I believe my landcover data is a classified raster. It has a color table and uses values like 11 for water and 12 for ice.
The majority algorithm I implemented that looked okay in the old code used a number of the nearest neighbors. Taking just one nearest neighbor made the blocky images I described in my original message. If gdalwarp had gdaladdo's mode algorithm, would that do a better job than the nearest-neighbor algorithm? When I tried using pct2rgb.py on the VRT, then running the gdalwarp with a variety of resampling methods, then using rgb2pct.py on the resulting output (referencing the VRT for the palette), the output was filled with speckles. For example, when a finger of sand poked out into the water, the various methods created many tiny one-pixel lakes and islands. Not quite right. Jack. -- mathuin at gmail dot com On Fri, Jan 27, 2012 at 11:27, Chaitanya kumar CH <chaitanya...@gmail.com> wrote: > John, > > You are right. gdaladdo is not for creating higher resolution images. I > thought you needed the opposite. > I assume your landcover data is a classified raster. Classified data is > usually zoomed in with nearest neighbor interpolation. Unless you fiddle > with the interpolation window, the majority algorithm is pretty much the > nearest neighbor while zooming in. > > If you want, you can convert the image into an RGB using pct2rgb.py and work > with the RGB image. > > > On Fri, Jan 27, 2012 at 11:30 PM, John Twilley <math...@gmail.com> wrote: >> >> I've never seen gdaladdo -- neat command! Alas, the overviews are the >> wrong way 'round -- I don't need to zoom out, I need to zoom in. >> gdaladdo doesn't handle fractional scale values well, so I can't make >> something bigger out of something smaller like I can with gdalwarp. >> >> Jack. >> -- >> mathuin at gmail dot com >> >> >> >> On Thu, Jan 26, 2012 at 20:01, Chaitanya kumar CH >> <chaitanya...@gmail.com> wrote: >> > John, >> > >> > Try gdaladdo [1] with the resampling algo set to 'mode'. If you use the >> > -ro >> > option, it will create an external overview. Check your output with >> > different combinations of levels. >> > >> > [1]: http://www.gdal.org/gdaladdo.html >> > >> > On Fri, Jan 27, 2012 at 5:09 AM, John Twilley <math...@gmail.com> wrote: >> >> >> >> I am working with elevation and landcover data downloaded from the >> >> USGS. I use gdalwarp to convert the data to a much smaller pixel. The >> >> elevation data works very nicely with cubic resampling, but the only >> >> resampling that works at all for the landcover data is >> >> nearest-neighbor and that's very blocky. When I last worked with >> >> landcover data, I used a majority algorithm which produced smoother >> >> output -- but that algorithm is not implemented in gdalwarp. >> >> I am looking over the source to gdalwarp to see how hard it is to add >> >> a new algorithm. Other than that, though, what options are available >> >> to me? Thanks in advance! >> >> Jack.-- >> >> mathuin at gmail dot com >> >> _______________________________________________ >> >> gdal-dev mailing list >> >> gdal-dev@lists.osgeo.org >> >> http://lists.osgeo.org/mailman/listinfo/gdal-dev >> > >> > >> > >> > >> > -- >> > Best regards, >> > Chaitanya kumar CH. >> > >> > +91-9494447584 >> > 17.2416N 80.1426E > > > > > -- > Best regards, > Chaitanya kumar CH. > > +91-9494447584 > 17.2416N 80.1426E _______________________________________________ gdal-dev mailing list gdal-dev@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/gdal-dev
