I have a DTM raster which has too high a spatial resolution for my intended use, and I need it more generalized. Can't put my finger on how much more, but considerably more.
My first thought was to smooth its peaks and valleys with a gaussian filter, but after a day of repeated passes (it's quite a large area, so each pass takes a lot of time) it was nowhere near how much generalized I needed it to be. Given that I'll later need to do the same with other similar areas, this approach is unfeasible.
I thought that, instead of gathering information from surrounding pixels to change the middle pixel (which requires me to process almost all the pixels) I'd just make a median filter and apply the value to all cells in the kernel, thus making the process vastly faster.
But I have no idea how methodologically sound this is, and how much I'd be losing in terms of precision. This is purely for aesthetic purposes, so the resulting height values do not need to be overtly accurate, but I do need the resulting DTM to largely resemble the area's topography, as if it had been created from a smaller resolution sensor (or close enough).
If this approach is gibberish, which would yield good results in this scenario, without being too computationally heavy?