A Novel Noise Filtering Model for Photon-Counting Laser Altimeter Data

The new generation of Ice, Cloud, and land Elevation Satellite (ICESat-2) which utilizes photon-counting laser detectors is scheduled for launch in 2017. This upcoming mission will provide data to assess changes of ice sheet elevation and mass, as well as the time-varying volume of sea ice. However, the next-generation ICESat sensor also presents new data processing challenges due to the high number of false returns present in the resultant point cloud that are mainly caused by the high sensitivity of the photon detector to solar returns. In this letter, we propose a novel noise filter for single photon laser altimeter data utilizing a Bayesian decision theory. We applied our algorithm to the Multiple Altimeter Beam Experimental Lidar (MABEL) data sets and compared the filtered estimate of ground to coincident high resolution airborne LiDAR data. The results show that the proposed algorithm differentiates between noise and ground surface returns effectively with 6-m root-mean-square error (RMSE) for the MABEL green channel lasers and 4-m RMSE for the near-infrared channel lasers. The Bayesian approach also outperformed a commonly applied point density-based algorithm, the modified density-based spatial clustering of applications with noise, particularly in areas of steep terrain.