Point cloud data obtained via spaceborne photon-counting lidar contain substantial background noise that significantly affects the high-precision extraction of surface elevation information. To address the limitations of existing algorithms in high-noise and undulating terrain areas, this paper proposes a single-photon point cloud denoising algorithm based on improved local distance statistics. This method employs a multilevel denoising strategy that progresses from coarse to fine stages. Specifically, in the fine denoising stage, an elliptical filtering kernel that better matches the distribution characteristics of the scene's photon point cloud is utilized. Additionally, a Gaussian bimodal model is used to fit the distance histogram and determine the optimal denoising threshold. Experiments conducted with ICESat-2 data demonstrate that the proposed method achieves an average precision of 97.11%, average F₁ score of 92.57%, and average accuracy rate of 91.66%, which is approximately 3.35 percentage point higher than those of existing algorithms. These findings indicate that the proposed method exhibits superior denoising performance in high-noise and undulating terrain areas. Therefore, this study provides a valuable reference for denoising photon point clouds in complex scenes.