Gregory, Matthew wrote:
Hi all,
A recent post got me thinking about resampling techniques. I've posted
a graphic to help illustrate my question.
http://www.fsl.orst.edu/lemma/sandbox/resample.png
In the graphic, assume the input image is the 3x3 grid with black
outlines and blue and yellow dot centers. Assume the red outlined
pixels represent three different output resolutions of a single pixel
each having its center at the black dot.
Using bilinear interpolation, each of these three output resolutions get
the same value (tested using gdalwarp) based on the four yellow cell
centers. I understand why this is happening and realize this is the
expected behavior.
My question, however, is whether or not there is a resampling technique
(inside or outside GDAL) that uses the proportional weights and values
of *all* input pixels touched by the output pixel. At the finest
resolution in the illustration, this would be equivalent a nearest
neighbor resampling (ie. the output pixel is wholly contained within the
input pixel) and at the coarsest resolution, all nine input pixels would
contribute to the output value based on proportional area.
Matt,
In a loose sense this is how averaged overviews works, and it is part of
why they are suitable for radically downsampling while the at most 4x4
resampling kernels in gdalwarp are not suitable for radical downsampling.
The current gdalwarp infrastructure does not make it easy to do what
you want - particularly when faced with transformations that are
non-linear. If we took the narrower case of simple downsampling we
could design various options like what you want - possible based on
the template of the averaged overviews.
However, I have no plans to pursue this myself.
Best regards,
--
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I set the clouds in motion - turn up | Frank Warmerdam, warmer...@pobox.com
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and watch the world go round - Rush | Geospatial Programmer for Rent
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