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Short:
I want to change the resolution of a raster and smooth the gray ramp like shown in the images bellow. The preference is to use GDAL, PIL or Numpy.

Description:
I'm kriging points into rasters with an output resolution of 20 meters with the High Performance Geostatistical Library. I don't want to change the output resolution because the interpolation time increase exponentially.
With this resolution the output image is ugly (pixelated and aliased). I don't know if it is conceptually correct but I want the image to be smoother like in the example bellow. It's something like 'reinterpolating' the image into a better resolution one. I'm using python so my preferences are GDAL, Python Imaging Library or Numpy. The answer could be theoretical, like pointing out the algorithm name or the concept of this kind of operation.

Source:
enter image description here

Destination:
enter image description here

EDIT Results with gdalwarp cubic spline:
enter image description here

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3 Answers 3

up vote 4 down vote accepted

1) The hard way: With a bit of coding it's (relatively) easy to implement bilinear interpolation to accomplish decent resampling.

2) The easy way: use GDAL as explained in this previous GISSE post, but in reverse (decreasing the pixel size).

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It worked. The best results were with cubic spline. I've posted the results above. Thanks. –  Pablo Aug 1 '12 at 12:04
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Use GDALReprojectImage, which is exposed in Python:

from osgeo import gdal
help(gdal.ReprojectImage)

For the smooth interpolation, use bilinear or cubic methods. This function is awkward, since it doesn't take keyword arguments, thus you need to find the position:

gdal.ReprojectImage(src_ds, dst_ds, None, None, gdal.GRA_Bilinear)

Probably the tricky part is setting up dst_ds, which needs to have a geotransform similar to src_ds, but with modified cell sizes.

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+1. I'm not sure what its "cubic" method is. If it's a cubic spline, it would likely be smoother than bilinear; but if it is cubic convolution, it will be less smooth than bilinear. A cubic spline would be about ideal. –  whuber Jul 31 '12 at 21:08
    
@whuber true, there are two "cubic" algorithms options: GRA_CubicSpline (try this first) vs. GRA_Cubic (convolution). –  Mike T Jul 31 '12 at 21:38
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To smooth out the variations, you need a low-pass filter. You could write your own using GDAL, or there's one using GRASS. I haven't tried it, but here's a guide http://wiki.awf.forst.uni-goettingen.de/wiki/index.php/Exercise_31

You may want to up-sample your raster first before applying the low-pass filter to get better resolution output.

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I don't think the OP wants to smooth out the variations: otherwise, why go to the effort of kriging in the first place? All its benefits would be lost. Instead, as the images suggest, the question asks for a smooth resampling procedure to create a higher-resolution version of the results already obtained (which is a smart and efficient way to speed up kriging). –  whuber Jul 31 '12 at 16:39
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