bionic (1) i.gensigset.1grass.gz
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 signaturefile=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 classification of the pixels in the training map and make these signatures
available to i.smap.
The user would then execute the GRASS program i.smap to create the final classified map.
OPTIONS
Parameters
trainingmap=name
ground truth training map
This raster layer, supplied as input by the user, has some of its pixels already classified, and the rest
(probably most) of the pixels unclassified. 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/developement 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 comprise the image to be analyzed. The
i.group command is used to construct 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 covariance 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 subsignatures (represeting subclasses). The algorithm in this program
starts with a maximum number of subclasses and reduces this number to a minimal 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 subsequent 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, forest, grasslands or urban areas are examples of information classes that a user may wish to
separate in an image. However, each of these information 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 provided accurate training
data.
This clustering algorithm estimates both the number of distinct subclasses in each class, and the
spectral mean and covariance for each subclass. The number of subclasses is estimated using Rissanen’s
minimum 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) algorithm [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 pixels
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
Last changed: $Date: 2015-02-14 22:07:18 +0100 (Sat, 14 Feb 2015) $
SOURCE CODE
Available at: i.gensigset source code (history)
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