[GRASS-user] supervised image classification using textural aspects

[Milton Cezar Ribeiro]

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/1999/02.11.16.14/doc/10_213o.pdf

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
>>
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>>
>>
>>
>
> --
> Jonathan A. Greenberg, PhD
> Postdoctoral Scholar
> Center for Spatial Technologies and Remote Sensing (CSTARS)
> University of California, Davis
> One Shields Avenue
> The Barn, Room 250N
> Davis, CA 95616
> Cell: 415-794-5043
> AIM: jgrn307, MSN: jgrn307 at hotmail.com, Gchat: jgrn307
>
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