Methodology. The proposed framework of removing specular return noise for photon-counting LiDAR data includes four main steps: data preprocessing, modified statistical features noise removal, noise removal of specular returns, and accuracy assessment.
Feb 29, 2024 · PDF | On Aug 1, 2023, Zijia Wang and others published A methodological framework for specular return removal from photon-counting LiDAR data ...
In this study, the proposed method considers underwater terrain, accurately extracting underwater signal photons using direction-adaptive rotated ellipses.
A methodological framework for specular return removal from photon-counting LiDAR data. Zijia Wang; Sheng Nie; Xiaohuan Xi; Cheng Wang; Jieying Lao; Zhixiang ...
Jun 28, 2024 · The method includes two steps. First, the local neighborhood radius is calculated according to photons' density, then the first-step denoising ...
A methodological framework for specular return removal from photon-counting LiDAR data. Zijia Wang, Sheng Nie, Xiaohuan Xi, Cheng Wang, Jieying Lao, Zhixiang ...
In this study, a multilevel filtering algorithm is proposed to denoise the daytime photon cloud data with high background noise.
Wang, A methodological framework for specular return removal from photon-counting LiDAR data, Int. J. Appl. Earth Obs. Geoinf., № 122, с. 103387; Qin ...
A methodological framework for specular return removal from photon-counting LiDAR data. International Journal of Applied Earth Observation and ...
Sep 21, 2023 · A methodological framework for specular return removal from photon-counting LiDAR data[J]. International Journal of Applied Earth ...