[GRASS-user] supervised image classification using textural
greenberg at ucdavis.edu
Fri Aug 22 14:00:46 EDT 2008
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:
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.
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.
> 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?
> grass-user mailing list
> grass-user at lists.osgeo.org
Jonathan A. Greenberg, PhD
Center for Spatial Technologies and Remote Sensing (CSTARS)
University of California, Davis
One Shields Avenue
The Barn, Room 250N
Davis, CA 95616
AIM: jgrn307, MSN: jgrn307 at hotmail.com, Gchat: jgrn307
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