On Fri, 2008-08-22 at 16:02 -0300, Milton Cezar Ribeiro wrote: > 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 [...]
All this is a "burning" issue for me as well. I was looking for r.texture as well but never found something detailed. I did some tests on my own without success. Now I read Markus' notes [1] and I'll try to play around sooner or later (thank you Markus). If i.smap could auto-segment multi-layer raster maps with some kind of input parameters (not training samples) and r.texture really does "statistical magic", then GRASS would challenge proprietary image segmentation/classification tools. I think that a lot of people would like that. [1] http://mpa.itc.it/markus/ortho_smap/orthophoto_smap_043050.txt Greetings, Nikos _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
