An Adaptive Ellipsoid Searching Filter for Airborne Single-Photon Lidar

Recent light detection and ranging (lidar) systems using photon-counting technology are able to collect data with significantly higher efficiency compared with the current commercially available linear-mode lidar systems. However, the high quantum sensitivity of single-photon lidar (SPL) systems results in noisy point clouds due to the influence of solar noise and dark count returns. Therefore, an effective noise removal algorithm is required to interpret SPL data. The uneven distribution of noise returns and the removal of noise close to signal returns are two significant challenges for SPL filtering. In this letter, a novel adaptive ellipsoid searching (AES) method is proposed. The AES uses a spherical noise density estimation model and a morphing ellipsoid determined by local principal components. The proposed method was tested on Sigma Space high-resolution quantum lidar system (HRQLS) SPL data sets and the results were compared with voxel-based filtering of the same data. Independent comparisons of each filtered result with coincident linear-mode airborne lidar data were also undertaken. We find that the root mean square error of the AES results on solid planes is 0.09 versus 0.11 m for voxel-based, 0.12 versus 0.14 m for bare ground, and 2.07 versus 2.55 m for vegetation canopy. We also used manually selected solid planar surfaces as a reference and find that the proposed method successfully removed twice as many noise points as the voxel-based method.