CN116433846A - Forest land cloud branch and leaf separation method based on size and local chaotic distance - Google Patents
Forest land cloud branch and leaf separation method based on size and local chaotic distance Download PDFInfo
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Abstract
The invention belongs to the technical field of ground laser radar point cloud data processing, and particularly relates to a forest land point cloud branch and leaf separation method based on size and local chaotic distance. According to the invention, woodland data acquired by using a foundation laser radar are clustered on the basis of region growing, and primary segmentation is carried out by the size of clustered clusters and the number of points in the point cloud; and then, carrying out fine segmentation on each of the rest clusters according to the size of the longest side of the circumscribed cuboid of the cluster and the chaotic distance en, dividing rest branches, and carrying out branch-leaf separation on the forest land scale. The separation accuracy and efficiency can also be adjusted by adjusting the number of neighbors searched during clustering and the smoothness threshold. The invention provides a new thought for forest land laser radar point cloud branch and leaf separation, which has both precision and efficiency, and can be further applied to the fields of measuring the average diameter and the height of a forest land trunk, measuring leaf area index, three-dimensional modeling of the forest land and the like.
Description
Technical Field
The invention belongs to the technical field of ground laser radar point cloud data processing, and particularly relates to a forest land point cloud branch and leaf separation method based on size and local chaotic distance.
Background
In recent years, with rapid development of photogrammetry and LiDAR (Light Detection and Ranging, liDAR) technology, liDAR three-dimensional point cloud data can be easily acquired. Lidar is an optical detection technique used mainly for distance measurement, which determines the distance by measuring the time of reflection of a laser pulse from a target object and obtains information about the shape, surface and material of the target object by calculating the energy and time of the echo pulse. Compared with the data acquired by other methods, the three-dimensional point cloud data of the laser radar can display the information of the surface, the shape and the like of an object more clearly and intuitively, so that the three-dimensional point cloud data is used in a plurality of fields, such as a geographic information system, robot vision, environment monitoring, power line inspection, city 3D modeling, intelligent agriculture, intelligent forest, biomass estimation, unmanned technology and the like, and the laser radar has higher development potential and vigorous development prospect.
In forestry related research, the laser radar technology is also widely applied, three-dimensional point cloud data has great advantages, three-dimensional point cloud coordinates and echo intensities are provided by foundation laser radar (Terrestrial Laser Scanning, TLS) data, the three-dimensional point cloud data and the TLS data have important purposes, and the three-dimensional point cloud data and the TLS data can accurately reflect characteristic parameters of some forests, such as trunk diameter and tree height, leaf area index, tree crown size and the like, which cannot be accurately reflected by traditional optical remote sensing technologies.
In the measurement of some characteristic parameters above in plots and forests, the use of point cloud data for more accurate branch and leaf separation is an important process. Because the forest structure and the tree structure are complex, the manual implementation of the branch-leaf separation can ensure the precision, but the defects are obvious, namely high cost and time consumption. In order to solve the problem, a plurality of methods for automatically and accurately separating branches and leaves are developed at home and abroad.
The traditional branch and leaf separation method mainly uses traditional optical remote sensing data, analyzes the data by means of characteristics such as vegetation indexes, height differences and the like, and has the problems of low segmentation precision, easiness in influence of vegetation shielding and the like. And the three-dimensional point cloud data provided by the laser radar technology can realize higher-precision branch-leaf separation by analyzing the data of multiple dimensions such as echo intensity, reflectivity, waveform, three-dimensional coordinates and the like. Since the echo intensities are susceptible to ambient and interference, they cannot be used directly to accurately perform the task of branch and leaf separation. At present, most of researches on the branch and leaf separation of the laser radar are focused on a method for separating branches and leaves of single wood and using machine learning, and in the existing method for separating branches and leaves, the branch and leaf separation of the single wood layer cannot completely meet the requirements in actual use; the machine learning method requires a large amount of data for manual training, requires a large amount of effort and time for initially designing a machine program, and is not compatible with robustness, efficiency and precision, and is difficult to separate branches and leaves of a woodland.
In summary, although the development prospect of the laser radar is quite optimistic, and can provide a lot of data with higher precision, the current research on branch and leaf separation still has a lot of defects, and the existing method cannot well meet the requirements in actual operation.
Disclosure of Invention
Aiming at the problems or the shortcomings, the invention provides a forest land cloud branch and leaf separation method based on the size and the local chaotic distance, which aims at solving the problems of low precision, higher cost, lower robustness and the like of branch and leaf separation of a forest land.
A forest land cloud branch and leaf separation method based on size and local chaotic distance comprises the following steps:
step 1, preliminary segmentation: clustering the foundation laser radar data point cloud of the target forest land, and primarily extracting the trunk point cloud:
1-1, clustering a foundation laser radar data point cloud of a target forest land by using a clustering algorithm based on region growing, and setting parameters: the number of neighbor points searched is set to 30-60, and the smoothing threshold is 0.05pi-0.07 pi.
1-2, respectively judging each class cluster generated by clustering in the step 1-1 as follows, and extracting branch class clusters:
if the number of points contained in the current class cluster exceeds 1500-2000, judging and marking the current class cluster as a branch.
If the maximum height difference (the maximum difference of coordinates in the Z-axis direction) of the current cluster exceeds a threshold dz, dz=0.75-2.00 m, the current cluster is judged and marked as a branch.
Step 2: and (3) carrying out the following operations on each cluster which is not judged to be a branch in all clusters generated by clustering in the step (1), and extracting the branch components again:
2-1, judging for each remaining class cluster: if the number n of the point clouds of the current class cluster is smaller than the threshold value 35-60, judging and marking the current class cluster as a leaf.
2-2, calculating the chaotic distance en of each cluster for the unlabeled clusters in the step 2-1, and judging:
taking points from the current class cluster one by one, taking the current point as a standard point, calculating the distance from other points in the class cluster to the standard point, taking the next point as the standard point after all calculation is completed, and repeating the calculation process to traverse all points of the current class cluster; and adding and dividing all calculated distances by the adding times to evaluate the chaotic distance en of the cluster population, wherein the calculation formula is as follows:
wherein X is i ,Y i ,Z i ,X j ,Y j ,Z j The X, Y and Z coordinates of the ith point and the jth point in the current class cluster are respectively represented, i and j take values in n, i is not equal to j, and the unit is m.
If en meets the threshold condition of 0.0m < en < 0.5m or en > 1.5m, and the longest side length of the circumscribed cuboid of the cluster is larger than L, L is more than or equal to 0.10m and less than or equal to 0.25m, the branch is judged, otherwise, the leaf is judged and marked.
Further, the foundation laser radar data point cloud of the target forest land used in the step 1 is preprocessed and then used, and the preprocessing is to discard the point cloud density ρ<144points/m 2 And/or using cloth analog filtering (Cloth Simulation Filter, CSF) methods to remove ground and weed points.
Further, the formula of en in the step 2 is multiplied by a scaling factor k, where k > 1, to increase the gap between the data, so that the screening result is better.
Step 1, principle related to step 2:
point cloud clustering algorithm principle based on region growing: the region growing subdivision algorithm (Region growing segmentation) is a clustering algorithm based on angular alignment between point normals, the purpose of which is to merge points that are sufficiently close in terms of smoothness constraints. It inputs a point cloud (P), a point normal (N), a point curvature (C), a neighbor finding function (Ω), a curvature threshold (C th ) And so on, after initializing the data, since the points with the smallest curvature are located in the flat area, the points are first sorted by curvature value. For ordered point clouds, the algorithm will choose the point with the smallest curvature value and region grow it before all points in the point cloud are marked. It follows that the algorithm will output a set of point clouds comprising a plurality of clusters, wherein each set of clusters is considered a portion of the same smooth surface.
Judging the chaotic distance of the point cloud: the calculation formula is formula (1), the formula can be judged according to the density degree of points in the class clusters, and the calculation process is as follows: first a fixed point is determined, the distances from the remaining points to the point are calculated, the summation is accumulated, then the accumulated number of times is removed, and the process is repeated with the next point until all points are calculated. In actual programming and use, the difference between the data can be increased by multiplying the formula by the scaling factor k (k > 1) to better screen.
According to the invention, woodland data acquired by using a foundation laser radar are clustered on the basis of region growing, and primary segmentation is carried out by the size of clustered clusters and the number of points in the point cloud, so that trunks of trees are mainly segmented; and then carrying out fine segmentation on each of the rest clusters through the size of the longest side and the chaotic distance (en) of the circumscribed cuboid of the cluster, dividing rest branches and leaf point clouds, and carrying out branch and leaf separation on the forest land scale. According to the invention, the separation precision and efficiency can be further adjusted by adjusting two parameters of the number of the searched adjacent points and the smoothness threshold value during clustering according to the practical requirements.
In conclusion, the method is simple in principle, the accuracy and the efficiency are considered, the branches and leaves of the forest land are separated, the efficiency and the accuracy can be adjusted according to different conditions, a new thought is provided for the branches and leaves separation of the laser radar point cloud of the forest land, and the method can be further applied to the fields of measuring the average diameter and the height of the trunk of the forest land, measuring the leaf area index, three-dimensional modeling of the forest land and the like.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a graph of the results of the cloud separation of branch points according to the embodiment;
FIG. 3 is a graph showing the result of the separation of the branches and leaves in the example.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples.
The following was made the target pattern data of this example by a piece of raw eucalyptus open forest (dry sclerophyll Box-Ironbark forest) pattern data in australia, victoria. The hardware configuration of the running program is: the processor is 11th Gen Intel (R) Core (TM) i5-11400H@2.70GHz, and the memory is SAMSUNG 16G 3200MHZ; the software is configured with: the operating system is Windows 11 family Chinese 22H2, the development environment is Visual Studio 2022, the programming language used is C++, and the third party library used is mainly PCL (Point Cloud Library).
A forest land cloud branch and leaf separation method based on size and local chaotic distance comprises the following steps:
step 1, data acquisition and pretreatment: the data used were from the native eucalyptus open forest (http:// dx. Doi/10.4227/05/542B 766D5D 00D) in Victoria, australia, as the distance sensor was comparedSince the density of the point cloud is low in the far place, the data is cut out after being downloaded, and the area (ρ) with low density of the point cloud is removed<144points/m 2 )。
And ground and weed points were removed using the cloth analog filtering (Cloth Simulation Filter, CSF) method in the point cloud processing software cloudcomputer.
Step 2, preliminary segmentation: clustering the foundation laser radar data point cloud of the target forest land, and primarily extracting the trunk point cloud. Wherein, the parameters are set, the number of the searched neighboring points is set to 30, the smoothness threshold is 0.05 pi, the point threshold is 1500, and the altitude difference threshold is 0.75m.
Step 3, extracting the branch components of each cluster which is not judged to be a branch from all clusters generated by clustering in the step 2 again:
the parameter setting, the threshold value of the number n of the point clouds is 50, the chaotic distance interval is [0.0m,0.5m ]. U.1.5 m, + ] and the proportionality coefficient k is 10, L is 0.15m, and the data is written after the segmentation is completed;
and (3) precision evaluation: and (3) obtaining the branch and leaf separation result of the point cloud of the sample area through the steps 1-3, wherein the branch and leaf separation result is shown in fig. 2 and 3.
The Accuracy evaluation calculates the separation result compared with the parameters such as Kappa coefficient, overall Accuracy (Accuracy) and the like of the manual separation point cloud, and the sample basic information and the calculation result of the separation Accuracy are shown in table 1, which reflects the separation conditions of all aspects.
TABLE 1 branch point cloud separation accuracy
As can be seen from the above experimental data, the present embodiment performs branch-leaf separation on an open-source plot foundation laser radar data. According to the method steps of the invention, the branch and leaf separation of the sample site cloud is realized. Quantitative accuracy evaluation is carried out on the separation result through Kappa coefficient and overall accuracy, and the result shows that the method can efficiently separate branches and leaves of the woodland on the premise of guaranteeing the branch and leaf separation accuracy.
Claims (3)
1. The forest land cloud branch and leaf separation method based on the size and the local chaotic distance is characterized by comprising the following steps of:
step 1, preliminary segmentation: clustering the foundation laser radar data point cloud of the target forest land, and primarily extracting the trunk point cloud:
1-1, clustering a foundation laser radar data point cloud of a target forest land by using a clustering algorithm based on region growing, and setting parameters: the number of the searched neighboring points is set to be 30-60, and the smoothing threshold value is 0.05 pi-0.07 pi;
1-2, respectively judging each class cluster generated by clustering in the step 1-1 as follows, and extracting branch class clusters:
if the number of points contained in the current class cluster exceeds 1500-2000, judging and marking the current class cluster as a branch;
if the maximum height difference of the current cluster exceeds a threshold dz, dz=0.75-2.00 m, judging and marking the current cluster as a branch;
step 2: and (3) carrying out the following operations on each cluster which is not judged to be a branch in all clusters generated by clustering in the step (1), and extracting the branch components again:
2-1, judging for each remaining class cluster: if the number n of the point clouds of the current class cluster is smaller than the threshold value 35-60, judging and marking the current class cluster as a leaf;
2-2, calculating the chaotic distance en of each cluster for the unlabeled clusters in the step 2-1, and judging:
taking points from the current class cluster one by one, taking the current point as a standard point, then calculating the distance from other points in the class cluster to the standard point, and taking the next point as the standard point after all calculation is completed; repeating the calculation process to traverse all points of the current cluster, and accumulating and summing all calculated distances and dividing the accumulated distances by the accumulated times to evaluate the overall chaotic distance en of the cluster, wherein the calculation formula is as follows:
wherein X is i ,Y i ,Z i ,X j ,Y j ,Z j X, Y and Z coordinates of an ith point and a jth point in the current class cluster are respectively represented, i and j take values in n, i is not equal to j, and the unit is m;
if en meets the threshold condition of 0.0m < en < 0.5m or en > 1.5m, and the longest side length of the circumscribed cuboid of the cluster is larger than L, L is more than or equal to 0.10m and less than or equal to 0.25m, the branch is judged, otherwise, the leaf is judged and marked.
2. The method for separating branches and leaves of forest land cloud based on the size and the local chaotic distance according to claim 1, wherein the method comprises the following steps of: the foundation laser radar data point cloud of the target forest land used in the step 1 is preprocessed and then used, and the preprocessing is to discard the point cloud density ρ<144points/m 2 And/or using cloth-like filtered CSF methods to remove ground points and weed points.
3. The method for separating branches and leaves of forest land cloud based on the size and the local chaotic distance according to claim 1, wherein the method comprises the following steps of: the formula of en in the step 2 is multiplied by a proportionality coefficient k, wherein k is larger than 1, so that the gap between data is increased, and the screening result is better.
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