A new denoising method for photon-counting LiDAR data with different surface types and observation conditions. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Lao, Jieying; Wang, Cheng; Nie, Sheng; Xi, Xiaohuan; Long, Hui; Feng, Baokun; Wang, Zijia
• Is Part Of: International journal of digital earth. Volume 16:Issue 1(2023)
• Page Start: 1551
• Page End: 1567
• Publication Date: 2023-12-31
• Subjects: Photon-counting LiDAR -- adaptive denoising -- complex surface types and topographies -- MATLAS -- ICESat-2; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2023.2203952
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: 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 the first-step denoising process is completed via photons' curvature feature based on KNN search and covariance matrix. Second, the local photon filtering direction and threshold are obtained based on the first-step denoising results by RANSAC and elevation frequency histogram, and the local dense noise photons that the first-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.