A Density-Guided and Residual-Feedback Denoising Method for Building Height Estimation from ICESat-2/ATLAS Data

Building height is a critical parameter for urban analysis, yet accurately estimating it from ICESat-2 photon-counting LiDAR data remains challenging due to pervasive noise photons and uneven noise distribution. To address the limitations of fixed-threshold denoising methods and improve adaptability across varying density conditions, this study proposes a dual-stage denoising framework for ICESat-2 ATL03 photon data. In the first stage, local photon densities are estimated within a reliable radius, log-transformed, and stratified into multiple levels. Adaptive thresholds are then applied at each level to suppress low-density noise while minimizing over-filtering in sparse regions. In the second stage, residual feedback-driven adaptive fitting strategy is applied along the ground track, where polynomial fitting was performed in sliding windows, with the window size dynamically adjusted based on residuals to refine local structures and eliminate outliers. The experiment was conducted in South Holland and Friesland, across 84 ICESat-2 tracks, where quantitative evaluations under varying day/night and beam conditions confirmed the effectiveness of the proposed framework. For denoising, the proposed method achieved high denoising accuracy, with F1-scores exceeding 0.97 in most cases, outperforming previous methods. Furthermore, building heights derived from footprint buffering and elevation differencing are validated against airborne LiDAR, yielding coefficient of determination (R2) values of 0.7235 and 0.9487 for the two regions, with root mean square error (RMSE) values of 1.5045 m and 1.8849 m, respectively. This study confirms the effectiveness and robustness of the proposed dual-stage framework, demonstrating its strong capability for both noise suppression in ICESat-2 ATL03 photon data and the subsequent accurate estimation of building heights.