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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, g.gui.iclass, or i.gensig.


       imagery, classification, Maximum Likelihood Classification, MLC


       i.maxlik --help
       i.maxlik   group=name   subgroup=name   signaturefile=name   output=name     [reject=name]
       [--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

       group=name [required]
           Name of input imagery group

       subgroup=name [required]
           Name of input imagery subgroup

       signaturefile=name [required]
           Name of input file containing signatures
           Generated by either i.cluster, g.gui.iclass, or i.gensig

       output=name [required]
           Name for output raster map holding classification results

           Name for output 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 g.gui.iclass.  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 g.gui.iclass,
       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 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  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, g.gui.iclass, or i.gensig, however, can create signatures
       that are not valid distributed (more likely with g.gui.iclass).  If this occurs,  i.maxlik
       will reject them and display a warning message.

       The  signature file (signaturefile) contains the cluster and covariance matrices that were
       calculated by the GRASS program i.cluster (or the region  means  and  covariance  matrices
       generated  by  g.gui.iclass, 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  optional  name of a reject 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.


       Second part of the unsupervised classification  of  a  LANDSAT  subscene  (VIZ,  NIR,  MIR
       channels) in North Carolina (see i.cluster manual page for the first part of the example):
       # using here the signaturefile created by i.cluster
       i.maxlik group=lsat7_2002 subgroup=lsat7_2002 \
         signaturefile=sig_cluster_lsat2002 \
         output=lsat7_2002_cluster_classes reject=lsat7_2002_cluster_reject
       # visually check result
       d.mon wx0
       d.rast.leg lsat7_2002_cluster_classes
       d.rast.leg lsat7_2002_cluster_reject
       # see how many pixels were rejected at given levels lsat7_2002_cluster_reject units=k,p
       # optionally, filter out pixels with high level of rejection
       # here we remove pixels of at least 90% of rejection probability, i.e. categories 12-16
       r.mapcalc "lsat7_2002_cluster_classes_filtered = \
                  if(lsat7_2002_cluster_reject <= 12, lsat7_2002_cluster_classes, null())"

       RGB composite of input data

       Output raster map with pixels classified (10 classes)

       Output  raster  map  with  rejection  probability  values (pixel classification confidence


       Image processing and Image classification wiki pages and for historical reference also the
       GRASS GIS 4 Image Processing manual

        g.gui.iclass, i.cluster, i.gensig,, i.segment, i.smap, r.kappa


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

       Last changed: $Date: 2015-09-14 18:35:33 +0200 (Mon, 14 Sep 2015) $


       Available at: i.maxlik source code (history)

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