I saw a few messages talking about i.class not being available to WindowsGRASS.
Next January I will give a few classes of Remote Sensing in the University and I was planning to use GRASS6.4 in the classes. One of the major modules is Supervised Classification. Those classes will be taught using Windows OS.
According to GRASS book (I bought it ), I can perform supervised radiometric classification using digitized training areas (i.class) (I) or using auxiliary data (II).
About (I) I realized that i.class options are not available in Windows. Besides i.class is there any alternative to have an interactive selection of training areas?
About (II). Is it possible to use a ESRI shapefile with all areas as an input and then converted to shapefile?
Another question/request is if there is any manual/tutorial explaining classification procedure in GRASS6.4?
Thanks
Best Regards,
Luis Lisboa
Universitat Pompeu Fabra, Barcelona
I saw a few messages talking about i.class not being available to
WindowsGRASS.
Next January I will give a few classes of Remote Sensing in the University
and I was planning to use GRASS6.4 in the classes. One of the major modules
is Supervised Classification. Those classes will be taught using Windows OS.
According to GRASS book (I bought it ), I can perform supervised
radiometric classification using digitized training areas (i.class) (I) or
using auxiliary data (II).
About (I) I realized that i.class options are not available in Windows.
Besides i.class is there any alternative to have an interactive selection of
training areas?
I am afraid not -- only non-interactive approach. Digitize areas,
rasterize vector areas and use i.gensig.
About (II). Is it possible to use a ESRI shapefile with all areas as an
input and then converted to shapefile?
Import data to native format using v.out.ogr or link data using
v.external (some problems can occur). The OGR interface is being
improved in GRASS 7.
Another question/request is if there is any manual/tutorial explaining classification procedure in GRASS6.4?
I am using GRASS for remote sensing course at my university (only in
Czech) [1], classification [2, 3].
I am afraid not – only non-interactive approach. Digitize areas, rasterize vector areas and >use i.gensig.
Ok. But If I try to have Linux or Mac OS machine, I could use i.class right?
About your links, ehehehe unfortunely Czech is not in my language domain (English, Spanish, French, Catalan). But I will try to get some ideas Thanks
Import data to native format using v.out.ogr or link data using
v.external (some problems can occur). The OGR interface is being
improved in GRASS 7.
Uhm I’m planning to use 6.4 (it has binaries). has anyone tried with success this operation?
Thanks
Best regards
Luis Lisboa
Universitat Pompeu Fabra, Barcelona
I have imported shape files in grass 6.4 using v.in.ogr. Not a big hassle.
One way to acquire training areas for the supervised classification is
to digitize them using either the tcl/tk digitizer or, what I find
easiest, the QGis digitizer. QGis is free and can create shape files.
I'm not sure (never tested it) but I believe you can even use QGis to
create Grass vector files.
Once the vectors are done, just convert them to raster, use i.gensig
to extract the signatures and then use i.maxlik to classify the image.
cheers
Daniel
On Thu, Dec 3, 2009 at 9:33 AM, Luis Lisboa <luislisboa1975@gmail.com> wrote:
Hello Martin thanks for your reply.
I am afraid not -- only non-interactive approach. Digitize areas, rasterize
vector areas and >use i.gensig.
Ok. But If I try to have Linux or Mac OS machine, I could use i.class right?
About your links, ehehehe unfortunely Czech is not in my language domain
(English, Spanish, French, Catalan). But I will try to get some ideas
Thanks
Import data to native format using v.out.ogr or link data using
v.external (some problems can occur). The OGR interface is being
improved in GRASS 7.
Uhm I'm planning to use 6.4 (it has binaries). has anyone tried with success
this operation?
Thanks
Best regards
Luis Lisboa
Universitat Pompeu Fabra, Barcelona
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), I can perform supervised radiometric classification using digitized training areas (i.class) (I) or using auxiliary data (II).