John,<br><br>You are right. gdaladdo is not for creating higher resolution images. I thought you needed the opposite.<br>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.<br>
<br>If you want, you can convert the image into an RGB using pct2rgb.py and work with the RGB image.<br><br><div class="gmail_quote">On Fri, Jan 27, 2012 at 11:30 PM, John Twilley <span dir="ltr"><<a href="mailto:mathuin@gmail.com">mathuin@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">I've never seen gdaladdo -- neat command! Alas, the overviews are the<br>
wrong way 'round -- I don't need to zoom out, I need to zoom in.<br>
gdaladdo doesn't handle fractional scale values well, so I can't make<br>
something bigger out of something smaller like I can with gdalwarp.<br>
<br>
Jack.<br>
<div class="im HOEnZb">--<br>
mathuin at gmail dot com<br>
<br>
<br>
<br>
</div><div class="HOEnZb"><div class="h5">On Thu, Jan 26, 2012 at 20:01, Chaitanya kumar CH<br>
<<a href="mailto:chaitanya.ch@gmail.com">chaitanya.ch@gmail.com</a>> wrote:<br>
> John,<br>
><br>
> Try gdaladdo [1] with the resampling algo set to 'mode'. If you use the -ro<br>
> option, it will create an external overview. Check your output with<br>
> different combinations of levels.<br>
><br>
> [1]: <a href="http://www.gdal.org/gdaladdo.html" target="_blank">http://www.gdal.org/gdaladdo.html</a><br>
><br>
> On Fri, Jan 27, 2012 at 5:09 AM, John Twilley <<a href="mailto:mathuin@gmail.com">mathuin@gmail.com</a>> wrote:<br>
>><br>
>> I am working with elevation and landcover data downloaded from the<br>
>> USGS. I use gdalwarp to convert the data to a much smaller pixel. The<br>
>> elevation data works very nicely with cubic resampling, but the only<br>
>> resampling that works at all for the landcover data is<br>
>> nearest-neighbor and that's very blocky. When I last worked with<br>
>> landcover data, I used a majority algorithm which produced smoother<br>
>> output -- but that algorithm is not implemented in gdalwarp.<br>
>> I am looking over the source to gdalwarp to see how hard it is to add<br>
>> a new algorithm. Other than that, though, what options are available<br>
>> to me? Thanks in advance!<br>
>> Jack.--<br>
>> mathuin at gmail dot com<br>
>> _______________________________________________<br>
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><br>
><br>
><br>
><br>
> --<br>
> Best regards,<br>
> Chaitanya kumar CH.<br>
><br>
> <a href="tel:%2B91-9494447584" value="+919494447584">+91-9494447584</a><br>
> 17.2416N 80.1426E<br>
</div></div></blockquote></div><br><br clear="all"><br>-- <br>Best regards,<br>Chaitanya kumar CH.<br><br>+91-9494447584<br>17.2416N 80.1426E<br>