期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2022
卷号：V-2-2022
页码：119-126
DOI：10.5194/isprs-annals-V-2-2022-119-2022
语种：English
出版社：Copernicus Publications
摘要：Geospatial data acquisition of terrains produces huge, noisy and scattered point clouds. An efficient use of the acquired data requires structured and compact data representations. Working directly in a point cloud is often not appealing. To face this challenge, approximation with tensor product B-spline surfaces is attractive. It reduces the point cloud description to relatively few coefficients compared to the volume of the original point cloud. However, this representation lacks the ability to adapt the resolution of the shape to local variations in the point cloud. The result is frequently that noise is approximated and that surfaces have unwanted oscillations.Locally Refined (LR) B-spline surfaces were introduced to face this challenge and provide a tool for approximating Geographic Information System point clouds. In our LR B-spline based approximation algorithm, iterative least-squares approximation is combined with a Multilevel B-spline Approximation to reduce memory consumption. We apply the approach to data sets from coastal regions in Norway and the Netherlands, and compare the obtained approximation with a raster method. We further highlight the potential of LR B-spline volumes for spatio-temporal visualisation of deformation patterns.
关键词：multilevel B-spline approximation; GIS data set; LR B-spline surfaces
