On 15.11.18 12:59, Marco wrote:
Upsample to 1m, then downsample to 5?
That helps with the 'five does not divide by two' aspect and would be an
improvement on averaging over all contributing points. But it still uses
a box filter on the interpolated signal, which does not have what you'd
want in a low-pass filter, namely a short transition band and good
stop-band attenuation.
Another problem with your proposal is that it initially requires to
produce an intermediate data set which is four times (2*2) as large as
the original one, which makes the process long and memory-hungry.
It's better to use a sensible smoothing method in the first place. I
suppose noone has given this over much thought as in the past you were
ever so happy about every bit of resolution you could get. But in a time
where we get very high resolution LIDAR data, the need to downsample
properly is arising. Look at the interpolation methods: gdalwarp lists
twelve different ones. The first few are for upsampling, and the
remainder mostly for dealing with noisy data. Upsampling is well
covered: cubicspline and lanczos are reasonably sophisticated upsampling
filters, but there is no good downsampling filter. I think this is an
omission, hence my post. The problem is real; downsampling with
'average' produces artifacts, even from previously upsampled data.
Kay
On 15-11-18 12:06, Kay F. Jahnke wrote:
I have a very detailed GeoTIFF DEM of part of my target area (2m
resolution). The remainder of the area is covered in 5m. So I'd like
to downsample the moore detailed data to have everything in 5m for
further processing. The nearest thing to what I have in mind is using
-r average, but from signal processing theory I know that using a
simple average over a block of data is not a good approximation for a
low-pass filter. Also, since some of the source pixels are fully
inside the target pixels and some are only partly (5 doesn't divide by
2), An average over all participating source pixels seems wrong. What
would you advise?
With regards
Kay
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