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I'm trying to make a supervised classification in Grass 6.4.3 under Linuxmint 16.

After digitizing the training areas, I transformed them from vector to raster. Then I've done the following: just in case: g.region rast=train_area_2013_03_05_raster@PERMANENT,2013_03_05_toar4@PERMANENT

then

i.group group=group subgroup=sub_group input=2013_03_05_toar4@PERMANENT,2013_03_05_toar5@PERMANENT,2013_03_05_toar3@PERMANENT

and

i.gensig trainingmap=train_area_2013_03_05_raster@PERMANENT group=group@PERMANENT subgroup=sub_group signaturefile=superv_class

but the resulting signature file, only contains one character: "#".

The output of i.gensig in the console is the following:

Finding training classes...
3 classes found
Calculating class means...
Calculating class covariance matrices...
Signature 1 not invertible
Signature 2 not invertible
Signature 3 not invertible
i.gensig complete.

Obviously if I try: i.maxlik group=group@PERMANENT subgroup=sub_group sigfile=superv_class class=results_class

I obtain the following error message:

ERROR: Unable to read signature file <superv_class>

I've been searching, but I couldn't find the solution.

I've tried: this Problem running i.gensigset in Grass. Any ideas?

My problem is similar to this: i.maxlik cannot read i.class output signature file. But here, there's no solution suggested. As in this case, I also used i.cluster to generate an unsupervised signature file and i.maxlik reads this perfectly well.

Here there's another similar question, but again, with no solution...

What does Signature X not invertible means?

Any idea? Thanks for your help!

  • If you found a solution to your question, why not post it as an answer instead of an edit? – Joseph Jul 3 '14 at 12:26
1

Joseph, you're right. Here is the solution to MY problem. May be someone could add some more detail, but this is how I solved it:

  • The region of my interest was about a quarter of the original image so, I clipped the Landsat image to a smaller size, leaving out a big region in which I'm not interested BEFORE start classifying (I was planning to do it at the end of the process).
  • I re-digitized the training areas paying attention to, as someone told me: "Training areas should be big enough to contain a significant number of pixels, but small enough to be homogeneous".
  • After that, I've done all the process (v.to.rast, i.gensig, i.maxlik) again, and with no problems!

  • Besides the size of the image and that I had all the training areas in a small region of it, I think that my previous digitized training-areas weren't too small, but most probably, they weren't homogeneous.

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