A method, device, equipment, medium and product for cropping airborne lidar point clouds in forest areas

Abstract

The present application discloses a method, device, equipment, medium and product for cropping airborne lidar point clouds in forest areas, relating to the field of processing airborne lidar point cloud data in forest areas. The method includes quantifying the canopy edge features by using mathematical morphological opening operation according to the canopy height model, where the canopy height model is constructed based on the height information in the airborne lidar point cloud data in forest areas; quantifying the distance features according to the horizontal distance between the pixels in the canopy height model and their neighboring pixels; fusing the canopy edge features and the distance features to determine the fused features; constructing a connectivity graph according to the fused features, and taking the shortest path between the endpoints in the connectivity graph as the cutting line; performing a block processing on the canopy height model according to the cutting line to determine multiple segmented canopy height models, and extracting the airborne lidar point cloud data in the segmented canopy height models. The present application improves the cropping accuracy of the lidar point cloud data of the canopy in the forest scene.

Description

Technical Field

[0001] The present application relates to the field of processing lidar point cloud data in forest areas, and particularly to a method, device, equipment, medium and product for cropping lidar point cloud in forest areas. Background Technique

[0002] Compared with optical remote sensing and photogrammetry technologies, lidar can penetrate the forest canopy and obtain detailed three-dimensional structure data of the forest interior and understory terrain, 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 forest three-dimensional environment in the form of a large number of point clouds. After collecting lidar point cloud data, it is necessary to crop the point cloud into multiple small-sized data for easy data storage, preview and processing. Among them, point cloud cropping can be achieved through various methods, including manual cropping, grid cropping, and attribute cropping; for manual cropping, operators use 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 lidar point cloud attributes (such as scanning angle and data acquisition time). However, these automated methods cannot maintain the integrity of ground objects, and ground 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 ground objects to maintain the integrity of ground objects in the segmented data. However, this method is more suitable for urban scenes. In forest scenes, the tree canopies are closely adjacent, and it is difficult to crop the point cloud based on the gaps between the tree canopies. The cropped segmented point clouds contain incomplete tree canopies, which directly affects the calculation accuracy of lidar point cloud forest structure feature parameters, and further limits the estimation accuracy of parameters such as biomass, volume, and carbon storage. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device, equipment, medium and product for cropping lidar point cloud in forest areas to solve the problem of low cropping accuracy of lidar point cloud data of tree canopies in forest scenes.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In a first aspect, the present application provides a method for cropping lidar point cloud in forest areas, including:

[0007] Quantifying the canopy edge features by using mathematical morphological opening operation according to the canopy height model; the canopy height model is constructed based on the height information in the lidar point cloud data in forest areas;

[0008] Quantifying the distance features according to the horizontal distance between the pixels in the canopy height model and the neighboring pixels of the pixels;

[0009] Fuse the crown edge feature and the distance feature to determine a fused feature;

[0010] Construct a connected graph according to the fused feature, and use the shortest path between each endpoint in the connected graph as a cutting line;

[0011] Perform a block processing on the crown height model according to the cutting line, determine a plurality of block crown height models, and extract the forest area airborne lidar point cloud data in the block crown height models.

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

[0013] A crown edge feature quantization module, configured to quantize the crown edge feature by using a mathematical morphological opening operation according to a crown height model; the crown height model is constructed based on height information in forest area airborne lidar point cloud data;

[0014] A distance feature quantization module, configured to quantize the distance feature according to the horizontal distance between a pixel in the crown height model and the neighboring pixels of the pixel;

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

[0016] A cutting line determination module, configured to construct a connected graph according to the fused feature, and use the shortest path between each endpoint in the connected graph as a cutting line;

[0017] A block processing module, configured to perform a block processing on the crown height model according to the cutting line, determine a plurality of block crown height models, and extract the forest area airborne lidar point cloud data in the block crown height models.

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

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the forest area airborne lidar point cloud clipping method described in any one of the above.

[0020] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the forest area airborne lidar point cloud clipping method described in any one of the above.

[0021] According to the specific embodiments provided by the present application, the following technical effects are disclosed: By determining the fusion feature that takes into account both the crown edge feature and the distance feature, the shortest path between the endpoints in the connected graph constructed by the fusion feature is obtained in the path planning manner as the cutting line, that is, the cutting line is the shortest broken line along the crown edge between two endpoints, so as to realize the complete retention of the crown in the segmented point cloud and ensure that the size of the segmented point cloud is close to the design range, improving the cutting 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 technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a flowchart of a method for cropping airborne lidar point cloud in a forest area provided by an embodiment of the present application;

[0024] Figure 2 It is an effect diagram of key steps in an embodiment of the present application;

[0025] Figure 3 It is a comparison diagram between the design range of point cloud cropping and the cropping result of the present application; among them, Figure 3 (a) in it is a schematic diagram of the design range; Figure 3 (b) in it is a schematic diagram of the cropping result of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0027] To make the above objects, features and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

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

[0029] S1: According to the canopy height model, use mathematical morphological opening operation to quantify the canopy edge features; the canopy height model is constructed based on the height information in the airborne lidar point cloud data of the forest area.

[0030] S2: Quantify the distance features according to the horizontal distance between the pixels in the canopy height model and the neighboring pixels of the pixel.

[0031] S3: Fuse the canopy edge features and the distance features to determine the fusion features.

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

[0033] S5: Perform block processing on the canopy height model according to the cutting line to determine multiple block canopy height models, and extract the airborne lidar point cloud data in the block canopy height models.

[0034] In an exemplary embodiment, before S1, it further includes:

[0035] Use the cloth simulation filtering algorithm to extract the ground point cloud data in the airborne lidar point cloud data of the forest area.

[0036] Construct a terrain model according to the ground point cloud data.

[0037] Perform normalization processing on the airborne lidar point cloud data of the forest area, calculate the height difference between the airborne lidar point cloud data of the forest area and the terrain model, and use the height difference as the point cloud elevation;

[0038] Use the grid method to construct a canopy height model according to the point cloud elevation corresponding to the normalized point cloud data, and the pixel value in the canopy height model is the elevation value of the highest point in the corresponding grid.

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

[0040] S11: Use mathematical morphological opening operation to smooth the interior of the canopy of the canopy height model, retain the canopy edge, and determine the canopy height model after the opening operation.

[0041] S12: Quantify the canopy edge features according to the change value between the canopy height model and the canopy height model after the opening operation.

[0042] Furthermore, the mathematical morphological opening operation can smooth the interior of the canopy and retain the canopy edge. The canopy edge features are quantified by calculating the change value before and after the opening operation of the canopy height model, as follows.

[0043]

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

[0045] EP(r, c, r′, c′) = E(r, c) + E(r′, c′)

[0046] Wherein, EP(r, c, r', c') represents the canopy edge feature value between the pixel (r, c) and its neighboring pixel (r', c').

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

[0048] S21: According to the formula Wherein, DP(r, c, r', c') is the distance feature value between any pixel (r, c) and the neighboring pixel (r', c') of this any 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, and c' is the column number of the neighboring pixel, and ps is the pixel size.

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

[0050] S31: The canopy edge feature and the distance feature are fused by the linear weighting method to determine the fusion feature; wherein, the fusion 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 fusion feature value between any pixel (r, c) and the neighboring pixel (r', c') of this any pixel (r, c), θ is the weight, EP(r, c, r', c') is the canopy edge feature value between any pixel (r, c) and the neighboring pixel (r', c') of this 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 this any pixel (r, c).

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

[0052] S41: Edges are constructed between each pixel and its corresponding eight neighboring pixels 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: Calculate the shortest path between each endpoint in the connected graph using the formula D(r', c') = min(D(r, c), IF(r, c, r', c')), and use the shortest path as the cutting line; where D(r', c') is the shortest distance between the endpoint in the connected graph and the neighboring pixel (r', c') of any pixel (r, c); D(r, c) is the shortest distance between the endpoint in the connected graph and the pixel (r, c); and IF(r, c, r', c') is the fusion eigenvalue between any pixel (r, c) and the neighboring pixel (r', c') of this pixel (r, c).

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

[0055] Taking the central pixel of the area surrounded by the cutting line as the seed point, use the region growing method to segment the canopy height model, and the growth process terminates when encountering the cutting line. Extract the point cloud in the segmented canopy height model to achieve point cloud segmentation.

[0056] The effect diagrams of the key steps are as Figure 2 shown.

[0057] Figure 3 Fig. shows an example of the result of the method for cropping airborne lidar point clouds in forest areas of the present application, Figure 3 where ①, ②, and ③ in are the cropping positions.

[0058] Compared with the designed cropping range, the present application can cut along the canopy edge, maintaining the integrity of the canopy, and at the same time being similar in shape to 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 cropping result is obtained. By way of example and not limitation, the result can be output as a point cloud file (*.las) for more convenient use.

[0060] The present application automatically obtains the data cutting line at the canopy edge, ensuring that the canopy near the cutting line is completely retained, thereby providing block point cloud data with complete canopies for data storage, preview, and processing.

[0061] The present application uses the shortest path that takes into account both the canopy edge and distance features between endpoints as the cutting line to crop the lidar point clouds in forest areas. The cutting line needs to avoid the inside of the canopy and be as short as possible in length to achieve a balance between canopy integrity and the cropping range.

[0062] Based on the same inventive concept, an embodiment of the present application further provides a forest area airborne lidar point cloud clipping device for implementing the forest area airborne lidar point cloud clipping method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the forest area airborne lidar point cloud clipping device provided below can refer to the limitations on the forest area airborne lidar point cloud clipping method in the above text, and will not be repeated here.

[0063] In an exemplary embodiment, a forest area airborne lidar point cloud clipping device is provided, including:

[0064] A canopy edge feature quantification module, configured to quantify the canopy edge feature by using mathematical morphological opening operation according to the canopy height model; the canopy height model is constructed based on the height information in the forest area airborne lidar point cloud data.

[0065] A distance feature quantification module, configured to quantify the distance feature according to the horizontal distance between the pixel in the canopy height model and the neighboring pixels of the pixel.

[0066] A fusion feature determination module, configured to fuse the canopy edge feature and the distance feature to determine the fusion feature.

[0067] A cutting line determination module, configured to construct a connected graph according to the fusion feature, and use the shortest path between the end points in the connected graph as the cutting line.

[0068] A block processing module, configured to perform block processing on the canopy height model according to the cutting line, determine a plurality of block canopy height models, and extract the forest area airborne lidar point cloud data in the block canopy height models.

[0069] The design of the present application takes into account the fusion feature of the canopy edge and the distance feature, and calculates the lidar point cloud cutting line through the path planning method, realizes the complete retention of the canopy in the segmented point cloud, and ensures that the size of the segmented point cloud is close to the design range.

[0070] The present application quantifies the canopy edge feature based on the mathematical morphological opening operation, combines the horizontal distance between the central pixel and the neighboring pixels to quantify the distance feature, fuses the two features by the linear weighting method to form a fusion feature that takes into account the canopy edge and the distance; constructs a connected graph based on the fusion feature, and determines the cutting line by calculating the shortest path between the designed end points; the cutting line is the shortest broken line along the canopy edge between two end points, which takes into account the canopy integrity and the similarity of the segmented morphology.

[0071] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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 area airborne lidar point cloud cropping data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for cropping forest area airborne lidar point clouds.

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

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

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

[0075] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAMs), magnetoresistive random access memories (MRAMs), ferroelectric random access memories (FRAMs), phase change memories (PCMs), graphene memories, etc. Volatile memories can include random access memories (RAMs) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0076] In this application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located and obtaining the authorization given by the owner of the corresponding device.

[0077] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0078] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope described in this specification.

[0079] In this text, specific examples are used to illustrate the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application. At the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to 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 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 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); 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 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.

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: A linear weighted method is used 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 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. A forest airborne laser radar point cloud clipping device, characterized in that: The forest area airborne laser radar point cloud clipping device adopts the forest area airborne laser radar point cloud clipping method according to any one of claims 1 to 5, and 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.

7. 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 5.

8. 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 5 is implemented.

9. 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 5 is implemented.

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