A Process-Oriented Method for Rapid Acquisition of Canopy Height Model From RGB Point Cloud in Semiarid Region

This study addressed the problems of difficult parameter selection of ground point filtering algorithm, slow speed, and susceptibility to wrong classification points of the traditional interpolation algorithm in extracting canopy height model from dense point cloud produced by structure from motion. Finding the true ground is vital for canopy height estimation using unmanned aerial vehicle red, green, and blue point cloud, especially for semiarid area where cloud points of short vegetation like shrubs and dwarf trees generally mixed up with the ground points. This article proposes a ground point extraction strategy that combines the advantages of structural and spectral filtering. Specifically, spectral filtering simplifies the threshold selection for structural filtering, whereas structural filtering removes the outliers from spectral filtering. Considering the misclassified points in the ground filtering algorithm, a fast, nonparametric ground point interpolation algorithm was used to suppress the wrong classification points. This novel algorithm is based on the basic idea of least square quadratic fitting and prediction of terrain profile (PF). The quantitative results show that compared to inverse distance weighting (IDW), radial basis function (RBF), and ordinary kriging (OK), it takes less time (PF: 122 s, IDW: 518 s, RBF: 1374 s, OK: 1129 s), and has lower RMSE (PF: 0.301 m, IDW: 0.549 m, RBF: 0.903 m, OK: 0.427 m).