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LR B-splines to approximate bathymetry datasets: An improved statistical criterion to judge the goodness of fit

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.

Category

Academic article

Client

  • Research Council of Norway (RCN) / 270922

Language

English

Author(s)

Affiliation

  • SINTEF Digital / Mathematics and Cybernetics
  • Gottfried Wilhelm Leibniz Universität Hannover

Year

2022

Published in

International Journal of Applied Earth Observation and Geoinformation

ISSN

1569-8432

Volume

112

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