Laser point cloud ground object automatic identification method fusing line channel orthoimage

Abstract

The invention discloses a line channel orthoimage fused laser point cloud ground object automatic identification method comprising the following steps: collecting line channel orthoimage data, and pre-processing the line channel orthoimage data; adopting a random forest algorithm to identify and classify the corrected orthoimage; the method comprises the following steps: collecting line channel laser point cloud data and tower standing book data, preprocessing the laser point cloud data, extracting ground point cloud and tower and power line point cloud, and taking the remaining laser point cloud data as target point cloud; and carrying out point cloud segmentation on a target point cloud by taking an orthoimage classification result as a constraint condition, and carrying out fine classification on a coarse classification result by adopting a random forest algorithm to obtain a point cloud recognition result. On the basis of various characteristics of the orthoimage, the two-dimensional advantage of the orthoimage and the three-dimensional advantage of the laser point cloud are combined, the point cloud classification precision is improved, point cloud classification parallel processing is added on the basis of orthoimage classification, and the point cloud classification processing efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of ultra-ultra-high voltage transmission lines, and more particularly to a method for automatic identification of laser point clouds and ground objects that integrates orthophoto images of line channels. Background technique [0002] According to the classification strategy of the classification method, the existing point cloud classification methods can be divided into two categories: hierarchical classification methods and simultaneous classification methods. The hierarchical classification method consists of two steps, filtering processing and object classification. In the filtering stage, the hierarchical classification method uses point cloud filtering algorithm to divide point cloud data into ground points and non-ground points. Commonly used point cloud filtering algorithms include mathematical morphology algorithm, slope filtering algorithm, and irregular triangulation progressive encryption. Algori...

AI Technical Summary

Problems solved by technology

[0011] (1) Although traditional algorithms such as unsupervised classification and supervised classification have great advantages in data processing efficiency, they are far from sufficient in classification accuracy, especially on orthophotos with centimeter-level accuracy. satisfactory

Although algorithms such as deep learning and machine learning have advantages in classification accuracy, the efficiency of data classification processing is not high.

[0012] (2) From the current process of image classification, it can be found that most studies focus on the classification and processing of data, while ignoring the quality of the data itself, and often the quality of data directly affects the results of image classification

[0013] At present, with the continuous improvement of UAV lidar technology, its application fields are also expanding, and it has good applications in power inspection, terrain mapping, 3D modeling and other fields. The point cloud data is unordered point cloud data, and the relevant information of the ground or specific objects cannot be obtained directly from the data. Data processing is required to extract effective information from the point cloud data.

The current point cloud data automatic recognition technology has certain shortcomings in the selection of point cloud classification accuracy and point cloud classification efficiency. If the accuracy of point cloud classification is to be improved, the efficiency of point cloud classification will be reduced. Efficiency will lose the accuracy of point cloud classification to a certain extent

Images