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LiDAR point cloud data individual tree extraction method based on spectral clustering algorithm

A spectral clustering algorithm and point cloud data technology, which is applied in the field of LiDAR point cloud data single-tree extraction based on spectral clustering algorithm, can solve the problem that the segmentation accuracy cannot be guaranteed, and achieve strong practical value, good surface and high-efficiency single-tree recognition effect

Active Publication Date: 2019-11-08
RES INST OF FOREST RESOURCE INFORMATION TECHN CHINESE ACADEMY OF FORESTRY
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Problems solved by technology

However, these algorithms need to make assumptions about the number of individual trees, and at the same time have certain requirements for the distribution of data, and the segmentation accuracy cannot be guaranteed

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  • LiDAR point cloud data individual tree extraction method based on spectral clustering algorithm
  • LiDAR point cloud data individual tree extraction method based on spectral clustering algorithm
  • LiDAR point cloud data individual tree extraction method based on spectral clustering algorithm

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Embodiment

[0024] A single tree extraction method of LiDAR point cloud data based on spectral clustering algorithm, the implementation method is as follows figure 1 As shown, including the following operations:

[0025] Step 1. Normalize the height information of the airborne LiDAR point cloud data and establish CHM;

[0026] Step 2, voxelize the normalized point cloud in step 1 using the mean shift algorithm;

[0027] Mean shift is a clustering algorithm that groups points by iteratively shifting each point towards a point shifted from the mean. It does not require assumptions about the data distribution or the number of clusters, and is a fast and efficient classifier. The present invention uses the mean shift method to complete the voxelization process, each voxel is represented by the clustering result of the mean shift, the position of the voxel is determined by the coordinate center of the cluster point, and the weight of the voxel is equal to the number of points therein.

[00...

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Abstract

The invention aims to provide an LiDAR point cloud data individual tree extraction method based on a spectral clustering algorithm, which specifically comprises the steps of normalizing height information of LiDAR point cloud data, and performing voxelization by using a mean shift clustering algorithm; constructing a similarity graph in the voxel space based on a Gaussian similarity function; calculating feature values and feature vectors of the similarity graph by using a method, and determining a segmentation individual tree number k by using a feature value interval; and taking the featurevectors corresponding to the first k minimum feature values as columns to construct a feature vector matrix, performing k-means clustering on normalized row elements of the feature vector matrix in afeature space, and mapping a segmentation result back to the LiDAR point cloud to obtain single-tree clustering, thereby realizing single-tree segmentation of the point cloud. The method provided by the invention not only can carry out effective individual tree segmentation on the sample plot scale, but also can provide a stable segmentation result for the regional scale, and has a very high practical value.

Description

technical field [0001] The invention relates to a laser radar point cloud data processing technology, in particular to a method for single tree extraction of LiDAR point cloud data based on a spectral clustering algorithm. Background technique [0002] LiDAR (Light Detecting and Ranging, LiDAR) technology is one of the most revolutionary achievements in the field of remote sensing in the past 20 years. As an active remote sensing technology, airborne lidar can obtain the spatial structure characteristics of forests in a large range, and has outstanding advantages in high-precision extraction of key forest parameters. [0003] At present, there are many algorithms for extracting single trees from airborne LiDAR point cloud data, which can be mainly divided into surface model methods based on Canopy Height Models (CHM) and 3D methods using point cloud information. 3D methods can further Divided into clustering methods and voxel-based methods. The clustering method is an effe...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/2411
Inventor 庞勇王伟伟
Owner RES INST OF FOREST RESOURCE INFORMATION TECHN CHINESE ACADEMY OF FORESTRY
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