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


       imagery, classification, supervised classification, SMAP, signatures


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

           Allow output files to overwrite existing files

           Print usage summary

           Verbose module output

           Quiet module output

           Force launching GUI dialog

       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

           Maximum number of sub-signatures in any class
           Default: 5


       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

       The user would then execute the GRASS program i.smap to create the final classified map.


           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

       This map must be prepared by the user in advance by using a combination  of  wxGUI  vector
       digitizer  and, 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

           imagery group

       This  is the name of the group that contains the band files which comprise the image to be
       analyzed. The command is used to construct groups of raster layers which  comprise
       an image.

           subgroup containing image files

       This  names  the  subgroup  within  the  group  that  selects  a subset of the bands to be
       analyzed. The 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.

           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

           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.


       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.


       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].


       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 ( and r.univar show the range)

           ·   the training areas need to contain a sufficient amount of pixels


           ·   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.smap,, r.univar, wxGUI vector digitizer


       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) $


       Available at: i.gensigset source code (history)

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