Robust best-fit planes from geospatial data | Zendy

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

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Robust best-fit planes from geospatial data

Author(s) -

Richard R. Jones,

Mark A. Pearce,

Carl Jacquemyn,

Francesca Watson

Publication year - 2015

Publication title -

geosphere

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.879

H-Index - 58

ISSN - 1553-040X

DOI - 10.1130/ges01247.1

Subject(s) - geospatial analysis , goodness of fit , curse of dimensionality , data point , regression , plane (geometry) , georeference , eigenvalues and eigenvectors , regression analysis , least squares function approximation , geology , mathematics , geometry , algorithm , data mining , statistics , computer science , remote sensing , geography , physics , quantum mechanics , physical geography , estimator

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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