i.gensigset(1grass) GRASS GIS User's Manual i.gensigset(1grass)
NAME
i.gensigset - Generates statistics for i.smap from raster map.
KEYWORDS
imagery, classification, supervised classification, SMAP, signatures
SYNOPSIS
i.gensigset
i.gensigset --help
i.gensigset trainingmap=name group=name subgroup=name signature-
file=name [maxsig=integer] [--overwrite] [--help] [--verbose]
[--quiet] [--ui]
Flags:
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog
Parameters:
trainingmap=name [required]
Ground truth training map
group=name [required]
Name of input imagery group
subgroup=name [required]
Name of input imagery subgroup
signaturefile=name [required]
Name for output file containing result signatures
maxsig=integer
Maximum number of sub-signatures in any class
Default: 5
DESCRIPTION
i.gensigset is a non-interactive method for generating input into
i.smap. It is used as the first pass in the a two-pass classification
process. It reads a raster map layer, called the training map, which
has some of the pixels or regions already classified. i.gensigset will
then extract spectral signatures from an image based on the classifica-
tion of the pixels in the training map and make these signatures avail-
able to i.smap.
The user would then execute the GRASS program i.smap to create the fi-
nal classified map.
OPTIONS
Parameters
trainingmap=name
ground truth training map
This raster layer, supplied as input by the user, has some of its pix-
els already classified, and the rest (probably most) of the pixels un-
classified. Classified means that the pixel has a non-zero value and
unclassified means that the pixel has a zero value.
This map must be prepared by the user in advance by using a combination
of wxGUI vector digitizer and v.to.rast, or some other import/develop-
ment process (e.g., v.transects) to define the areas representative of
the classes in the image.
At present, there is no fully-interactive tool specifically designed
for producing this layer.
group=name
imagery group
This is the name of the group that contains the band files which com-
prise the image to be analyzed. The i.group command is used to con-
struct groups of raster layers which comprise an image.
subgroup=name
subgroup containing image files
This names the subgroup within the group that selects a subset of the
bands to be analyzed. The i.group command is also used to prepare this
subgroup. The subgroup mechanism allows the user to select a subset of
all the band files that form an image.
signaturefile=name
resultant signature file
This is the resultant signature file (containing the means and covari-
ance matrices) for each class in the training map that is associated
with the band files in the subgroup selected.
maxsig=value
maximum number of sub-signatures in any class
default: 5
The spectral signatures which are produced by this program are "mixed"
signatures (see NOTES). Each signature contains one or more subsigna-
tures (represeting subclasses). The algorithm in this program starts
with a maximum number of subclasses and reduces this number to a mini-
mal number of subclasses which are spectrally distinct. The user has
the option to set this starting value with this option.
INTERACTIVE MODE
If none of the arguments are specified on the command line, i.gensigset
will interactively prompt for the names of these maps and files.
It should be noted that interactive mode here only means interactive
prompting for maps and files. It does not mean visualization of the
signatures that result from the process.
NOTES
The algorithm in i.gensigset determines the parameters of a spectral
class model known as a Gaussian mixture distribution. The parameters
are estimated using multispectral image data and a training map which
labels the class of a subset of the image pixels. The mixture class
parameters are stored as a class signature which can be used for subse-
quent segmentation (i.e., classification) of the multispectral image.
The Gaussian mixture class is a useful model because it can be used to
describe the behavior of an information class which contains pixels
with a variety of distinct spectral characteristics. For example, for-
est, grasslands or urban areas are examples of information classes that
a user may wish to separate in an image. However, each of these infor-
mation classes may contain subclasses each with its own distinctive
spectral characteristic. For example, a forest may contain a variety
of different tree species each with its own spectral behavior.
The objective of mixture classes is to improve segmentation performance
by modeling each information class as a probabilistic mixture with a
variety of subclasses. The mixture class model also removes the need
to perform an initial unsupervised segmentation for the purposes of
identifying these subclasses. However, if misclassified samples are
used in the training process, these erroneous samples may be grouped as
a separate undesired subclass. Therefore, care should be taken to pro-
vided accurate training data.
This clustering algorithm estimates both the number of distinct sub-
classes in each class, and the spectral mean and covariance for each
subclass. The number of subclasses is estimated using Rissanen’s mini-
mum description length (MDL) criteria [1]. This criteria attempts to
determine the number of subclasses which "best" describe the data. The
approximate maximum likelihood estimates of the mean and covariance of
the subclasses are computed using the expectation maximization (EM) al-
gorithm [2,3].
WARNINGS
If warnings like this occur, reducing the remaining classes to 0:
...
WARNING: Removed a singular subsignature number 1 (4 remain)
WARNING: Removed a singular subsignature number 1 (3 remain)
WARNING: Removed a singular subsignature number 1 (2 remain)
WARNING: Removed a singular subsignature number 1 (1 remain)
WARNING: Unreliable clustering. Try a smaller initial number of clusters
WARNING: Removed a singular subsignature number 1 (-1 remain)
WARNING: Unreliable clustering. Try a smaller initial number of clusters
Number of subclasses is 0
then the user should check for:
• the range of the input data should be between 0 and 100 or 255
but not between 0.0 and 1.0 (r.info and r.univar show the
range)
• the training areas need to contain a sufficient amount of pix-
els
REFERENCES
• J. Rissanen, "A Universal Prior for Integers and Estimation by
Minimum Description Length," Annals of Statistics, vol. 11, no.
2, pp. 417-431, 1983.
• A. Dempster, N. Laird and D. Rubin, "Maximum Likelihood from
Incomplete Data via the EM Algorithm," J. Roy. Statist. Soc. B,
vol. 39, no. 1, pp. 1-38, 1977.
• E. Redner and H. Walker, "Mixture Densities, Maximum Likelihood
and the EM Algorithm," SIAM Review, vol. 26, no. 2, April 1984.
SEE ALSO
i.group, i.smap, r.info, r.univar, wxGUI vector digitizer
AUTHORS
Charles Bouman, School of Electrical Engineering, Purdue University
Michael Shapiro, U.S.Army Construction Engineering Research Laboratory
SOURCE CODE
Available at: i.gensigset source code (history)
Accessed: unknown
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GRASS 7.8.7 i.gensigset(1grass)
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