TY - JOUR

T1 - Surface approximation of coastal regions

T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II

AU - Kermarrec, Gael

AU - Skytt, Vibeke

AU - Dokken, Tor

N1 - Funding Information: This study is supported by the Deutsche Forschungsgemeinschaft under the project KE2453/2-1 and the Norwegian research council under grant number 270922. The first data set is provided by the Norwegian map authorities, division Sjøkartverket. The Nederlandse Organisatie voor Wetenschappelijk Onderzoek is thanked for having founded the project which led to the freely available point clouds of the sand-dune.

PY - 2022/5/17

Y1 - 2022/5/17

N2 - 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.

AB - 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.

KW - GIS data set

KW - LR B-spline surfaces

KW - Multilevel B-spline approximation

UR - http://www.scopus.com/inward/record.url?scp=85132304649&partnerID=8YFLogxK

U2 - 10.5194/isprs-annals-V-2-2022-119-2022

DO - 10.5194/isprs-annals-V-2-2022-119-2022

M3 - Conference article

AN - SCOPUS:85132304649

VL - 5

SP - 119

EP - 126

JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

SN - 2194-9042

IS - 2

Y2 - 6 June 2022 through 11 June 2022

ER -