[GRASS-user] supervised image classification using
greenberg at ucdavis.edu
Mon Aug 25 16:30:51 EDT 2008
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.
Dylan Beaudette wrote:
> On Saturday 23 August 2008 08:19:15 am Markus Neteler wrote:
>> On Fri, Aug 22, 2008 at 2:29 PM, Wout Bijkerk <wout.bijkerk at xs4all.nl>
>> 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:
>> 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
> Next time I will give r.texture a try. For the record, here is another example
> of i.smap in use.
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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