Ubuntu Manpage: i.gensigset - Generates statistics for i.smap from raster map.

NAME

       i.gensigset  - Generates statistics for i.smap from raster map.

KEYWORDS

       imagery, classification, supervised, SMAP

SYNOPSIS

       i.gensigset
       i.gensigset help
       i.gensigset     trainingmap=name     group=name     subgroup=name     signaturefile=string
       [maxsig=integer]   [--verbose]  [--quiet]

   Parameters:
       trainingmap=name
           Ground truth training map

       group=name
           Name of input imagery group

       subgroup=name
           Name of input imagery subgroup

       signaturefile=string
           Name for output file containing result signatures

       maxsig=integer
           Maximum number of sub-signatures in any class
           Default: 10

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. The user must use r.digit, a
combination of v.digit and v.to.rast, or some other import/developement process (e.g.,
v.in.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: 10

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 [3].

REFERENCES

       1      J.  Rissanen, "A Universal Prior for Integers and Estimation by Minimum Description
              Length," Annals of Statistics, vol. 11, no. 2, pp. 417-431, 1983.

       2      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.

       3      E.  Redner  and  H.  Walker,  "Mixture  Densities,  Maximum  Likelihood  and the EM
              Algorithm," SIAM Review, vol. 26, no. 2, April 1984.

AUTHORS

       Charles Bouman, School of Electrical Engineering, Purdue University
       Michael Shapiro, U.S.Army Construction Engineering Research Laboratory

       Last changed: $Date: 2011-11-08 03:29:50 -0800 (Tue, 08 Nov 2011) $

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