河南城建学院学报2024，Vol.33Issue(4)：108-113,6.DOI:10.14140/j.cnki.hncjxb.2024.04.014
基于双通道残差网络的机载LiDAR点云数据分类
Classification of airborne LiDAR point cloud data based on dual channel residual network
摘要
Abstract
To improve the insufficient information circulation in the classification of airborne LiDAR point cloud data by traditional residual network,an airborne LiDAR point cloud data classification model,namely DP-Res-Net,based on dual-channel residual network is proposed.DP-ResNet adopts an encoding-decoding structure.In the encoding stage,two different forms of dual channel residual structure and parameter-free aggregation opera-tor are mainly combined.This can not only strengthen the circulation of network information,but also reduce network parameters.The decoding stage is done using traditional inverse distance weighting and 1×1 convo-lution.To verify the classification performance of the DP-ResNet model,classification experiments were per-formed on the GML DataSetA dataset.The results show that compared with the benchmark network Closerlook,the OA and AvgF1 of DP-ResNet model are improved by 6.25%and 15.45%respectively,indicating better classification performance.Compared with other models,DP-ResNet also has strong competitiveness.关键词
机载激光雷达/点云数据/残差网络/DP-ResNet 模型Key words
airborne laser radar/point cloud data/residual network/DP-ResNet model分类
天文与地球科学引用本文复制引用
肖根..基于双通道残差网络的机载LiDAR点云数据分类[J].河南城建学院学报,2024,33(4):108-113,6.基金项目
国家自然科学基金项目(42374147) （42374147）
智能地理信息处理湖北省重点实验室开放研究项目(KLIGIP-2023-C02) （KLIGIP-2023-C02）
