[gdal-dev] Heuristics to classify raster data ?

[Joaquim Luis]

Even,

Did not get it all. You want a method that allows you to tell between a 
map and aerial/satellite image?
I believe the k-means algorithm would produce quite good results on maps 
as is expectable that individual clusters would have low variance.

Joaquim

> Hi,
>
> I'd be interested in an algorithm to automate the classification of raster data
> between maps (let's say rendering of OpenStreetMap data, or other digital
> maps) one one side and aerial/satellite imagery on the other side, without
> looking at metadata (bare geotiff typically). This is to help in automating
> bulk of import of data from a media and establishing a first level of
> classification.
>
> Has anyone already done that and has code and/or advice to share, or know a
> software project that would do that ?
>
> Some ideas that came to my mind :
> - maps have typically a much more reduce number of colors than imagery, but
> you may have imagery that has already been transformed to 256 colors to reduce
> storage space.
> - maps have generally a majority color (e.g. white, green), but not in all
> zones (urban zones will have more features)
> - maps have higher spatial frequency (lines, text) whereas imagery will be
> more continuous : use of gradient, and compute statistics on it ?
>
> Even
>