期刊
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MGRS.2023.3285261
关键词
Laser radar; Point cloud compression; Three-dimensional displays; Strips; Software tools; Surveys; Surface topography
Data artifacts are common in airborne lidar point clouds and their derivatives, degrading visual quality and compromising data-driven analyses. This article provides an overview of these artifacts and surveys existing solutions. From an end-user perspective, it aims to facilitate rapid issue diagnosis and efficient referrals to specialized resources during data collection and processing stages. The article also promotes collaboration between the scientific community, software developers, and system manufacturers for a comprehensive airborne lidar point cloud processing bundle.
Data artifacts are a common occurrence in airborne lidar point clouds and their derivatives [e.g., intensity images and digital elevation models (DEMs)]. Defects, such as voids, holes, gaps, speckles, noise, and stripes, not only degrade lidar visual quality but also compromise subsequent data-driven analyses. Despite significant progress in understanding these defects, end users of lidar data confronted with artifacts are stymied by the scarcities of both resources for the dissemination of topical advances and analytic software tools. The situation is exacerbated by the wide-ranging array of potential internal and external factors, with examples including weather/atmospheric/Earth surface conditions, system settings, and laser receiver-transmitter axial alignment, that underlie most data artifact issues. In this article, we provide a unified overview of artifacts commonly found in airborne lidar point clouds and their derivatives and survey the existing literature for solutions to resolve these issues. The presentation is from an end-user perspective to facilitate rapid diagnoses of issues and efficient referrals to more specialized resources during data collection and processing stages. We hope that the article can also serve to promote coalescence of the scientific community, software developers, and system manufacturers for the ongoing development of a comprehensive airborne lidar point cloud processing bundle. Achieving this goal would further empower end users and move the field forward.
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