A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weightin...

This article presents a new algorithm for spatial deinterlacing that could easily be integrated in a more complete deinterlacing system, typically a spatio-temporal motion adaptive one. The spatial interpolation part often fails to reconstruct close to horizontal lines with a proper continuity, leading to highly visible artifacts. Our system preserves the structure continuity taking into accoun...

SAKWeb pretends to be the first E-Learning research development of spatial autocorrelation and Kriging interpolation for geostatisticiens. It is expected to be available in 2009 as a worldwide Web platform (named myGeoOffice.org) for all research institutes and universities that needs to carried out truly spatial stochastic and deterministic interpolation analysis. This will include simulation,...

Given a multidimensional data set and a model of its density, we consider how to deene the optimal interpolation between two points. This is done by assigning a cost to each path through space, based on two competing goals|one to interpolate through regions of high density, the other to minimize arc length. From this path functional, we derive the Euler-Lagrange equations for extremal motion; g...

Information about the distribution and abundance of the habitat-forming sessile organisms in marine ecosystems is of great importance for conservation and natural resource managers. Spatial interpolation methodologies can be useful to generate this information from in situ sampling points, especially in circumstances where remote sensing methodologies cannot be applied due to small-scale spatia...

many steps of seismic data processing sequence suppose that data sets are sampled in time and spatial dimensions uniformly. today, this assumption is true but only in time dimension. modern seismic exploration equipment permits seismic data sets to be sampled uniformly and densely in time dimension. however, along spatial dimension uniform and dense sampling are not possible because of operatin...

Given a multidimensional data set and a model of its density, we consider how to define the optimal interpolation between two points. This is done by assigning a cost to each path through space, based on two competing goals-one to interpolate through regions of high density, the other to minimize arc length. From this path functional, we derive the Euler-Lagrange equations for extremal motionj ...

The temporal and spatial continuity of spatially distributed estimates of snow-covered area (SCA) are limited by the availability of cloud-free satellite imagery; this also affects spatial estimates of snow water equivalent (SWE), as SCA can be used to define the extent of snow telemetry (SNOTEL) point SWE interpolation. In order to extend the continuity of these estimates in time and space to ...

Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpo...