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NAME

       i.cluster   -  Generates  spectral  signatures  for  land  cover types in an image using a
       clustering algorithm.
       The resulting signature file is used as input for i.maxlik, to  generate  an  unsupervised
       image classification.

KEYWORDS

       imagery, classification, signatures

SYNOPSIS

       i.cluster
       i.cluster --help
       i.cluster   group=name   subgroup=name   signaturefile=name  classes=integer   [seed=name]
       [sample=rows,cols]     [iterations=integer]     [convergence=float]     [separation=float]
       [min_size=integer]    [reportfile=name]    [--overwrite]  [--help]  [--verbose]  [--quiet]
       [--ui]

   Flags:
       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       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

       classes=integer [required]
           Initial number of classes
           Options: 1-255

       seed=name
           Name of file containing initial signatures

       sample=rows,cols
           Number of rows and columns over which a sample pixel is taken

       iterations=integer
           Maximum number of iterations
           Default: 30

       convergence=float
           Percent convergence
           Options: 0-100
           Default: 98.0

       separation=float
           Cluster separation
           Default: 0.0

       min_size=integer
           Minimum number of pixels in a class
           Default: 17

       reportfile=name
           Name for output file containing final report

DESCRIPTION

       i.cluster performs the first pass in the two-pass unsupervised classification of  imagery,
       while  the  GRASS  module i.maxlik executes the second pass.  Both commands must be run to
       complete the unsupervised classification.

       i.cluster is a clustering algorithm (a modification of the k-means  clustering  algorithm)
       that  reads  through  the  (raster)  imagery  data  and builds pixel clusters based on the
       spectral reflectances of  the  pixels  (see  Figure).   The  pixel  clusters  are  imagery
       categories  that  can  be  related  to  land  cover  types  on  the  ground.  The spectral
       distributions of the clusters (e.g., land cover spectral signatures) are influenced by six
       parameters  set by the user. A relevant parameter set by the user is the initial number of
       clusters to be discriminated.

       Fig.:  Land  use/land  cover  clustering  of  LANDSAT  scene
       (simplified)

       i.cluster  starts  by  generating  spectral  signatures  for  this  number of clusters and
       "attempts" to end up with this number of clusters  during  the  clustering  process.   The
       resulting  number  of  clusters  and  their  spectral  distributions,  however,  are  also
       influenced by the range of the spectral values (category values) in the  image  files  and
       the  other  parameters  set by the user.  These parameters are:  the minimum cluster size,
       minimum cluster separation, the percent convergence, the maximum number of iterations, and
       the row and column sampling intervals.

       The  cluster  spectral signatures that result are composed of cluster means and covariance
       matrices.  These cluster means and  covariance  matrices  are  used  in  the  second  pass
       (i.maxlik)  to classify the image.  The clusters or spectral classes result can be related
       to land cover types on the ground.  The user has to specify the name of  group  file,  the
       name of subgroup file, the name of a file to contain result signatures, the initial number
       of clusters to be discriminated, and optionally other parameters  (see  below)  where  the
       group  should contain the imagery files that the user wishes to classify.  The subgroup is
       a subset of this group.  The user must create a group and subgroup by  running  the  GRASS
       program  i.group  before  running i.cluster.  The subgroup should contain only the imagery
       band files that the user wishes to classify.  Note that this subgroup  must  contain  more
       than  one  band  file.  The purpose of the group and subgroup is to collect map layers for
       classification or analysis. The signaturefile is the file  to  contain  result  signatures
       which  can  be  used  as  input  for i.maxlik.  The classes value is the initial number of
       clusters to be discriminated; any parameter values  left  unspecified  are  set  to  their
       default values.

   Parameters:
       group=name
           The  name  of  the group file which contains the imagery files that the user wishes to
           classify.

       subgroup=name
           The name of the subset of the group specified in group option, which must contain only
           imagery  band  files  and  more than one band file. The user must create a group and a
           subgroup by running the GRASS program i.group before running i.cluster.

       signaturefile=name
           The name assigned to output signature file which contains signatures  of  classes  and
           can  be  used  as  the  input  file for the GRASS program i.maxlik for an unsupervised
           classification.

       classes=value
           The number of clusters that will initially be identified  in  the  clustering  process
           before the iterations begin.

       seed=name
           The name of a seed signature file is optional. The seed signatures are signatures that
           contain cluster means and covariance matrices  which  were  calculated  prior  to  the
           current  run  of i.cluster. They may be acquired from a previously run of i.cluster or
           from a supervised classification signature training  site  section  (e.g.,  using  the
           signature file output by g.gui.iclass).  The purpose of seed signatures is to optimize
           the cluster decision boundaries (means) for the number of clusters specified.

       sample=rows,cols
           These numbers are optional with default values based on the size of the data set  such
           that the total pixels to be processed is approximately 10,000 (consider round up). The
           smaller these numbers, the larger the sample size used to generate the signatures  for
           the classes defined.

       iterations=value
           This  parameter  determines the maximum number of iterations which is greater than the
           number of iterations predicted to achieve the optimum percent convergence. The default
           value  is  30. If the number of iterations reaches the maximum designated by the user;
           the user may want  to  rerun  i.cluster  with  a  higher  number  of  iterations  (see
           reportfile).
           Default: 30

       convergence=value
           A  high  percent  convergence is the point at which cluster means become stable during
           the iteration process.  The default value is 98.0 percent.  When  clusters  are  being
           created,  their  means  constantly change as pixels are assigned to them and the means
           are recalculated to include the new pixel.  After  all  clusters  have  been  created,
           i.cluster  begins  iterations  that  change  cluster means by maximizing the distances
           between them.  As these means shift, a higher and higher  convergence  is  approached.
           Because  means  will  never become totally static, a percent convergence and a maximum
           number of iterations  are  supplied  to  stop  the  iterative  process.   The  percent
           convergence  should be reached before the maximum number of iterations. If the maximum
           number of iterations is reached, it is probable that the desired  percent  convergence
           was not reached. The number of iterations is reported in the cluster statistics in the
           report file (see reportfile).
           Default: 98.0

       separation=value
           This is the minimum separation below which clusters will be merged  in  the  iteration
           process. The default value is 0.0. This is an image-specific number (a "magic" number)
           that depends on the image data being classified and the number of final clusters  that
           are  acceptable.  Its determination requires experimentation. Note that as the minimum
           class (or cluster) separation is increased, the maximum number  of  iterations  should
           also  be  increased  to  achieve this separation with a high percentage of convergence
           (see convergence).
           Default: 0.0

       min_size=value
           This is the minimum number of pixels that will be used to define  a  cluster,  and  is
           therefore the minimum number of pixels for which means and covariance matrices will be
           calculated.
           Default: 17

       reportfile=name
           The reportfile  is  an  optional  parameter  which  contains  the  result,  i.e.,  the
           statistics  for  each cluster. Also included are the resulting percent convergence for
           the clusters, the number of iterations that was required to achieve  the  convergence,
           and the separability matrix.

NOTES

   Sampling method
       i.cluster  does  not  cluster  all  pixels,  but only a sample (see parameter sample). The
       result of that clustering is not  that  all  pixels  are  assigned  to  a  given  cluster;
       essentially,  only  signatures  which are representative of a given cluster are generated.
       When running i.cluster on the same data asking for the same number of  classes,  but  with
       different sample sizes, likely slightly different signatures for each cluster are obtained
       at each run.

   Algorithm used for i.cluster
       The algorithm uses input parameters set by the user on the initial number of clusters, the
       minimum  distance  between  clusters,  and  the correspondence between iterations which is
       desired, and minimum size for each cluster. It also asks if all pixels to be clustered, or
       every  "x"th  row  and  "y"th  column  (sampling),  the  correspondence between iterations
       desired, and the maximum number of iterations to be carried out.

       In the 1st pass, initial cluster means for each band  are  defined  by  giving  the  first
       cluster  a value equal to the band mean minus its standard deviation, and the last cluster
       a value equal to the band mean plus its standard deviation, with all other  cluster  means
       distributed  equally  spaced  in  between  these. Each pixel is then assigned to the class
       which it is closest to, distance being measured as Euclidean distance. All  clusters  less
       than  the  user-specified minimum distance are then merged. If a cluster has less than the
       user-specified minimum number of pixels, all those pixels are again reassigned to the next
       nearest  cluster.  New cluster means are calculated for each band as the average of raster
       pixel values in that band for all pixels present in that cluster.

       In the 2nd pass, pixels are then again reassigned to clusters based on new cluster  means.
       The  cluster  means  are  then  again  recalculated.   This  process is repeated until the
       correspondence between iterations reaches a user-specified  level,  or  till  the  maximum
       number of iterations specified is over, whichever comes first.

EXAMPLE

       Preparing  the  statistics  for unsupervised classification of a LANDSAT subscene in North
       Carolina:
       g.region raster=lsat7_2002_10 -p
       # store VIZ, NIR, MIR into group/subgroup (leaving out TIR)
       i.group group=lsat7_2002 subgroup=lsat7_2002 \
         input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
       # generate signature file and report
       i.cluster group=lsat7_2002 subgroup=lsat7_2002 \
         signaturefile=sig_cluster_lsat2002 \
         classes=10 reportfile=rep_clust_lsat2002.txt
       To complete the unsupervised classification, i.maxlik is subsequently used.   See  example
       in its manual page.

SEE ALSO

           •   Image classification wiki page

           •   Historical reference also the GRASS GIS 4 Image Processing manual (PDF)

           •   Wikipedia  article  on k-means clustering (note that i.cluster uses a modification
               of the k-means clustering algorithm)

        g.gui.iclass, i.group, i.gensig, i.maxlik, i.segment, i.smap, r.kappa

AUTHORS

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

SOURCE CODE

       Available at: i.cluster source code (history)

       Accessed: unknown

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       © 2003-2022 GRASS Development Team, GRASS GIS 7.8.7 Reference Manual