On 30/03/12 23:56, Eric Momsen wrote:
I am reading more about image classification (from
http://grass.osgeo.org/wiki/GRASS_SoC_Ideas ):
4. Implement image segmentation algorithms and tools
5. Implement region-based classification
6. Implement hierarchical classification tools (e.g. being able to
create a large class "forest", with subclasses of different types of
forests)
I see Hamish is interested in mentoring the parallelization portion of
that list. Are these other ideas orphans, or is someone available
that could discuss the background and needs of the community around
these ideas (and/or mentor...) Thanks!
I'm the one who added these idea to the list as I see that this is one
of the reasons colleagues do not adopt GRASS. However, I'm not an expert
in the matter and am not sure I would be very helpful as mentor
(although I'm willing to try).
Concerning the ideas:
4. Currently GRASS does not provide any image segmentation as such.
i.smap contains image segmentation in its process, but the user cannot
get segmented outputs. Many algorithms exist and its an ongoing field of
research. FLOSS software that provide such algorithms include Orfeo
Toolbox (OTB), SAGA, R, Sextante (?) and probably a whole series of
others. I think the implementation of a series of such algorithms could
be a project on its own.
5. One of the main applications of image segmentation today is in
region-based classification of very high resolution imagery. As with
current resolutions individual objects are composed of many pixels, it
is often more efficient to first identify "objects" or homogeneous
multi-pixel regions in the image through segmentation and then to
classify these regions. OTB provides this I think, but I don't know if
any other FLOSS software does. 5 depends on 4, so it is only possible if
4. is limited to the strict minimum in terms of segmentation algorithms
and then focus is put on 5. Maybe a bit too ambitious.
6. In the current classification algorithms in GRASS each designated
class of pixels is on the same hierarchical level as others. However, it
is often interesting to provide the option to classify an image first in
a rough manner into a series of base classes (built-up, vegetation,
naked soils) and then to refine classification within each of these
classes (e.g. built-up into high-density / low-density, vegetation into
forest, grasslands, etc), but to keep the hierarchy, i.e. to allow
extracting an image (and a legend) of the classification at each level.
Hope this helps and maybe motivates others to join-in as mentors.
Moritz
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