Automatic terrain sundry identification method

Abstract

The invention discloses an automatic terrain sundry identification method, and relates to a terrain sundry identification system based on deep learning. The system performs a series of processing on low-precision three-dimensional terrain point cloud data acquired based on an unmanned aerial vehicle aerial photography technology. To solve the problem that low-precision terrain feature informationis limited, geometrical features and color features of terrain point clouds are reconstructed, a multi-attribute descriptor is designed, and local information of each point in the input terrain pointclouds is described; then, the terrain point clouds are aggregated into a plurality of super points by using a multi-attribute descriptor in unsupervised classification, so that computation complexityis reduced; a directed attribute graph is defined and the super points are combined into a graph structure, namely a super point graph; and finally, the nodes of the super-point graph are classifed by using the proposed LSTM algorithm based on depth graph convolution. The system can finally perform semantic segmentation on the sundries in the terrain point cloud, and the sundries in the terrain can be displayed through different colors. Through further research and analysis, basic technical support can be provided for topographic analysis and the like.

Description

technical field [0001] The invention belongs to the application of a point cloud semantic segmentation method in large-scale and low-precision outdoor terrain point cloud segmentation, and mainly relates to a method for automatic recognition of terrain debris aimed at identifying large-scale terrain debris. Background technique [0002] With the rapid development of the engineering construction industry, the demand for topographic survey and geological exploration is constantly changing, and more and more attention is paid to the efficient and accurate survey and measurement of topography and geomorphology. On the other hand, UAV remote sensing technology has low processing cost and high degree of automation in the process of terrain collection, making UAV an important tool for current terrain collection. [0003] The digital surface model DSM (Digital Surface Model) obtained by UAV remote sensing includes surface buildings, bridges, trees and other terrain debris other than...

AI Technical Summary

Problems solved by technology

[0011] In view of the above-mentioned problems and defects in the prior art, the purpose of the present invention is to provide a method for automatic recognition of terrain debris, to solve the problem of poor classification effect of large-scale low-precision terrain point cloud debris recognition of local sparse point cloud structures, and difficult Identify problems such as fine-grained pattern recognition and weak generalization ability in complex scenes

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