So as a heads up, we actually built some functionality into starspan to
deal with the huge amount of time it takes to process texture images for
data exploration. If you check out the minirasterstrip output, it
allows you to take your field data, and produce a small strip of raster
windows around each of your field points along with an affiliated
vector. This allows you to run the texture transforms just on the
neighborhood around known data. This will allow you to quickly test out
a bunch of different texture (and spectral) transforms without waiting a
week to finish a bunch of texture transforms that may or may not be
useful. Here's the basic workflow:
1) Acquire "training" vector and overlapping raster data.
2) Run starspan to output the minirasterstrip + minirasterstrip vector.
3) Perform spatial (texture) and spectral transforms on the minirasterstrip.
4) Apply different classifiers to the minirasterstrip (with various
spatial and spectral transforms), decide on which spectral and spatial
transforms are required, and which classifier to use.
5) As a FINAL step, apply the spectral and spatial transforms to the
entire image, and apply the classification rules.
http://starspan.casil.ucdavis.edu
--j
Dylan Beaudette wrote:
On Saturday 23 August 2008 08:19:15 am Markus Neteler wrote:
Wout,
On Fri, Aug 22, 2008 at 2:29 PM, Wout Bijkerk <[EMAIL PROTECTED]>
wrote: ...
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
...
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?
...
It's worth trying. I have done so for orthophoto classification and
it definitely helped:
http://mpa.itc.it/markus/ortho_smap/
The texture map(s) add patterns which can stabilize the process
of assigning pixels to classes/can improve the segmentation part
of i.smap as it renders heterogeneous areas to homogeneous
areas.
Markus
_______________________________________________
Next time I will give r.texture a try. For the record, here is another example
of i.smap in use.
http://casoilresource.lawr.ucdavis.edu/drupal/node/548
Dylan
--
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: [EMAIL PROTECTED], Gchat: jgrn307
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