Remotely-sensed raster data may be too fine-grained for many purposes.
A typical approach to coarsen the grain is to upscale by reducing the resolution. If the data are continuous (e.g elevation, surface temperature) cells on the raster can be aggregated (and therefore reduced in number) by using a mean function. Or if they are discrete (e.g. vegetation) they may be aggregated using the mode of adjacent cells.
Moving windows are a second example of upscaling. Here a "window" is slid across the raster and the cells under the window are replaced using some function of their values (e.g. using a mean or mode).
I believe that model-based approaches to upscaling will simplify a raster by adopting a more complex model of the raster (i.e. the cells and their adjacencies).
Can anyone clarify my definition of model-based approaches, and provide an example? Do the resulting "cells" even need to be regular, could they vary in shape and size?
EDIT: To be more specific about what I mean by model-based upscaling. Here is an example of what I think may be considered a model-based approach.
Consider using a mathematical graph to represent discrete features on a raster. Then use some method to identify components of these features (i.e. groups of vertices). A Voronoi tessellation, for example, using the components as generators, could then be used to produce an irregular tiling that represents an upscaling of the original raster.
... I could give more detail here, but opt not to, as I am interested in mostly whether or not this would be considered a model-based approach to upscaling ... or alternatively how such operations could be described in generic terms. Is this merely a way of doing interpolation (as defined in an answer below)?