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       i.maxlik  - Classifies the cell spectral reflectances in imagery data.
       Classification  is  based  on  the  spectral  signature  information  generated  by either
       i.cluster, i.class, or i.gensig.


       imagery, classification, MLC


       i.maxlik help
       i.maxlik   [-q]   group=name   subgroup=name   sigfile=name   class=name     [reject=name]
       [--overwrite]  [--verbose]  [--quiet]

           Run quietly

           Allow output files to overwrite existing files

           Verbose module output

           Quiet module output

           Name of input imagery group

           Name of input imagery subgroup

           Name of file containing signatures
           Generated by either i.cluster, i.class, or i.gensig

           Name for raster map holding classification results

           Name for raster map holding reject threshold results


       i.maxlik  is  a  maximum-likelihood  discriminant  analysis classifier.  It can be used to
       perform the second step in either an unsupervised or a supervised image classification.

       Either image classification methods are performed in two steps.   The  first  step  in  an
       unsupervised  image  classification  is  performed  by  i.cluster;  the  first  step  in a
       supervised classification is executed by the GRASS program i.class.  In  both  cases,  the
       second step in the image classification procedure is performed by i.maxlik.

       In  an  unsupervised  classification,  the  maximum-likelihood classifier uses the cluster
       means and covariance matrices from the i.cluster signature  file  to  determine  to  which
       category (spectral class) each cell in the image has the highest probability of belonging.
       In a supervised image classification, the maximum-likelihood classifier  uses  the  region
       means and covariance matrices from the spectral signature file generated by i.class, based
       on regions (groups of image pixels) chosen by the user, to  determine  to  which  category
       each cell in the image has the highest probability of belonging.

       In  either  case,  the  raster map layer output by i.maxlik is a classified image in which
       each cell has been assigned to a spectral class (i.e., a category).  The spectral  classes
       (categories) can be related to specific land cover types on the ground.

       The  program  will  run  non-interactively  if  the user specifies the names of raster map
       layers, i.e., group and subgroup names, seed signature file  name,  result  classification
       file name, and any combination of non-required options in the command line, using the form
       i.maxlik[-q] group=name subgroup=name sigfile=name class=name [reject=name]

       where each flag and options have the meanings stated below.

       Alternatively, the user can simply type i.maxlik  in  the  command  line  without  program
       arguments.  In this case the user will be prompted for the program parameter settings; the
       program will run foreground.


              The imagery group contains the subgroup to be classified.

              The subgroup contains image files, which were used to create the signature file  in
              the program i.cluster, i.class, or i.gensig to be classified.

              The  name  of  the signatures to be used for the classification. The signature file
              contains the cluster and covariance matrices that  were  calculated  by  the  GRASS
              program  i.cluster  (or  the  region  means  and  covariance  matrices generated by
              i.class, if the user runs a supervised classification). These  spectral  signatures
              are  what determine the categories (classes) to which image pixels will be assigned
              during the classification process.

              The name of a raster map holds the classification  results.  This  new  raster  map
              layer  will  contain categories that can be related to land cover categories on the

              The optional name of a raster map holds the reject threshold results. This  is  the
              result of a chi square test on each discriminant result at various threshold levels
              of  confidence  to  determine  at  what  confidence  level  each  cell   classified
              (categorized).  It is the reject threshold map layer, and contains the index to one
              calculated confidence level for each classified cell in the  classified  image.  16
              confidence intervals are predefined, and the reject map is to be interpreted as 1 =
              keep and 16 = reject. One of the possible uses for this map layer is as a mask,  to
              identify  cells  in  the  classified image that have a low probability (high reject
              index) of being assigned to the correct class.


       The maximum-likelihood classifier assumes that the  spectral  signatures  for  each  class
       (category)  in  each  band  file  are  normally  distributed  (i.e.,  Gaussian in nature).
       Algorithms, such as i.cluster, i.class, or i.gensig, however, can create  signatures  that
       are  not  valid  distributed  (more  likely  with i.class).  If this occurs, i.maxlik will
       reject them and display a warning message.

       This program runs interactively if the  user  types  i.maxlik  only.  If  the  user  types
       i.maxlik  along  with  all  required  options, it will overwrite the classified raster map
       without prompting if this map existed.


       Completion of the unsupervised  classification  of  a  LANDSAT  subscene  (VIZ,  NIR,  MIR
       channels) in North Carolina (see i.cluster manual page for the first part):
       i.maxlik group=my_lsat7_2002 subgroup=my_lsat7_2002 sigfile=sig_clust_lsat2002 \
                 class=lsat7_2002_clust_classes reject=lsat7_2002_clust_classes.rej
       # Visually check result
       d.mon x0
       d.rast.leg lsat7_2002_clust_classes
       d.rast.leg lsat7_2002_clust_classes.rej


       The GRASS 4 Image Processing manual

        i.class, i.cluster, i.gensig,, r.kappa


       Michael Shapiro, U.S.Army Construction Engineering Research Laboratory
       Tao Wen, University of Illinois at Urbana-Champaign, Illinois

       Last changed: $Date: 2012-12-19 14:16:40 -0800 (Wed, 19 Dec 2012) $

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