Hi Moritz, Thanks for the answer. All right, I will try that, although I thought it could work also for one class (keeping the rest unclassified). I also tried rescaling my images to range 0-255, but that didn't solve the problem. Still, it's strange that the reject map gives 100% (or close to) over the ROIs, I would expect the opposite...
Will come back to say if adding more classes solves the problem when I try. cheers, Umberto Il 21/12/15 14:29, Moritz Lennert <[email protected]> ha scritto: > > On 20/12/15 14:43, Umberto Filippo Minora wrote: > >I have three maps which are 1) shortwave infrared reflectances derived > >from Landsat ETM+ (Data Type: FCELL); 2) cumulative solar radiation > >derived using "r.sun" from GRASS (Data Type: FCELL); 3) elevation map > >(DEM, Data Type: CELL). > >I am trying to perform a supervised maximum likelihood classification > >over these bands. > >So, first I grouped them using "i.group". > >Then I imported a shp with rock glacier areas, created a new column in > >its attribute table called "IDmaxlik" and assigned a value of "1" (int) > >to it. > >Then I converted the shp to raster with this command: > > > > v.to.rast in=rg_visible out=rg_visible use=attr > >attribute_column=IDmaxlik > > > >and I used "i.gensig" to generate the signature file. > > > > i.gensig trainingmap=rg_visible group=perma_max subgroup=perma_max > >signaturefile=rg_sig > > > >Finally, I run "i.maxlik": > > > > i.maxlik group=perma_max subgroup=perma_max signaturefile=rg_sig > >output=classification01 reject=classification01_reject > > > >Each pixel of the output map ("classification01") is 1, as if the > >everything was classified as "rock glacier". Moreover, the reject map > >("classification01_reject"), has higher values (closer to 16, or 100%) > >over the same rock glaciers used as the region of interest than elsewhere. > > > >Might be the problem related to the fact that "i.gensig" expected a clip > >of the RGB raster on the ROIs, rather than a mask? > > No, I think the problem stems from the fact that you only provide one class, > so each pixel is attributed to that class. If the rest of the area is quite > heterogeneous you should probably even create several other classes and > respective training areas. > > Moritz >
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