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
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