A main direction-based noise removal algorithm for ICESat-2 photon-counting LiDAR data

Abstract

A new generation of space-borne LiDAR (Light Detection And Ranging) satellite ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) equipped with ATLAS (Advanced Topographic Laser Altimeter System) can perform earth observation. The main problem is to remove the noise photons from the data. The study proposes a main direction-based noise removal algorithm based on three sets of photon-counting LiDAR data. In order to extract the main direction, features in the spatial neighborhood (k) of photons are calculated, most of the initial noise is removed according to the angle between the main direction of photons and the along-track distance direction. Qualitative and quantitative evaluations are employed to validate the proposed algorithm. The obtained results and the performed analysis reveal that the proposed algorithm can process day and night data with different signal-to-noise ratios, while the accuracy of various surface types exceeds 96%. More specifically, the accuracy of the proposed algorithm for night data can reach 97.43%. Based on quantitative evaluations using SPL (Single photon LiDAR), MATLAS, and airborne LiDAR data, the average R, P, and F values are 0.951, 0.959, and 0.954, respectively. Meanwhile, the result of the proposed algorithm is compatible with the ATL03 photons with low, medium, and high confidence, and its accuracy is superior to ATL08 products. The proposed algorithm had fewer parameters and significantly outperformed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the improved local statistical distance algorithm. This algorithm is expected to provide a reference for subsequent photon-counting LiDAR data processing.