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Hi Paola,<br>
<br>
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.<br>
<br>
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:
<a class="moz-txt-link-freetext" href="https://dipot.ulb.ac.be/dspace/bitstream/2013/312410/3/FCN_for_historical_images_AUTHOR_VERSION.pdf">https://dipot.ulb.ac.be/dspace/bitstream/2013/312410/3/FCN_for_historical_images_AUTHOR_VERSION.pdf</a>.
Since we've done that work, networks have evolved very rapidly, and
you should probably be able to get better results nowadays.<br>
<br>
Moritz<br>
<br>
<div class="moz-cite-prefix">Le 4/07/24 à 18:23, Paola Salmona via
grass-user a écrit :<br>
</div>
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cite="mid:PA4PR07MB8598DE5DAD503B5275FE3E6397DE2@PA4PR07MB8598.eurprd07.prod.outlook.com">
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Hi everybody,</div>
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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.</div>
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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.</div>
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It "worked" with some alerts, but, beside being a cumbersome
method, I am not sure about the quality of the re-imported
image.</div>
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Does anybody know a better way?</div>
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Thank you very much!!!</div>
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Paola</div>
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