On 14/10/15 17:40, Nikos Alexandris wrote:
Moritz Lennert:
If you only work with one band, you could use your training areas to
define the mean value and standard deviation in band 4 that corresponds
to your class of interest and then just use r.recode to classify your
image with something like this:
mean-stddev:mean+stddev:1
*:0
Interesting. But that would depend a lot of how big the study extent
is, how many the training areas are, how they are spread, the illumination
geometry of the input acquisition, and more I guess.
This problem is present whatever your technique. But technically, unless
I'm completely mistaken, what I propose is a real maximum likelihood
classification. i.maxlik could not do anything else with only one band.
Using just the info of one band to identify such features sounds quite
hazardous to me, though. More auxiliary info would be better. Segmenting
the image and then filling the polygon attribute table with other info
(which could include infos derived from a DEM or other layers) might be
the best way to go.
Moritz
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