Aiming at the problem of misclassification of buildings and adjacent vegetation in urban point cloud of airborne LiDAR,a method of building point cloud classification in airborne LiDAR that improved supervoxel segmentation algorithm based on W-OPTICS was proposed.By integrating the differences in point cloud features within voxels and their density characteristics to construct adaptive weights,the W-OPTICS algorithm is proposed to offset the initial seed voxels.This optimization allows the seed voxels to possess both spatial local similarity and high-density characteristics,addressing the issue of random seed point selection at the junctions of buildings and adjacent land classes leading to cross-boundary misclassification.Combining the Principal Component Analysis(PCA)algorithm to aggregate key features,building classification is then achieved through the Support Vector Machine(SVM).The experimental results show that:For the Vaihingen dataset,the completeness rate,accuracy rate and detection quality of the proposed method reached 95.0％,94.0％and 89.6％,respectively,which was 1.7％,4.2％and 5.3％higher than that of the original supervoxel method.The misclassification of buildings and adjacent land classes was significantly reduced,and the omissionis improved to some extent.For actual measured point cloud data,the method described in this paper surpasses classical supervoxel segmentation methods and TerraSolid automatic classification results in all accuracy metrics.This validates the stability and robustness of the algorithm proposed in this paper for building classification.
关键词
点云/建筑物/OPTICS/种子点优化/分类策略
Key words
point cloud/buildings/OPTICS/seed point optimization/classification strategy
维普
万方数据