Abstract: [Purposes] This paper aims to address the technical bottlenecks of automatic classification in high-precision DEM production under complex terrain,including suboptimal data quality,unreasonable filtering effects,and excessive manual editing workload. [Methods] A dual-feature coupled point cloud classification optimization (DFC-PCACO)algorithm is proposed,which takes terrain geometric and point cloud spatial distribution features as dual core constraints.Adopting the process of “terrain classification-differentiated processing-closed-loop optimization”,it integrates multi-dimensional sub-features under the dual-feature framework to realize the secondary optimization of classification results. [Findings] Validated by experimental data from flat and densely forested mountainous areas,the DFC-PCACO algorithm significantly improves point cloud classification accuracy compared with automatic classification： total error is 2.37% in flat areas,11.92% in densely forested mountainous areas,and manual editing efficiency is increased by 35%～40%. [Conclusions] The DFC-PCACO algorithm provides an efficient technical solution for large-scale high-precision DEM production in complex terrain areas．

Key words: airborne LiDAR, point cloud classification, DFC-PCACO algorithm, complex terrain, DEM generation