if not, calculating the central points of the three classification point sets to form a triangle, wherein the point set with the largest angle is the single-plant forest point set; and when the number of the point sets is more than four, calculating the central point of each category point set and the central points of all the category point sets, wherein the point sets close to the central points of all the category point sets are single forest point sets.
3. The DBSCAN clustering and four-quadrant-based laser point cloud tree breast diameter obtaining method according to claim 1, wherein the method of the fifth step) is that an average value is obtained for a single tree point cloud set, a mean value point of the single tree point cloud set is obtained, and single tree point cloud data is divided into four quadrants by taking the mean value point as a central point; calculating the point which is farthest away from the central point in each quadrant, and constructing a triangle by sequentially connecting the point which is farthest away from the central point and the central point in each quadrant; calculating the number of points in the unit area of each triangle, and determining the triangle with the least number of points in the unit area as the triangle with the center of the breast diameter of the single tree;
calculating the circumscribed circle of the triangle with the least points in the unit area to obtain the center and the radius of the circumscribed circle; taking the center of an circumscribed circle as the center of the circle, enlarging the radius of the circumscribed circle by 0.03 meter to form a circumscribed new circle, calculating points falling into the circumscribed new circle, and then judging whether the proportion of the point set in the breast height point set of the single tree is more than 80 percent or not, if so, the point set is an optimal point set; if not, forming a central circle by taking the central point as the center of the circle and the radius of the external circle as the radius, calculating points which fall into the central circle and the external new circle in the single tree point set, and taking the point set as a new tree point set;
constructing a rectangular area by taking the center of an external circle as an initial vibration center and taking a connecting line of the center of the external circle and the center point as a diagonal line, and moving the rectangular area by 0.003 m in the X/Y direction respectively to form a vibration center set in the rectangular area; when constructing the vibration center set, deducting points 0.01 m away from the center point; constructing a vibration circle by taking the vibration center as the center of a circle and the distance between the vibration center and the center point as the radius, and constructing vibration rings by taking the inner and outer radii of the vibration circle as 0.01 meter respectively;
moving the vibration center in the vibration center set, constructing different vibration rings, calculating points falling into each vibration ring in the new forest point set, and after the whole vibration center set is circulated once, the vibration ring falling into the vibration ring with the largest number of points is the optimal vibration ring, and the point set in the vibration ring is the optimal point set.
4. The DBSCAN clustering and four-quadrant-based laser point cloud forest breast diameter obtaining method according to claim 1, wherein the method of the sixth step) is to calculate a median of an optimal point set, calculate a difference between coordinates of all points and the median, and then calculate an optimal fitting circle of the point set according to the difference by a least square method, so as to obtain the position and breast diameter of a single forest.
5. The DBSCAN clustering and four-quadrant-based laser point cloud forest breast diameter obtaining method according to claim 1, wherein the method in the third step) is that a forest breast diameter point cloud file is loaded, a DBSCAN clustering method is adopted, a scanning radius is set to be 0.4 m, the minimum scanning point number is 30 points, the whole area forest data is segmented, noise points with the point number less than 30 points are filtered, and therefore point cloud data of each forest and a forest support rod are segmented.
CN202210137082.9A 2022-02-15 2022-02-15 Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants Pending CN114511546A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210137082.9A CN114511546A (en) 2022-02-15 2022-02-15 Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210137082.9A CN114511546A (en) 2022-02-15 2022-02-15 Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants

Publications (1)

Publication Number Publication Date
CN114511546A true CN114511546A (en) 2022-05-17

Family

ID=81551460

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210137082.9A Pending CN114511546A (en) 2022-02-15 2022-02-15 Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants

Country Status (1)

Country Link
CN (1) CN114511546A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115937304A (en) * 2023-03-13 2023-04-07 季华实验室 Method and device for accurately estimating stumpage position and breast diameter through sparse point cloud
CN115953607A (en) * 2023-01-04 2023-04-11 北京数字绿土科技股份有限公司 Trunk diameter at breast height extraction method and system using point cloud data

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115953607A (en) * 2023-01-04 2023-04-11 北京数字绿土科技股份有限公司 Trunk diameter at breast height extraction method and system using point cloud data
CN115953607B (en) * 2023-01-04 2024-02-13 北京数字绿土科技股份有限公司 Trunk breast diameter extraction method and system using point cloud data
CN115937304A (en) * 2023-03-13 2023-04-07 季华实验室 Method and device for accurately estimating stumpage position and breast diameter through sparse point cloud
CN115937304B (en) * 2023-03-13 2023-06-16 季华实验室 Method and device for accurately estimating stump position and breast diameter through sparse point cloud

Similar Documents

Publication Publication Date Title
CN112595258B (en) Ground object contour extraction method based on ground laser point cloud
CN110111414B (en) Orthographic image generation method based on three-dimensional laser point cloud
EP2064657B1 (en) Geospatial modeling system providing building roof type identification features and related methods
Xu et al. Reconstruction of scaffolds from a photogrammetric point cloud of construction sites using a novel 3D local feature descriptor
CN114511546A (en) Laser point cloud forest breast diameter obtaining method based on DBSCAN clustering and four quadrants
CN109886961A (en) Medium-and-large-sized measurement of cargo measurement method based on depth image
CN112132840B (en) Vehicle-mounted street tree point cloud classification and feature information extraction method
CN114998536A (en) Model generation method and device based on novel basic mapping and storage medium
KR20100136604A (en) Real-time visualization system of 3 dimension terrain image
CN113916130B (en) Building position measuring method based on least square method
CN114332366A (en) Digital city single house point cloud facade 3D feature extraction method
CN113470090A (en) Multi-solid-state laser radar external reference calibration method based on SIFT-SHOT characteristics
CN114764871B (en) Urban building attribute extraction method based on airborne laser point cloud
CN114119863A (en) Method for automatically extracting street tree target and forest attribute thereof based on vehicle-mounted laser radar data
CN104751479A (en) Building extraction method and device based on TIN data
EP2150940A2 (en) Geospatial modeling system providing data thinning of geospatial data points and related methods
CN115761303A (en) Ground object classification method based on airborne laser radar point cloud and remote sensing image data
CN114663373A (en) Point cloud registration method and device for detecting surface quality of part
CN113963259A (en) Street view ground object multi-dimensional extraction method and system based on point cloud data
CN112233056A (en) Point cloud fusion method and device for multi-source data and storage medium
CN116258857A (en) Outdoor tree-oriented laser point cloud segmentation and extraction method
CN114862715A (en) TIN (triangulated irregular network) progressive encryption denoising method fusing terrain feature semantic information
CN114898118A (en) Automatic statistical method and system for power transmission line house removal amount based on multi-source point cloud
CN106446306A (en) Gauss sphere cluster based machine part reverse engineering modeling method
Rahmayudi et al. Comparison of semi automatic DTM from image matching with DTM from Lidar

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