Dear Helmuth,
Please note that in general it is considered bad netiquette to address
such a mail to individual developers instead of the project mailing list
[1] (in CC). I, therefore, invite you to use that channel for communication.
On 5/01/19 13:36, Helmuth Wiesinger wrote:
Dear Mr. Lennert,
I’m working on a topic of image segmentation for my agricultural
areas. I’m from Austria, and involved in both industries - farming
and also IT/software development. I’m trying to solve the issue to
estimate the size of wild boar damaged areas in my fields, for the
beginning starting with corn fields [1].
I’ve read an article you were involved with regarding the
semi-automated process chain with GRASS and R, using i.segment.uspo
add-on [2]. I also watched the video
<https://ftp.gwdg.de/pub/misc/openstreetmap/FOSS4G-2016/foss4g-2016-1533-building_applications_with_foss4g_bricks_two_examples_of_the_use_of_grass_gis_modules_as_a_high-level_language_for_the_analyses_of_continuous_space_data_in_economic_geography-hd.mp4>
of your speech about it @Bonn FOSS4G conference 2016. I’m completely
new to image segmentation and after I’ve tried now for two weeks to
make progress I just wanted to ask you for a brief answer if you
think I’m heading into the right direction – trying to solve this
issue, using drone-created GEOTIFFs with ODM (DpenDroneMap), and
subsequently trying to segment/classify them with GRASS.
What I did so far:
* installed GRASS 7.4.2 on Windows 10, but got stuck at a described
bug in i.segment function
<http://osgeo-org.1560.x6.nabble.com/GRASS-GIS-3705-i-segment-produces-empty-output-td5388643.html#a5388649>.
This should be solved with the imminent 7.4.4 release.
* installed GRASS 7.4.0 on Ubuntu using Oracle Virtualbox, where
i.segment works * did some try&error with i.segment with different
thresholds and different minsize parameters for an rgb image (made
from the example tiff [2] using r.composite)
i.segment will certainly work better if you provide it as input a group
of three different r, g, b maps, instead of one single r.composite
output, because then it will have three variables on which to determine
similarity between pixels instead of only one.
* installed add-on’s i.segment.stats and i.segment.uspo,
r.neighborhoodmatrix * gave i.segment.uspo a trial run, but the
result wasn’t really what I was looking for. I assume more tuning is
needed.
Without more explanation of what you are looking for and in what ways
the results diverged from that expectation, it is difficult to help you
on this.
Do you think that this is an efficient approach to solve the
question, how many square meters are damaged by wild boar in a
specific field?
I don't really know how such damage manifests itself spectrally and how
important "objects" are for detection, compared to pixel-by-pixel
information. It might be that for this particular question a pixel-based
approach might be more relevant. If you want to try machine learning
algorithms on this, you can look at the r.learn.ml addon.
I’d really appreciate if you could spare the time for a short
answer.
[1] an example image made with ODM can be found here:
https://www.dropbox.com/s/zo8gihoa9bmunl4/test_orthophoto_try3.zip?dl=0
[2] article “*An Open-Source Semi-Automated Processing Chain for
Urban Object-Based Classification*”
Moritz
[1] https://lists.osgeo.org/mailman/listinfo/grass-user
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