Hi Paola,
Automatically classifying monochrome images is not easy. If you really
want to use classical methods, your best bet is probably to create a
series of pseudo-bands based on indicators such as texture (e.g. using
r.texture, or possibly also r.neighbors). Another approach would be
object-oriented, first segmenting the image into coherent segments and
then calculating indicators for each of these segments, but segmentation
generally also works much better on multiband imagery, so if you use,
e.g., i.segment, then you would probably also have to create a series of
pseudo-bands. Or you can try with r.clump.
When I've worked on such images in the past, neural network approaches
seemed to actually work best. Here's an example on (fairly bad quality)
historic images:
https://dipot.ulb.ac.be/dspace/bitstream/2013/312410/3/FCN_for_historical_images_AUTHOR_VERSION.pdf.
Since we've done that work, networks have evolved very rapidly, and you
should probably be able to get better results nowadays.
Moritz
Le 4/07/24 à 18:23, Paola Salmona via grass-user a écrit :
Hi everybody,
I'm trying to classify some photograms from a Royal Air Force flight
in 1944. They are in greyscale and when I import them into grass using
r.in.gdal I get only one band and I can't use image processing command
such as i.cluster or i.gensig.
I have tried to cheat by assigning to an image a color table,
exporting it, converting in RGB in an external software, then
importing it again into grass.
It "worked" with some alerts, but, beside being a cumbersome method, I
am not sure about the quality of the re-imported image.
Does anybody know a better way?
Thank you very much!!!
Paola
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