Object-oriented airborne laser radar point cloud filtering method

Abstract

The invention discloses an object-oriented airborne laser radar point cloud filtering method. The method comprises the following steps: loading airborne laser radar point cloud data; identifying and removing a gross error point in airborne laser radar point cloud; performing smooth surface growth based airborne laser radar point cloud segmentation to obtain an object; analyzing a multi-echo ratio feature of the object to identify a potential earth object, and removing the earth object and a laser radar point contained in the earth object; extracting a feature point of the object, and performing subsequent operation by utilizing the feature point instead of an original laser radar point contained in the object; performing feature point based object type judgment, and updating the type of the original laser radar point contained in the object. According to the method, a complete object-oriented airborne laser radar point cloud filtering technical process is formed, so that the airborne laser radar point cloud filtering precision is effectively improved; and the method has wide applicability for various geomorphic types such as a mountainous area, an urban area and the like.

Description

technical field [0001] The invention relates to an airborne laser radar point cloud filtering method, in particular to an object-oriented airborne laser radar point cloud filtering method, and belongs to the technical field of laser radar point cloud data information extraction. Background technique [0002] During the operation of airborne lidar equipment, the laser scanning process is blind, that is, the laser pulse may hit the ground, or it may hit buildings, bridges, power lines, lighthouses, vehicles and other artificial features or vegetation. Therefore, the acquired airborne lidar point cloud data contains both ground points and object points. Point cloud classification is the basis for subsequent applications. In the process of engineering application, the airborne lidar point cloud data classification often adopts the idea of ​​hierarchical classification, that is, firstly, the ground points and the ground object points (non-ground points) in the point cloud data a...

AI Technical Summary

Problems solved by technology

However, the triangulation network progressive encryption filtering method also has a significant weakness. For hilly and mountainous areas, it is easy to mistake ground points at steep terrain changes for non-ground points, such as figure 2 shown; for urban areas, it is easy to mistake the feature points below the vehicle for ground points

[0006] The above problems lead to the existing triangular network pr

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