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

6       i.maxlik  - Classifies the cell spectral reflectances in imagery data.
7       Classification is based on the spectral signature information generated
8       by either i.cluster, g.gui.iclass, or i.gensig.
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KEYWORDS

11       imagery, classification, Maximum Likelihood Classification, MLC
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SYNOPSIS

14       i.maxlik
15       i.maxlik --help
16       i.maxlik group=name subgroup=name signaturefile=name output=name   [re‐
17       ject=name]   [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]
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19   Flags:
20       --overwrite
21           Allow output files to overwrite existing files
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23       --help
24           Print usage summary
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26       --verbose
27           Verbose module output
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29       --quiet
30           Quiet module output
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32       --ui
33           Force launching GUI dialog
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35   Parameters:
36       group=name [required]
37           Name of input imagery group
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39       subgroup=name [required]
40           Name of input imagery subgroup
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42       signaturefile=name [required]
43           Name of input file containing signatures
44           Generated by either i.cluster, g.gui.iclass, or i.gensig
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46       output=name [required]
47           Name for output raster map holding classification results
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49       reject=name
50           Name for output raster map holding reject threshold results
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DESCRIPTION

53       i.maxlik  is a maximum-likelihood discriminant analysis classifier.  It
54       can be used to perform the second step in either an unsupervised  or  a
55       supervised image classification.
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57       Either  image  classification  methods are performed in two steps.  The
58       first step in an unsupervised  image  classification  is  performed  by
59       i.cluster; the first step in a supervised classification is executed by
60       the GRASS program g.gui.iclass (or by providing any  other  raster  map
61       with  already  existing training areas). In both cases, the second step
62       in the image classification procedure is performed by i.maxlik.
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64       In an unsupervised classification,  the  maximum-likelihood  classifier
65       uses  the cluster means and covariance matrices from the i.cluster sig‐
66       nature file to determine to which category (spectral class)  each  cell
67       in  the image has the highest probability of belonging. In a supervised
68       image classification, the maximum-likelihood classifier uses the region
69       means  and  covariance matrices from the spectral signature file gener‐
70       ated by g.gui.iclass, based on regions (groups of image pixels)  chosen
71       by  the user, to determine to which category each cell in the image has
72       the highest probability of belonging.
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74       In either case, the raster map output by i.maxlik is a classified image
75       in which each cell has been assigned to a spectral class (i.e., a cate‐
76       gory).  The spectral classes (categories) can be  related  to  specific
77       land cover types on the ground.
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NOTES

80       The  maximum-likelihood classifier assumes that the spectral signatures
81       for each class (category) in each band file  are  normally  distributed
82       (i.e.,   Gaussian   in   nature).    Algorithms,   such  as  i.cluster,
83       g.gui.iclass, or i.gensig, however, can create signatures that are  not
84       valid  distributed  (more  likely  with g.gui.iclass).  If this occurs,
85       i.maxlik will reject them and display a warning message.
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87       The signature file (signaturefile) contains the cluster and  covariance
88       matrices  that  were  calculated by the GRASS program i.cluster (or the
89       region means and covariance matrices generated by g.gui.iclass, if  the
90       user  runs  a supervised classification). These spectral signatures are
91       what determine the categories (classes) to which image pixels  will  be
92       assigned during the classification process.
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94       The optional name of a reject raster map holds the reject threshold re‐
95       sults. This is the result of a chi square test on each discriminant re‐
96       sult  at  various  threshold  levels of confidence to determine at what
97       confidence level each cell classified (categorized). It is  the  reject
98       threshold  map  layer,  and contains the index to one calculated confi‐
99       dence level for each classified cell in the classified image. 16 confi‐
100       dence intervals are predefined, and the reject map is to be interpreted
101       as 1 = keep and 16 = reject. One of the  possible  uses  for  this  map
102       layer is as a mask, to identify cells in the classified image that have
103       a low probability (high reject index) of being assigned to the  correct
104       class.
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EXAMPLE

107       Second  part  of  the unsupervised classification of a LANDSAT subscene
108       (VIZ, NIR, MIR channels) in North Carolina (see i.cluster  manual  page
109       for the first part of the example):
110       # using here the signaturefile created by i.cluster
111       i.maxlik group=lsat7_2002 subgroup=res_30m \
112         signaturefile=cluster_lsat2002 \
113         output=lsat7_2002_cluster_classes reject=lsat7_2002_cluster_reject
114       # visually check result
115       d.mon wx0
116       d.rast.leg lsat7_2002_cluster_classes
117       d.rast.leg lsat7_2002_cluster_reject
118       # see how many pixels were rejected at given levels
119       r.report lsat7_2002_cluster_reject units=k,p
120       # optionally, filter out pixels with high level of rejection
121       # here we remove pixels of at least 90% of rejection probability, i.e. categories 12-16
122       r.mapcalc "lsat7_2002_cluster_classes_filtered = \
123                  if(lsat7_2002_cluster_reject <= 12, lsat7_2002_cluster_classes, null())"
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125       RGB composite of input data
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127       Output raster map with pixels classified (10 classes)
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129       Output  raster map with rejection probability values (pixel classifica‐
130       tion confidence levels)
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SEE ALSO

133       Image processing and Image classification wiki pages and for historical
134       reference also the GRASS GIS 4 Image Processing manual
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136        g.gui.iclass, i.cluster, i.gensig, i.group, i.segment, i.smap, r.kappa
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AUTHORS

139       Michael Shapiro, U.S.Army Construction Engineering Research Laboratory
140       Tao Wen, University of Illinois at Urbana-Champaign, Illinois
141       Semantic label support: Maris Nartiss, University of Latvia
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SOURCE CODE

144       Available at: i.maxlik source code (history)
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146       Accessed: Mon Jun 20 16:47:30 2022
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148       Main  index | Imagery index | Topics index | Keywords index | Graphical
149       index | Full index
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151       © 2003-2022 GRASS Development Team, GRASS GIS 8.2.0 Reference Manual
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155GRASS 8.2.0                                                        i.maxlik(1)
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