On 23/05/11 06:38, Chethan S. wrote:
Hi all!
For my work I am interested in identifying a single kind of vegetation
from LANDSAT Images. If I use signature file with just one signature
class(I take many training sites all over the area), the classified
image with i.maxlik appears full pink. On the other hand if I randomly
pick training sites for one more class(water) and have two classes in
the signature file this does not happen. So I am wondering will it not
be possible to use a single class's signature sites for supervised
classification.
Classification will attribute each pixel to the class with the signature
closest to the one of the pixel. If you only have one class then this
will, by definition, always be the closest.
I don't know if there is another way, but if you have a series of
threshold values for your class in each landsat band, then you could
just use r.mapcalc to identify those pixels that are within the defined
thresholds in each band, i.e. something like this:
r.mapcalc myareas = "if((band1>X1 && band1<Y1) && (band2>X2 && band2<Y2)
&& etc), 1, null())
Moritz
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