Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods...

In the current distributed video coding (DVC), a low resolution video sequence is generated with spatial and/or temporal downsampling at the encoder. At the decoder side, interpolation is performed and the interpolated pixels are further refined by using a error-correcting code such as Turbo codes or LDPC. In our previous work, we proposed a spatial domain DVC which uses a line-based downsampli...

Spatial interpolation is an important feature of a Geographic Information System, which is the procedure used to estimate values at unknown locations within the area covered by existing observations. This paper constructs fuzzy rule bases with the aid of a Selforganising Map (SOM) and Backpropagation Neural Networks (BPNNs). These fuzzy rule bases are then used to perform spatial interpolation....

The R package ipdw provides functions for interpolation of georeferenced point data via Inverse Path Distance Weighting. Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances. This method of interpolation requires significant computation and is only practical for relatively small and coarse grids. The ipdw implementation may provi...

We propose using a constraint relational representation for spatial data derived using an inverse distance weighting interpolation method. The advantage of our approach is that many queries that could not be done in traditional GIS systems can now be easily expressed and evaluated in constraint database systems. The data visualization can also be based on constraint techniques.

Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simulations. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipitation estimates based on point rain gauge measurement interpolation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially dis...

Even with the most extensive precautions and careful planning, space based imagers will inevitably experience problems resulting in partial data corruption and possible loss. Such a loss occurs, for example, when individual image detectors are damaged. For a scanning imager this results in missing lines in the image. Images with missing lines can wreak havoc since algorithms not typically desig...

A new method, smoothing spline ANOVA, for combining station records of surface air temperature to get the estimates of regional averages as well as gridpoint values is proposed. This method is closely related to the optimal interpolation (also optimal averaging) method. It may be viewed as a generalization of these methods from spatial interpolation methods to a method interpolating in both spa...

Climate modelers generally require meteorological information on regular grids, but monitoring stations are, in practice, sited irregularly. Thus, there is a need to produce public data records that interpolate available data to a high density grid, which can then be used to generate meteorological maps at a broad range of spatial and temporal scales. In addition to point predictions, quantific...

The Minimum Weighted Norm Interpolation (MWNI) algorithm (Liu and Sacchi, 2001) has been proposed as a method to reconstruct band-limited seismic data along 1, 2 and 3 spatial dimensions. In addition, tests showing the ability of the method to reconstruct data prior to amplitude versus angle wave equation migration were provided in Liu et. al (2003). The method incorporates bandwidth limitation...