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A Hierarchical Airborne LiDAR Point Cloud Classification Method Using Geometry and Intensity Features

A technology of geometric features and classification methods, applied in the field of remote sensing science, can solve problems such as insufficient robustness of classifiers, variable intensity information, poor generalization ability, etc., achieve good airborne LiDAR point cloud classification results, realize fusion, and improve practicality sexual effect

Active Publication Date: 2021-01-29
NANJING UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, due to the influence of flight altitude, transmission power, atmospheric transmittance, etc., the intensity information recorded by airborne LiDAR is variable, resulting in the classifier obtained by direct training of supervised learning using geometric information and intensity information. Poor, difficult to migrate to airborne LiDAR data in other regions

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  • A Hierarchical Airborne LiDAR Point Cloud Classification Method Using Geometry and Intensity Features
  • A Hierarchical Airborne LiDAR Point Cloud Classification Method Using Geometry and Intensity Features
  • A Hierarchical Airborne LiDAR Point Cloud Classification Method Using Geometry and Intensity Features

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Embodiment 1

[0041] to combine figure 1 As shown, a hierarchical airborne LiDAR point cloud classification method using geometric and intensity features of the present invention first obtains the three-dimensional geometric information and intensity information of the ground surface through the airborne LiDAR, and according to the geometric information and intensity information for each LiDAR points to construct geometric features and intensity features; then use random forest classifier to process geometric features to obtain the supervised classification results of airborne LiDAR point clouds; extract ground objects from the supervised classification results, and use Gaussian mixture model to process ground object points Intensity features, to obtain the unsupervised classification results of ground object points in the airborne LiDAR point cloud; it is worth noting that, through hierarchical processing of the geometric information and intensity information of the airborne LiDAR point clo...

Embodiment 2

[0069] The content of this embodiment is basically the same as that of Embodiment 1, the difference is that in this embodiment, the fast point feature histogram is expressed as fpfh, the normal vector is expressed as N, the height is expressed as h, and the intensity feature is expressed as i; this embodiment adopts A kind of hierarchical airborne LiDAR point cloud classification method using geometric and intensity features of embodiment 1, the specific steps are as follows:

[0070] Step 1: First, use airborne LiDAR technology to obtain airborne LiDAR data (such as figure 2 , image 3 shown), it is worth noting that the airborne LiDAR data in this embodiment are provided by the International Society for Photogrammetry and Remote Sensing (http: / / www2.isprs.org / commissions / comm3 / wg4 / tests.html), and are provided by Leica The ALS50 system was taken in August 2008. The specific implementation of this example adopts the C++ programming language, which is implemented on the Ubu...

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Abstract

The invention discloses a hierarchical airborne LiDAR point cloud classification method using geometric and intensity features, and belongs to the field of remote sensing science and technology. The steps of the present invention are as follows: first, obtain the three-dimensional geometric information and intensity information of the ground surface through the airborne LiDAR, and construct geometric features and intensity features for each LiDAR point according to the geometric information and intensity information; then use the random forest classifier to process the geometric features, and obtain Supervised classification results; extract ground features from supervised classification results, and use Gaussian mixture model to process the intensity characteristics of ground feature points to obtain unsupervised classification results; then use heuristic rules to fuse supervised classification results with unsupervised classification results, Get the final classification result. The present invention overcomes the shortcomings of the airborne LiDAR point cloud supervision classifier caused by variable intensity information in the prior art, which is unstable and difficult to migrate, and can use the geometric information and intensity information of the airborne LiDAR point cloud in layers to obtain Better airborne LiDAR point cloud classification results.

Description

technical field [0001] The invention relates to the field of remote sensing science and technology, more specifically, to a hierarchical airborne LiDAR point cloud classification method using geometric and intensity features. Background technique [0002] LiDAR refers to Light Detection And Ranging (LiDAR for short), that is, laser radar. LiDAR uses the Global Positioning System (GPS for short) and the Inertial Measurement Unit (IMU for short) to directly obtain the three-dimensional geometric information of surface objects. The data measured by LiDAR is represented by discrete points, so LiDAR data is also called point cloud data; applying classification technology to interpret objects such as buildings, vegetation, cars, and ground in these original point clouds is an important aspect of urban form and ecological research. an indispensable part of the process. However, different types of ground features, such as buildings, vegetation, cars, ground, etc., often appear in ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/50G06F18/24323
Inventor 陈焱明刘小强杨康李满春程亮陈坚利马丹驯姜朋辉周琛姚梦汝肖一嘉施庆军
Owner NANJING UNIV