Forest airborne laser radar point cloud cutting method, device, equipment, medium and product

Abstract

The invention discloses a forest airborne laser radar point cloud cutting method, device and equipment, a medium and a product, and relates to the field of forest airborne laser radar point cloud data processing.The method comprises the steps that according to a tree crown height model, tree crown edge features are quantized through mathematical morphology opening operation; the crown height model is constructed based on height information in forest airborne laser radar point cloud data; distance features are quantized according to the horizontal distance between the pixels in the crown height model and the neighborhood pixels of the crown height model; fusing the crown edge features and the distance features, and determining fusion features; constructing a connected graph according to the fusion features, and taking the shortest path between end points in the connected graph as a cutting line; and according to the cutting line, performing block processing on the crown height model, determining a plurality of block crown height models, and extracting forest airborne laser radar point cloud data in the block crown height models, thereby improving the cutting precision of the laser radar point cloud data of the crown in the forest scene.

Description

Technical Field

[0001] The present application relates to the field of forest area airborne laser radar point cloud data processing, and in particular to a forest area airborne laser radar point cloud clipping method, device, equipment, medium and product. Background Art

[0002] Compared with optical remote sensing and photogrammetry, LiDAR can penetrate the forest canopy and obtain detailed three-dimensional structural data of the terrain inside and under the forest, providing important support for quantifying forest terrain and forest structure, and is widely used in forest resource inventory and ecosystem research. LiDAR data mainly depicts the three-dimensional environment of the forest in the form of massive point clouds. After collecting LiDAR point cloud data, the point cloud needs to be cropped into multiple small-sized data blocks for data storage, preview and processing. Among them, point cloud cropping can be achieved through a variety of methods, including manual cropping, grid cropping, and attribute cropping; for manual cropping, the operator uses interactive tools in software such as CloudCompare and TerraSolid to manually crop the point cloud; grid cropping crops the point cloud based on grid lines; attribute cropping crops the point cloud according to the attributes of the LiDAR point cloud (such as scanning angle and data acquisition time). However, these automated methods cannot maintain the integrity of the objects, and the objects near the cutting line are often divided into different point cloud blocks.

[0003] Recently, some scholars have cropped point clouds by automatically identifying the gaps between objects to maintain the integrity of the objects in the block data. However, this method is more suitable for urban scenes. In forest scenes, the tree crowns are closely adjacent to each other, making it difficult to crop point clouds based on the gaps between the tree crowns. The cropped block point clouds contain incomplete tree crowns, which directly affects the accuracy of the calculation of forest structure characteristic parameters of airborne lidar point clouds, thereby limiting the estimation accuracy of parameters such as biomass, stock, and carbon storage. Summary of the invention

[0004] The purpose of this application is to provide a forest airborne lidar point cloud clipping method, device, equipment, medium and product to solve the problem of low clipping accuracy of lidar point cloud data of tree crowns in forest scenes.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for clipping forest area airborne laser radar point cloud, comprising:

[0007] According to the crown height model, the crown edge characteristics are quantified using mathematical morphological opening operations; the crown height model is constructed based on the height information in the forest airborne laser radar point cloud data;

[0008] quantifying distance features according to horizontal distances between pixels in the tree crown height model and neighboring pixels of the pixel;

[0009] Fusion of the crown edge feature and the distance feature to determine a fusion feature;

[0010] Constructing a connectivity graph according to the fusion features, and using the shortest path between endpoints in the connectivity graph as a cutting line;

[0011] The crown height model is divided into blocks according to the cutting line to determine a plurality of block crown height models, and the forest area airborne laser radar point cloud data in the block crown height models is extracted.

[0012] In the second aspect, the present application provides a forest area airborne laser radar point cloud clipping device, comprising:

[0013] A tree crown edge feature quantification module is used to quantify the tree crown edge features using mathematical morphological opening operations according to a tree crown height model; the tree crown height model is constructed based on the height information in the forest area airborne laser radar point cloud data;

[0014] A distance feature quantification module, used for quantifying the distance feature according to the horizontal distance between the pixel in the tree crown height model and the neighboring pixel of the pixel;

[0015] A fusion feature determination module, used to fuse the crown edge feature and the distance feature to determine a fusion feature;

[0016] A cutting line determination module, used to construct a connectivity graph according to the fusion features, and use the shortest path between each endpoint in the connectivity graph as a cutting line;

[0017] The block processing module is used to perform block processing on the crown height model according to the cutting line, determine multiple block crown height models, and extract the forest area airborne laser radar point cloud data in the block crown height model.

[0018] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described forest area airborne lidar point cloud clipping methods.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for clipping forest area airborne lidar point cloud.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned forest airborne lidar point cloud clipping methods.

[0021] According to the specific embodiments provided by the present application, the present application discloses the following technical effects: the present application determines a fusion feature that takes into account both the crown edge feature and the distance feature, and obtains the shortest path of each endpoint in the connected graph constructed by the fusion feature in a path planning manner as a cutting line, that is, the cutting line is the shortest broken line along the crown edge between the two endpoints, so as to achieve complete retention of the crown in the block point cloud, and ensure that the size of the block point cloud is highly close to the design range, thereby improving the cropping accuracy of lidar point cloud data in forest scenes, and providing technical support for the storage, preview and processing of massive lidar point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A flow chart of a method for cutting forest area airborne laser radar point cloud provided in an embodiment of the present application;

[0024] Figure 2 This is a key step effect diagram of an embodiment of the present application;

[0025] Figure 3 The figure is a comparison between the design range of point cloud clipping and the clipping result of this application; wherein, Figure 3 (a) is a schematic diagram of the design scope; Figure 3 (b) is a schematic diagram of the cutting results of this application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] The embodiment of the present application provides a method for cropping forest area airborne laser radar point cloud, which is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, Figure 1As shown, the method includes the following steps.

[0029] S1: According to the crown height model, the crown edge characteristics are quantified using mathematical morphological opening operations; the crown height model is constructed based on the height information in the forest airborne lidar point cloud data.

[0030] S2: quantifying the distance feature according to the horizontal distance between the pixel in the tree crown height model and the neighboring pixels of the pixel.

[0031] S3: Fusing the crown edge feature and the distance feature to determine a fusion feature.

[0032] S4: constructing a connectivity graph according to the fusion features, and using the shortest path between endpoints in the connectivity graph as a cutting line.

[0033] S5: Divide the tree crown height model into blocks according to the cutting line, determine a plurality of block tree crown height models, and extract the forest area airborne laser radar point cloud data in the block tree crown height models.

[0034] In an exemplary embodiment, before S1, the following steps are also included:

[0035] The cloth simulation filtering algorithm is used to extract the ground point cloud data from the airborne lidar point cloud data of the forest area.

[0036] A terrain model is constructed according to the ground point cloud data.

[0037] Normalizing the forest area airborne laser radar point cloud data, calculating the height difference between the forest area airborne laser radar point cloud data and the terrain model, and using the height difference as the point cloud elevation;

[0038] Using the grid method, a tree crown height model is constructed according to the point cloud elevation corresponding to the normalized point cloud data. The pixel value in the tree crown height model is the elevation value of the highest point in the corresponding grid.

[0039] In an exemplary embodiment, S1 may be replaced by the following steps:

[0040] S11: Smoothing the interior of the crown of the crown height model using a mathematical morphology opening operation, retaining the crown edge, and determining the crown height model after the opening operation.

[0041] S12: quantifying the crown edge features according to the crown height model and the change value of the crown height model after the opening operation.

[0042] Furthermore, mathematical morphology opening can smooth the interior of the crown and preserve the crown edge. The crown edge features are quantified by calculating the change value before and after the crown height model opening operation, as shown below.

[0043]

[0044] Where E(r,c) is the crown edge feature value of pixel (r,c), CHM(r,c) represents the pixel value of (r,c) in the crown height model, and SE represents the mathematical morphological opening operation. structural element.

[0045] EP(r,c,r′,c′)=E(r,c)+E(r′,c′)

[0046] Where EP(r,c,r',c') represents the crown edge eigenvalue between pixel (r,c) and its neighboring pixel (r',c').

[0047] In an exemplary embodiment, the distance feature is quantified using the horizontal distance between a pixel and its eight neighboring pixels, and S2 can be replaced by the following steps:

[0048] S21: According to the formula Among them, DP(r,c,r',c') is the distance characteristic value between any pixel (r,c) and its neighboring pixel (r',c'), r is the row number of the pixel, c is the column number of the pixel, r' is the row number of the neighboring pixel, c' is the column number of the neighboring pixel, and ps is the pixel size.

[0049] In an exemplary embodiment, S3 may be replaced by the following steps:

[0050] S31: Use a linear weighted method to fuse the crown edge feature and the distance feature to determine a fused feature; wherein the fused feature is: IF(r,c,r',c')=θ×EP(r,c,r',c')+(1-θ)×DP(r,c,r',c'); IF(r,c,r',c') is the fused feature value between any pixel (r,c) and the neighboring pixel (r',c') of the any pixel (r,c); θ is the weight; EP(r,c,r',c') is the crown edge feature value between any pixel (r,c) and the neighboring pixel (r',c') of the any pixel (r,c); DP(r,c,r',c') is the distance feature value between any pixel (r,c) and the neighboring pixel (r',c') of the any pixel (r,c).

[0051] In an exemplary embodiment, S4 may be replaced by the following steps:

[0052] S41: constructing edges between each pixel and the eight neighboring pixels corresponding to the pixel to form the connected graph; the weight of each edge in the connected graph is the fusion feature value of the fusion feature.

[0053] S42: Use the formula D(r',c')=min(D(r,c),IF(r,c,r',c')) to calculate the shortest path between each endpoint in the connected graph, and use the shortest path as the cutting line; wherein, D(r',c') is the shortest distance between an endpoint in the connected graph and a neighboring pixel (r',c') of any pixel (r,c); D(r,c) is the shortest distance between an endpoint in the connected graph and a pixel (r,c); IF(r,c,r',c') is the fusion feature value between any pixel (r,c) and the neighboring pixel (r',c') of the any pixel (r,c).

[0054] In an exemplary embodiment, S5 may be replaced by the following steps:

[0055] The central pixel of the area surrounded by the cutting line is used as the seed point, and the crown height model is divided into blocks using the regional growing method. The growing process is terminated when the cutting line is encountered. The point cloud in the block crown height model is extracted to achieve point cloud segmentation.

[0056] The key steps are shown in the following figure: Figure 2 shown.

[0057] Figure 3 The results of the forest airborne lidar point cloud clipping method of this application are shown in the following example. Figure 3 ①, ② and ③ are the cutting positions.

[0058] Compared with the designed cutting range, the present application can cut along the edge of the crown, maintain the integrity of the crown, and is similar to the shape of the designed range. It should be noted that the designed range is a square, and the designed endpoints are the four corner points of the square.

[0059] Through the above operations, the point cloud clipping result is obtained. As an example but not limitation, the result can be output as a point cloud file (*.las) for more convenient use.

[0060] This application automatically obtains data cutting lines at the edge of the tree crown to ensure that the tree crown near the cutting line is completely preserved, thereby providing complete block point cloud data of the tree crown for data storage, preview and processing.

[0061] This application uses the shortest path between endpoints that takes into account the crown edge and distance characteristics as the cutting line to crop the forest area LiDAR point cloud. The cutting line needs to avoid the inside of the crown and be as short as possible to achieve a balance between the integrity of the crown and the cropping range.

[0062] Based on the same inventive concept, the embodiment of the present application also provides a forest airborne laser radar point cloud clipping device for implementing the forest airborne laser radar point cloud clipping method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more forest airborne laser radar point cloud clipping device embodiments provided below can refer to the limitations of the forest airborne laser radar point cloud clipping method above, and will not be repeated here.

[0063] In an exemplary embodiment, a forest area airborne laser radar point cloud clipping device is provided, comprising:

[0064] The tree crown edge feature quantification module is used to quantify the tree crown edge features using mathematical morphological opening operations according to the tree crown height model; the tree crown height model is constructed based on the height information in the forest area airborne laser radar point cloud data.

[0065] The distance feature quantification module is used to quantify the distance feature according to the horizontal distance between the pixel in the tree crown height model and the neighboring pixel of the pixel.

[0066] The fusion feature determination module is used to fuse the crown edge feature and the distance feature to determine the fusion feature.

[0067] The cutting line determination module is used to construct a connected graph according to the fusion features, and use the shortest path between each endpoint in the connected graph as the cutting line.

[0068] The block processing module is used to perform block processing on the crown height model according to the cutting line, determine multiple block crown height models, and extract the forest area airborne laser radar point cloud data in the block crown height model.

[0069] The design of this application takes into account the fusion features of the crown edge and distance features, and calculates the onboard laser radar point cloud cutting line through path planning, so as to achieve the complete retention of the crown in the block point cloud and ensure that the size of the block point cloud is highly close to the design range.

[0070] This application quantifies the crown edge features based on mathematical morphology opening operations, combines the horizontal distance between the central pixel and the neighboring pixels to quantify the distance features, and fuses the two features through a linear weighted method to form a fusion feature that takes into account both the crown edge and the distance; constructs a connectivity graph based on the fusion features, and determines the cutting line by calculating the shortest path between the design endpoints; the cutting line is the shortest broken line along the crown edge between the two endpoints, which takes into account both the crown integrity and the block morphological similarity.

[0071] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store forest airborne laser radar point cloud clipping data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a forest airborne laser radar point cloud clipping method is implemented.

[0072] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the above method is implemented when the processor executes the computer program.

[0073] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0074] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0075] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ReadOnlyMemory, ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric random access memory (Ferroelectric RandomAccess Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0076] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0077] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0078] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0079] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for cutting point cloud of airborne laser radar in forest area, characterized in that: The forest area airborne laser radar point cloud clipping method comprises: According to the crown height model, the crown edge characteristics are quantified using mathematical morphological opening operations; the crown height model is constructed based on the height information in the forest airborne laser radar point cloud data; quantifying distance features according to horizontal distances between pixels in the tree crown height model and neighboring pixels of the pixel; Fusion of the crown edge feature and the distance feature to determine a fusion feature; Constructing a connectivity graph according to the fusion features, and using the shortest path between endpoints in the connectivity graph as a cutting line; The crown height model is divided into blocks according to the cutting line to determine a plurality of block crown height models, and the forest area airborne laser radar point cloud data in the block crown height models is extracted.

2. The method for cutting forest area airborne laser radar point cloud according to claim 1, characterized in that: Based on the crown height model, the crown edge features are quantified using mathematical morphological opening operations, which previously included: A cloth simulation filtering algorithm is used to extract ground point cloud data from the forest area airborne laser radar point cloud data; Constructing a terrain model according to the ground point cloud data; Normalizing the forest area airborne laser radar point cloud data, calculating the height difference between the forest area airborne laser radar point cloud data and the terrain model, and using the height difference as the point cloud elevation; The grid method is used to construct the tree crown height model according to the point cloud elevation corresponding to the normalized point cloud data.

3. The method for cutting forest area airborne laser radar point cloud according to claim 1, characterized in that: According to the crown height model, mathematical morphological opening operations are used to quantify the crown edge characteristics, including: Using mathematical morphology opening operation to smooth the interior of the crown of the crown height model, retaining the crown edge, and determining the crown height model after the opening operation; The crown edge feature is quantified according to the crown height model and the change value of the crown height model after the opening operation.

4. The method for cutting forest area airborne laser radar point cloud according to claim 1, characterized in that: According to the horizontal distance between the pixel in the tree crown height model and the neighboring pixel of the pixel, the distance feature is quantified, specifically including: According to the formula Wherein, DP(r, c, r′, c′) is the distance characteristic value between any pixel (r, c) and its neighboring pixel (r′, c′), r is the row number of the pixel, c is the column number of the pixel, r′ is the row number of the neighboring pixel, c′ is the column number of the neighboring pixel, and ps is the pixel size.

5. The method for cutting forest area airborne laser radar point cloud according to claim 1, characterized in that: The crown edge feature and the distance feature are fused to determine the fusion feature, which specifically includes: The crown edge feature and the distance feature are fused by a linear weighted method to determine a fused feature; wherein the fused feature is: IF(r, c, r′, c′)=θ×EP(r, c, r′, c′)+(1-θ)×DP(r, c, r′, c′); IF(r, c, r′, c′) is a fused feature value between any pixel (r, c) and a neighboring pixel (r′, c′) of the any pixel (r, c); θ is a weight; EP(r, c, r′, c′) is a crown edge feature value between any pixel (r, c) and a neighboring pixel (r′, c′) of the any pixel (r, c); DP(r, c, r′, c′) is a distance feature value between any pixel (r, c) and a neighboring pixel (r′, c′) of the any pixel (r, c).

6. The method for cutting forest area airborne laser radar point cloud according to claim 1, characterized in that: Constructing a connectivity graph according to the fusion features, and taking the shortest path between each endpoint in the connectivity graph as a cutting line, specifically includes: Construct edges between each pixel and the eight neighboring pixels corresponding to the pixel to form the connected graph; the weight of each edge in the connected graph is the fusion feature value of the fusion feature; The formula D(r′, c′)=min(D(r, c), IF(r, c, r′, c′)) is used to calculate the shortest path between each endpoint in the connected graph, and the shortest path is used as the cutting line; wherein D(r′, c′) is the shortest distance between an endpoint in the connected graph and a neighboring pixel (r′, c′) of any pixel (r, c); D(r, c) is the shortest distance between an endpoint and a pixel (r, c) in the connected graph; IF(r, c, r′, c′) is the fusion feature value between any pixel (r, c) and the neighboring pixel (r′, c′) of the any pixel (r, c).

7. A forest airborne laser radar point cloud clipping device, characterized in that: The forest area airborne laser radar point cloud clipping device comprises: A tree crown edge feature quantification module is used to quantify the tree crown edge features using mathematical morphological opening operations according to a tree crown height model; the tree crown height model is constructed based on the height information in the forest area airborne laser radar point cloud data; A distance feature quantification module, used for quantifying the distance feature according to the horizontal distance between the pixel in the tree crown height model and the neighboring pixel of the pixel; A fusion feature determination module, used to fuse the crown edge feature and the distance feature to determine a fusion feature; A cutting line determination module, used to construct a connectivity graph according to the fusion features, and use the shortest path between each endpoint in the connectivity graph as a cutting line; The block processing module is used to perform block processing on the crown height model according to the cutting line, determine multiple block crown height models, and extract the forest area airborne laser radar point cloud data in the block crown height model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the forest area airborne lidar point cloud clipping method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the forest area airborne laser radar point cloud clipping method described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the forest area airborne laser radar point cloud clipping method described in any one of claims 1 to 6 is implemented.

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