[GRASS-user] supervised image classification using textural aspects

Nikos Alexandris nikos.alexandris at felis.uni-freiburg.de
Mon Aug 25 08:39:09 EDT 2008

On Fri, 2008-08-22 at 16:02 -0300, Milton Cezar Ribeiro wrote:
> Dear Wout,
> Following Jonathan´s comments about window size selecion, give a look
> at:
> RIBEIRO, M. C. ; ALVES, D. S. ; YANASSE, C. C. F. ; SOARES, J. V. ;
> II, F. M. . Window size selection for SAR classification using texture
> measures: a case study of a Brazilian Amazon test site. In: Segunda
> jornada latino-americana de sensoriamento remoto por radar: técnicas
> de processamento de imagens, 1998, Santos, SP. Segunda jornada
> latino-americana de sensoriamento remoto por radar: técnicas de
> processamento de imagens, 1998
> http://marte.dpi.inpe.br/col/sid.inpe.br/deise/1999/
> Best wishes,
> miltinho
> 2008/8/22, Jonathan Greenberg <greenberg at ucdavis.edu>: 
>         Wout:
>           This is more of a general response, rather than a "how to"
>         for GRASS.  At 1m, you are absolutely going to need to include
>         some level of spatial processing (texture being the "brute
>         force" way of getting at these sorts of things).  At that
>         resolution, trees become multi-pixel objects, and there will
>         be more spectral variation within a tree crown than between
>         any two trees.  Which textures to try are an issue you will
>         need to resolve by experimentation -- variance is often an
>         important factor, average less so.  The window size choice is
>         extremely important, because each window size is picking up
>         different pieces of information.  For instance, for large
>         trees at 1m, a 3x3 window is going to be picking up
>         within-crown variation, so you will get high values near the
>         sunlit-to-shadow transition, and near the crown edges, but low
>         variation within the shadow or within the sunlit portions of
>         the tree.  Your window should be larger than a tree crown if
>         you expect to get fairly similar values within the tree crown
>         (which is critical if you want to approach this in a pixel
>         based approach).  I don't recommend pixel-based approaches for
>         macro-pixel objects, however.
>           When working with "hyperspatial" remote sensing data, keep
>         in mind you are classifying "trees" as unique landscape
>         objects (polygons, really), not the less well defined "forest"
>         -- as such, you should try to employ object-based approaches.
>          You can google scholar "tree crown remote sensing" to get
>         some ideas on how people approach this problem.  Keep an eye
>         out for papers by Lefsky, Pouliot, Popescu, Wulder, and
>         Leckie, amongst others.  If you want to understand how to
>         scale from tree crown objects to a "forest" I'll tout one of
>         my papers:
>         http://casil.ucdavis.edu/docman/view.php/52/141/greenbergetal2006b.pdf
>           It would be cool to implement some of these algorithms in
>         GRASS, but to my knowledge there is no package (you'd have to
>         write one) -- in fact, very few remote sensing packages have
>         even the beginnings of these capabilities, although some of
>         the authors I mention above may be willing to share their
>         code. 
>         --j 
>         Wout Bijkerk wrote:
>                 Hello everybody,
>                 I am trying to perform a supervised classification of
>                 false color
>                 images. The resolution of the bands (IR,R,G) is 1
>                 meter. Additionally I
>                 can use a DEM as input ( hor. res. = 5m), but
>                 apparently null-values
>                 within the training areas are causing some problems
>                 (see
>                 http://lists.osgeo.org/pipermail/grass-user/2008-June/045261.html) so I
>                 am not using the DEM for the moment. I intend to use
>                 the combined
>                 radiometric and geometric modules i.gensigset and
>                 i.smap.
>                 Looking at the images, I wonder if including textural
>                 features within
>                 the images would be usefull: a forest canopy has a far
>                 coarser texture
>                 than a grassland. Also in the Grassbook this is
>                 mentioned, and for the
>                 supervised classification of saltmarshes in Germany,
>                 textural features
>                 are also used (see i.e.
>                 http://www.nature-consult.de/images/downl/Agit_2008_nature-consult.pdf,
>                 but in German), but this is not further explained.
>                 This brings me to the following questions:
>                 1) Is it usefull to make a raster with textural image
>                 features  as an
>                 extra input for i.gensigset / i.smap? The
>                 i.gensigset / i.smap procedure
>                 is partly based on geometry and therefor on texture as
>                 well so what does
>                 a texturemap add?
>                 2) if it is usefull, which textural feature is then
>                 aproppriate? I have
>                 been experimenting and until now simply variance seems
>                 to make the
>                 difference between forest and shrubland compared to
>                 grassland, and
>                 reed-vegetation. This was using a windowsize of 5,
>                 meaning 5x5 m.
>                 Did anyone have any experience with this?
>                 Regards,
>                 Wout

All this is a "burning" issue for me as well. I was looking for
r.texture as well but never found something detailed. I did some tests
on my own without success. Now I read Markus' notes [1] and I'll try to
play around sooner or later (thank you Markus).

If i.smap could auto-segment multi-layer raster maps with some kind of
input parameters (not training samples) and r.texture really does
"statistical magic", then GRASS would challenge proprietary image
segmentation/classification tools. I think that a lot of people would
like that.

[1] http://mpa.itc.it/markus/ortho_smap/orthophoto_smap_043050.txt

Greetings, Nikos

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