Details
| Original language | English |
|---|---|
| Article number | 102894 |
| Journal | International Journal of Applied Earth Observation and Geoinformation |
| Volume | 112 |
| Early online date | 30 Jun 2022 |
| Publication status | Published - Aug 2022 |
Abstract
The task of representing remotely sensed scattered point clouds with mathematical surfaces is ubiquitous to reduce a high number of observations to a compact description with as few coefficients as possible. To reach that goal, locally refined B-splines provide a simple framework to perform surface approximation by allowing an iterative local refinement. Different setups exist (bidegree of the splines, tolerance, refinement strategies) and the choice is often made heuristically, depending on the applications and observations at hand. In this article, we introduce a statistical information criterion based on the t-distribution to judge the goodness of fit of the surface approximation for remote sensing data with outliers. We use a real bathymetry dataset and illustrate how concepts from model selection can be used to select the most adequate refinement strategy of the LR B-splines.
Keywords
- AIC, Bathymetry dataset, Full span refinement strategy, Information criteria, LR B-splines, Optimality, Surface fitting, T-distribution
ASJC Scopus subject areas
- Environmental Science(all)
- Global and Planetary Change
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Environmental Science(all)
- Management, Monitoring, Policy and Law
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In: International Journal of Applied Earth Observation and Geoinformation, Vol. 112, 102894, 08.2022.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - LR B-splines to approximate bathymetry datasets
T2 - An improved statistical criterion to judge the goodness of fit
AU - Skytt, Vibeke
AU - Kermarrec, Gaël
AU - Dokken, Tor
N1 - Funding Information: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Gael Kermarrec reports financial support was provided by Deutsche Forschungsgemeinschaft. This study is supported by the Deutsche Forschungsgemeinschaft under the project KE2453/2-1 and the Norwegian research council under grant number 270922. The data set is provided by the Norwegian map authorities, division Sjøkartverket.
PY - 2022/8
Y1 - 2022/8
N2 - The task of representing remotely sensed scattered point clouds with mathematical surfaces is ubiquitous to reduce a high number of observations to a compact description with as few coefficients as possible. To reach that goal, locally refined B-splines provide a simple framework to perform surface approximation by allowing an iterative local refinement. Different setups exist (bidegree of the splines, tolerance, refinement strategies) and the choice is often made heuristically, depending on the applications and observations at hand. In this article, we introduce a statistical information criterion based on the t-distribution to judge the goodness of fit of the surface approximation for remote sensing data with outliers. We use a real bathymetry dataset and illustrate how concepts from model selection can be used to select the most adequate refinement strategy of the LR B-splines.
AB - The task of representing remotely sensed scattered point clouds with mathematical surfaces is ubiquitous to reduce a high number of observations to a compact description with as few coefficients as possible. To reach that goal, locally refined B-splines provide a simple framework to perform surface approximation by allowing an iterative local refinement. Different setups exist (bidegree of the splines, tolerance, refinement strategies) and the choice is often made heuristically, depending on the applications and observations at hand. In this article, we introduce a statistical information criterion based on the t-distribution to judge the goodness of fit of the surface approximation for remote sensing data with outliers. We use a real bathymetry dataset and illustrate how concepts from model selection can be used to select the most adequate refinement strategy of the LR B-splines.
KW - AIC
KW - Bathymetry dataset
KW - Full span refinement strategy
KW - Information criteria
KW - LR B-splines
KW - Optimality
KW - Surface fitting
KW - T-distribution
UR - http://www.scopus.com/inward/record.url?scp=85133311219&partnerID=8YFLogxK
U2 - 10.1016/j.jag.2022.102894
DO - 10.1016/j.jag.2022.102894
M3 - Review article
AN - SCOPUS:85133311219
VL - 112
JO - International Journal of Applied Earth Observation and Geoinformation
JF - International Journal of Applied Earth Observation and Geoinformation
SN - 1569-8432
M1 - 102894
ER -