i.maxlik(1grass) GRASS GIS User's Manual i.maxlik(1grass)
NAME
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.
KEYWORDS
imagery, classification, Maximum Likelihood Classification, MLC
SYNOPSIS
i.maxlik
i.maxlik --help
i.maxlik group=name subgroup=name signaturefile=name output=name [re-
ject=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 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
reject=name
Name for output raster map holding reject threshold results
DESCRIPTION
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.
NOTES
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
class.
EXAMPLE
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
r.report 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)
SEE ALSO
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.group, 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.maxlik source code (history)
Accessed: unknown
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GRASS 7.8.7 i.maxlik(1grass)
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