海洋测绘2024，Vol.44Issue(4)：69-73,5.DOI:10.3969/j.issn.1671-3044.2024.04.015
基于α-shape与SSA-XGBoost算法的无人机点云孔洞修补
The repair of drone point cloud potholes based on the α-shape and SSA-XGBoost algorithms
摘要
Abstract
Aiming at the problems of difficult selection of core hyperparameters,difficult identification of point cloud hole repair range and low hole repair accuracy when using Extreme Gradient Boosting algorithm for UAV point cloud hole repair,this paper proposes a point cloud hole repair method based on Sparrow Search Algorithm to optimize the limit gradient lifting tree.Firstly,the α-shape algorithm is used to identify the holes in the point cloud.The position information of the holes in the point cloud and the surrounding point cloud is obtained and used as the input sample of the model.Using Sparrow search algorithm to optimize the core hyperparameters in the limit gradient lifting tree algorithm,the SSA-XGBoost point cloud hole repair model is constructed,and the model is applied to the repair of UAV point cloud holes.Finally,the prediction accuracy of SSA-XGBoost is compared with XGBoost and BP neural network.The experimental results show that the prediction of SSA-XGBoost model is more accurate than the other two algorithms,which has certain significance in point cloud hole repair.关键词
摄影测量/孔洞修补/α-shape算法/麻雀搜索算法/极限梯度提升树Key words
photogrammetry/hole repair/α-shape algorithm/sparrow search algorithm/extreme gradient boosting分类
天文与地球科学引用本文复制引用
宋晓辉,吕富强,窦彩英,唐诗华,党梦鑫..基于α-shape与SSA-XGBoost算法的无人机点云孔洞修补[J].海洋测绘,2024,44(4):69-73,5.基金项目
国家自然科学基金项目(42064003) （42064003）
广西自然科学基金项目(2022GXNSFBA035639) （2022GXNSFBA035639）
广西高校中青年教师科研基础能力提升项目(2023KY1199). （2023KY1199）
