An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

摘要: Abstract Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large is significant bottleneck exploring full usage of the datasets. In current study, an accurate and computationally efficient algorithm was developed ground identification, called Filtering Clustering Algorithm (FICA). The method evaluated on Land, Vegetation, Ice Sensor (LVIS) datasets acquired over Central NY. FICA incorporates set multi-scale second derivative filters k-means clustering order to avoid detecting false peaks. tested five different land cover types (deciduous trees, coniferous shrub, grass area) showed more results when compared existing algorithms. More specifically, with Gaussian decomposition, RMSE by 2.82 m (5.29 m GD) deciduous plots, 3.25 m (4.57 m 2.63 m (2.83 m shrub 0.82 m (0.93 m 0.70 m (0.51 m plots areas. performance also relatively consistent under various slope canopy coverage (CC) conditions. addition, better computational efficiency methods. FICA’s major accuracy advantage result adopted signal processing procedures that concentrate local portions as opposed decomposition uses curve-fitting strategy applied entire signal. good candidate large-scale implementation future space-borne sensors.