In my opinion the "mode" (majority) algorithm should be implemented into gdalwarp, as it would solve re-gridding (to finer or coarser grids) of thematic/discrete data.
The gdaladdo work-around (for coarser grids) is not very intuitive, and does not work for finer re-gridding. As there is existing code for gdaladdo, how hard would it be to adapt it to warping operations? I am not sure that the other gdaladdo methods are worth using in gdalwarp though... gdaladdo: nearest (default),average,gauss,cubic,average_mp,average_magphase,mode: gdalwarp: near (default), bilinear, cubic, cubicspline, lanczos: regards, Etienne On Fri, Jan 27, 2012 at 6:15 PM, John Twilley <math...@gmail.com> wrote: > 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 _______________________________________________ gdal-dev mailing list gdal-dev@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/gdal-dev
