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i.maxlik(1)		    GRASS GIS User's Manual		   i.maxlik(1)

       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	  [re-
       ject=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=nameA [required]
	   Name	of input imagery group

       subgroup=nameA [required]
	   Name	of input imagery subgroup

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

       output=nameA [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  sig-
       nature  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	gener-
       ated  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 cate-
       gory).	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 re-
       sults. This is the result of a chi square test on each discriminant re-
       sult 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	confi-
       dence level for each classified cell in the classified image. 16	confi-
       dence 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

       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  classifica-
       tion confidence levels)

       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

       Available at: i.maxlik source code (history)

       Main index | Imagery index | Topics index | Keywords index |  Graphical
       index | Full index

       A(C) 2003-2020 GRASS Development	Team, GRASS GIS	7.8.4 Reference	Manual

GRASS 7.8.4							   i.maxlik(1)


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