Robust best-fit planes from geospatial data

Richard R. Jones; Mark A. Pearce; Carl Jacquemyn; Francesca E. Watson

doi:10.1130/GES01247.1

Robust best-fit planes from geospatial data

Richard R. Jones, Mark A. Pearce, Carl Jacquemyn, Francesca E. Watson

School of Energy, Geoscience, Infrastructure and Society

Research output: Contribution to journal › Article › peer-review

30 Citations (Scopus)

Abstract

Total least squares regression is a reliable and efficient way to analyze the geometry of a best-fit plane through georeferenced data points. The suitability of the input data, and the goodness of fit of the data points to the best-fit plane are considered in terms of their dimensionality, and they are quantified using two parameters involving the minimum and intermediate eigenvalues from the regression, as well as the spatial precision of the data.

Original language	English
Pages (from-to)	196-202
Number of pages	7
Journal	Geosphere
Volume	12
Issue number	1
DOIs	https://doi.org/10.1130/GES01247.1
Publication status	Published - Feb 2016

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http://geosphere.gsapubs.org/lookup/doi/10.1130/GES01247.1

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title = "Robust best-fit planes from geospatial data",

abstract = "Total least squares regression is a reliable and efficient way to analyze the geometry of a best-fit plane through georeferenced data points. The suitability of the input data, and the goodness of fit of the data points to the best-fit plane are considered in terms of their dimensionality, and they are quantified using two parameters involving the minimum and intermediate eigenvalues from the regression, as well as the spatial precision of the data.",

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AB - Total least squares regression is a reliable and efficient way to analyze the geometry of a best-fit plane through georeferenced data points. The suitability of the input data, and the goodness of fit of the data points to the best-fit plane are considered in terms of their dimensionality, and they are quantified using two parameters involving the minimum and intermediate eigenvalues from the regression, as well as the spatial precision of the data.

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JO - Geosphere

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Robust best-fit planes from geospatial data

Abstract

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