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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 [q] group=name subgroup=name sigfile=name classes=integer [seed=name] [sample=row_interval,col_interval] [iterations=integer] [convergence=float] [separation=float] [min_size=integer] [reportfile=name] [verbose] [quiet]
Flags:
 q
 Quiet
 verbose
 Verbose module output
 quiet
 Quiet module output
Parameters:
 group=name
 Name of input imagery group
 subgroup=name
 Name of input imagery subgroup
 sigfile=name
 Name for output file containing result signatures
 classes=integer
 Initial number of classes
 Options: 1255
 seed=name
 Name of file containing initial signatures
 sample=row_interval,col_interval
 Sampling intervals (by row and col); default: ~10,000 pixels
 iterations=integer
 Maximum number of iterations
 Default: 30
 convergence=float
 Percent convergence
 Options: 0100
 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 twopass
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
kmeans 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
sigfile 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.
Flags:
 q
 Run quietly. Suppresses output of program
percentcomplete messages and the time elapsed from the
beginning of the program. If this flag is not used, these
messages are printed out.
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.
 sigfile=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
i.class).
The purpose of seed signatures is to optimize the cluster
decision boundaries (means) for the number of clusters
specified.
 sample=row_interval,col_interval
 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).
 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 imagespecific 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
Running in command line mode, i.cluster will
overwrite the output signature file and reportfile (if
required by the user) without prompting if the files
existed.
>
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 userspecified minimum distance are then merged. If a
cluster has less than the userspecified 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
userspecified 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 rast=lsat7_2002_10 p
# store VIZ, NIR, MIR into group/subgroup (leaving out TIR)
i.group group=my_lsat7_2002 subgroup=my_lsat7_2002 \
input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
i.cluster group=my_lsat7_2002 subgroup=my_lsat7_2002 sigfile=sig_clust_lsat2002 \
classes=10 report=rep_clust_lsat2002.txt
To complete the unsupervised classification, i.maxlik is subsequently used.
See example in its manual page.
SEE ALSO
i.class,
i.group,
i.gensig,
i.maxlik
AUTHORS
Michael Shapiro,
U.S. Army Construction Engineering Research Laboratory
Tao Wen,
University of Illinois at UrbanaChampaign, Illinois
Last changed: $Date: 20151127 01:47:53 0800 (Fri, 27 Nov 2015) $
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