g.gui.iclassgrass - Online in the Cloud

PROGRAM:

NAME

g.gui.iclass - Tool for supervised classification of imagery data.
Generates spectral signatures for an image by allowing the user to outline regions of
interest.

KEYWORDS

general, GUI, classification, supervised classification, signatures

SYNOPSIS

g.gui.iclass
g.gui.iclass --help
g.gui.iclass [-m] [group=name] [subgroup=name] [map=name] [trainingmap=name]
[--help] [--verbose] [--quiet] [--ui]

Flags:
-m
Maximize window

--help
Print usage summary

--verbose
Verbose module output

--quiet
Quiet module output

--ui
Force launching GUI dialog

Parameters:
group=name
Name of input imagery group

subgroup=name
Name of input imagery subgroup

map=name
Name of raster map to load

trainingmap=name
Ground truth training map to load

DESCRIPTION

Supervised Classification Tool (wxIClass) is a wxGUI compoment which allows the user to
create training areas and generate spectral signatures. The resulting signature file can
be used as input for i.maxlik or as a seed signature file for i.cluster. WxIClass can be
launched from the Layer Manager menu Imagery → Classify image → Interactive
input for supervised classification or via command line as g.gui.iclass.

wxIClass currently allows you to:

· create training areas (using customized vector digitizer)

· show histograms for each band and class (category)

· show coincidence plots for each band

· show raster cells that match training areas (within the number of standard
deviations specified)

· specify color of class

· write signature file

· import vector map

· export vector map with attribute table

wxIClass performs the first pass in the GRASS two-pass supervised image classification
process; the GRASS module i.maxlik executes the second pass. Both programs must be run to
generate a classified map in GRASS raster format.

wxIClass is an interactive program that allows the user to create multiple training areas
for multiple classes and calculate the spectral signatures based on the cells that are
within the training areas. During this process the user will be shown histograms for each
image band. The user can also display the cells of the image bands which fall within a
user-specified number of standard deviations from the means in the spectral signature. By
doing this, the user can see how much of the image is likely to be put into the class
associated with the signature.

The spectral signatures are composed of region means and covariance matrices. These
region means and covariance matrices are used in the second pass (i.maxlik) to classify
the image.

Alternatively, the spectral signatures generated by wxIClass can be used for seed means
for the clusters in i.cluster.

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