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. This falls outside the traditional { nearest neighbor | bilinear interpolation | cubic convolution } resampling techniques and there may be a reason why this is a bad idea. I can see that it might be prohibitively slow for large output pixel resolutions but, to my way of thinking, a potentially more accurate representation of the underlying (finer-resolution) data. matt _______________________________________________ gdal-dev mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/gdal-dev
