Lightweight Raw Echo Image Preprocessing for Long-Range Airborne Streak Tube Imaging LiDAR Using Adaptive Frequency-Domain Noise Suppression

Long-range airborne streak tube imaging lidar (ASTIL) raw echo images are degraded by atmospheric speckle, detector noise, and weak-return fluctuations, which can bias centroid localization before range calculation. This study presents a lightweight preprocessing method combining row–column geometry-aware echo region pre-classification with frequency-domain histogram-based adaptive suppression. Candidate regions are extracted from normalized streak images, classified by row–column morphology, filtered using local magnitude-spectrum percentile thresholds, and fused with a background-constrained weighted strategy. Simulated echo images, simulated point clouds, and 6 km airborne data were used for validation. In selected building roof control regions, the mean elevation root mean square error (RMSE) decreased from 0.34 m to 0.29 m, the mean absolute error (MAE) from 0.30 m to 0.26 m, and the mean roof elevation standard deviation from 0.19 m to 0.15 m, corresponding to an approximately 21% reduction in roof-level point cloud thickness. The results show that preprocessing before centroid extraction can improve roof-level vertical consistency without neural-network training or complex point cloud post-processing.