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i.smap(1grass) GRASS GIS User's Manual i.smap(1grass)

NAME
i.smap - Performs contextual image classification using sequential
maximum a posteriori (SMAP) estimation.

KEYWORDS
imagery, classification, supervised classification, segmentation, SMAP

SYNOPSIS
i.smap
i.smap --help
i.smap [-m] group=name subgroup=name signaturefile=name output=name
[goodness=name] [blocksize=integer] [--overwrite] [--help]
[--verbose] [--quiet] [--ui]

Flags:
-m
Use maximum likelihood estimation (instead of smap)

--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 i.gensigset

output=name [required]
Name for output raster map holding classification results

goodness=name
Name for output raster map holding goodness of fit (lower is bet-
ter)

blocksize=integer
Size of submatrix to process at one time
Default: 1024

DESCRIPTION
The i.smap program is used to segment multispectral images using a
spectral class model known as a Gaussian mixture distribution. Since
Gaussian mixture distributions include conventional multivariate Gauss-
ian distributions, this program may also be used to segment multispec-
tral images based on simple spectral mean and covariance parameters.

i.smap has two modes of operation. The first mode is the sequential
maximum a posteriori (SMAP) mode [1,2]. The SMAP segmentation algo-
rithm attempts to improve segmentation accuracy by segmenting the image
into regions rather than segmenting each pixel separately (see NOTES).

The second mode is the more conventional maximum likelihood (ML) clas-
sification which classifies each pixel separately, but requires some-
what less computation. This mode is selected with the -m flag (see be-
low).

OPTIONS
Flags:
-m
Use maximum likelihood estimation (instead of smap). Normal opera-
tion is to use SMAP estimation (see NOTES).

Parameters:
group=name
imagery group
The imagery group that defines the image to be classified.

subgroup=name
imagery subgroup
The subgroup within the group specified that specifies the subset
of the band files that are to be used as image data to be classi-
fied.

signaturefile=name
imagery signaturefile
The signature file that contains the spectral signatures (i.e., the
statistics) for the classes to be identified in the image. This
signature file is produced by the program i.gensigset (see NOTES).

blocksize=value
size of submatrix to process at one time
default: 1024
This option specifies the size of the "window" to be used when
reading the image data.

This program was written to be nice about memory usage without influ-
encing the resultant classification. This option allows the user to
control how much memory is used. More memory may mean faster (or
slower) operation depending on how much real memory your machine has
and how much virtual memory the program uses.

The size of the submatrix used in segmenting the image has a principle
function of controlling memory usage; however, it also can have a sub-
tle effect on the quality of the segmentation in the smap mode. The
smoothing parameters for the smap segmentation are estimated separately
for each submatrix. Therefore, if the image has regions with qualita-
tively different behavior, (e.g., natural woodlands and man-made agri-
cultural fields) it may be useful to use a submatrix small enough so
that different smoothing parameters may be used for each distinctive
region of the image.

The submatrix size has no effect on the performance of the ML segmenta-
tion method.

output=name
output raster map.
The name of a raster map that will contain the classification re-
sults. This new raster map layer will contain categories that can
be related to landcover categories on the ground.

INTERACTIVE MODE
If none of the arguments are specified on the command line, i.smap will
interactively prompt for the names of the maps and files.

NOTES
The SMAP algorithm exploits the fact that nearby pixels in an image are
likely to have the same class. It works by segmenting the image at
various scales or resolutions and using the coarse scale segmentations
to guide the finer scale segmentations. In addition to reducing the
number of misclassifications, the SMAP algorithm generally produces
segmentations with larger connected regions of a fixed class which may
be useful in some applications.

The amount of smoothing that is performed in the segmentation is depen-
dent of the behavior of the data in the image. If the data suggests
that the nearby pixels often change class, then the algorithm will
adaptively reduce the amount of smoothing. This ensures that exces-
sively large regions are not formed.

The degree of misclassifications can be investigated with the goodness
of fit output map. Lower values indicate a better fit. The largest 5 to
15% of the goodness values may need some closer inspection.

The module i.smap does not support MASKed or NULL cells. Therefore it
might be necessary to create a copy of the classification results using
e.g. r.mapcalc:

r.mapcalc "MASKed_map = classification_results"

EXAMPLE
Supervised classification of LANDSAT
g.region raster=lsat7_2002_10 -p
# store VIZ, NIR, MIR into group/subgroup
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
# Now digitize training areas "training" with the digitizer
# and convert to raster model with v.to.rast
v.to.rast input=training output=training use=cat label_column=label
# calculate statistics
i.gensigset trainingmap=training group=my_lsat7_2002 subgroup=my_lsat7_2002 \
signaturefile=my_smap_lsat7_2002 maxsig=5
i.smap group=my_lsat7_2002 subgroup=my_lsat7_2002 signaturefile=my_smap_lsat7_2002 \
output=lsat7_2002_smap_classes
# Visually check result
d.mon wx0
d.rast.leg lsat7_2002_smap_classes
# Statistically check result
r.kappa -w classification=lsat7_2002_smap_classes reference=training

REFERENCES
• C. Bouman and M. Shapiro, "Multispectral Image Segmentation us-
ing a Multiscale Image Model", Proc. of IEEE Int’l Conf. on
Acoust., Speech and Sig. Proc., pp. III-565 - III-568, San
Francisco, California, March 23-26, 1992.

• C. Bouman and M. Shapiro 1994, "A Multiscale Random Field Model
for Bayesian Image Segmentation", IEEE Trans. on Image Process-
ing., 3(2), 162-177" (PDF)

• McCauley, J.D. and B.A. Engel 1995, "Comparison of Scene Seg-
mentations: SMAP, ECHO and Maximum Likelyhood", IEEE Trans. on
Geoscience and Remote Sensing, 33(6): 1313-1316.

SEE ALSO
i.group for creating groups and subgroups
r.mapcalc to copy classification result in order to cut out MASKed sub-
areas
i.gensigset to generate the signature file required by this program

g.gui.iclass, i.maxlik, r.kappa

AUTHORS
Charles Bouman, School of Electrical Engineering, Purdue University

Michael Shapiro, U.S.Army Construction Engineering Research Laboratory

SOURCE CODE
Available at: i.smap source code (history)

Accessed: unknown

GRASS 7.8.7 i.smap(1grass)

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