Thanks Nikos, Well, actually my training areas are defined as they need to be specific features in my location (rock glaciers). I would like to use i.maxlik to classify the other pixels of my image (other than rock glaciers, alias my training areas). They should be classified as "similar" to my training areas with a certain degree (a certain likelihood). The fact I am only using one band is driven by the fact that only band 4 of Landsat 7 reflectances shows significant difference in value in my training areas than the other unclassified areas. Using twice the same band (band 4) is fine, but I am having difficulties in grouping the same band. Using i.group for instance recognizes that the map is the same and does not add it twice to the group, therefore I cannot use i.maxlik. I don't know if producing textures is the way to go in my case, but I will give this a try before rejecting this option. First, however, I need to study this function output. Meanwhile, many thanks to both of you (Veronoca and Nikos)! If you have any other useful idea, I will consider them as well.
Umberto Il 14/10/15 01:48, Nikos Alexandris <[email protected]> ha scritto: > > Umberto: > > > First of all, as I already have training areas, i want to use them in a > > > Supervised classification rather than an unsupervised one (as ISODATA). > > Having already (well defined) training areas is a big step. > > > > > Second, I am only interested in using one band for the classification, > > Interested or restricted? If more bands are available, and you go the > supervised way, why opting for a single-band-based process? Many would > rather advise for a segmentation process (aka object-based approach). > Of course, to second Veronica, it depends on what the "source" > resolution is and what the features of interest are. > > > > > which was the reason I could not use "i.maxlik". > > Don't hesitate to experiment with the same band twice, I'd say (again). > > Nikos >
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