Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordin...Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordingly,a new denoising method is presented to extract signal photons adaptively.The method includes two steps.First,the local neighborhood radius is calculated according to photons’density,then thefirst-step denoising process is completed via photons’curvature feature based on KNN search and covariance matrix.Second,the local photonfiltering direction and threshold are obtained based on thefirst-step denoising results by RANSAC and elevation frequency histogram,and the local dense noise photons that thefirst-step cannot be identified are further eliminated.The following results are drawn:(1)experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94,and the accuracy is effectively improves with the number of denoising times;(2)experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies.Overall,the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons,and the denoising results can provide more reasonable and reliable data for sustainable urban development.展开更多
基金supported by the National Key R&D Program of China under[grant number 2021YFF0704600]the National Natural Science Foundation of China under[grant number 42171352,42271365,U22A20566]the High-Level Talent Aggregation Project in Hunan Province,China-Innovation Team under[grant number 2019RS1060].
文摘Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordingly,a new denoising method is presented to extract signal photons adaptively.The method includes two steps.First,the local neighborhood radius is calculated according to photons’density,then thefirst-step denoising process is completed via photons’curvature feature based on KNN search and covariance matrix.Second,the local photonfiltering direction and threshold are obtained based on thefirst-step denoising results by RANSAC and elevation frequency histogram,and the local dense noise photons that thefirst-step cannot be identified are further eliminated.The following results are drawn:(1)experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94,and the accuracy is effectively improves with the number of denoising times;(2)experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies.Overall,the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons,and the denoising results can provide more reasonable and reliable data for sustainable urban development.
