A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data
A novel algorithm based on the clustering method of ordering points to identify the clustered structure (OPTICS) was proposed to remove noise photons in the ICESat-2 data and shows that the algorithm works well in distinguishing the signal and noise photons as indicated by high $F$ values.
Abstract
The generation of a Digital Elevation Model (DEM) using LiDAR technology is a key tool in \nhydraulic modelling for water resource management. The main objective of this research was to \ncompare the effect of combinations of flight pathways and filtering techniques on the generation \nof a DEM and the response of a hydraulic model in a section of the stream Granada in the \nmunicipality of Galapa (Atlántico). For this purpose, a DEM was generated in an area located in \nthe basin of the Granada stream with dense vegetation applying different filtering techniques to \nLiDAR surveys with different flight patterns. The DEM sections generated for each combination \nof flight form and filtering techniques using error metrics were then evaluated. Finally, we \nevaluated the response of hydraulic modelling in HEC-RAS for different precipitation events \nbased on the information obtained from IDEAM. The results suggest that the unification of flight \ntechniques, carried by nearby filtering techniques, produced more consistent DEM