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 <[EMAIL PROTECTED]>: > > 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 >> >> _______________________________________________ >> grass-user mailing list >> [email protected] >> http://lists.osgeo.org/mailman/listinfo/grass-user >> >> >> > > -- > 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 > > _______________________________________________ > grass-user mailing list > [email protected] > http://lists.osgeo.org/mailman/listinfo/grass-user >
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