Estimation of Single Tree Height Based on Improved K-Means Method for Unmanned Aerial Vehicle Point Cloud Data

The technology of identifying and measuring parameters of tree plays an important role in city 3D modeling and statistical analysis. This article focuses on tree height estimation based on UAV point cloud data, designs a method to achieve the separation of single trees from sparse tree clusters and tree-ground separation for accurate tree height estimation. The key of the algorithm is to obtain the single tree point and its corresponding ground point. To obtain accurate single tree points, this paper uses an improved k-means method. The highlight of the algorithm lies in the design of a validation algorithm based on tree structure to improve k-means method, making single tree separation results more accurate. After comparing the experimental results with actual measurement data, it is believed that this method is effective in extracting tree height.