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NAME

KEYWORDS

SYNOPSIS

Flags:

-m

Use maximum likelihood estimation (instead of smap)

-q

Run quietly

--overwrite

Allow output files to overwrite existing files

--verbose

Verbose module output

--quiet

Quiet module output

Parameters:

group=name

Name of input imagery group

subgroup=name

Name of input imagery subgroup

signaturefile=name

Name of file containing signatures

Generated by i.gensigset

output=name

Name for output raster map

blocksize=integer

Size of submatrix to process at one time

Default: 1024

DESCRIPTION

i.smap has two modes of operation. The first mode is the sequential maximum a posteriori (SMAP) mode [1,2]. The SMAP segmentation algorithm 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) classification which classifies each pixel separately, but requires somewhat less computation. This mode is selected with the -m flag (see below).

OPTIONS

Flags:

-m

Use maximum likelihood estimation (instead of smap). Normal operation is to use SMAP estimation (see NOTES).

-q

Run quietly, without printing messages about program progress. Without this flag, messages will be printed (to stderr) as the program progresses.

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 classified.

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 influencing 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 subtle 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 qualitatively different behavior, (e.g., natural woodlands and man-made agricultural 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 segmentation method.

output=name

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

INTERACTIVE MODE

NOTES

The amount of smoothing that is performed in the segmentation is dependent 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 excessively large regions are not formed.

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

g.region rast=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 training out=training use=cat labelcolumn=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 x0
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 using 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 Processing., 3(2), 162-177" (PDF)
• McCauley, J.D. and B.A. Engel 1995, "Comparison of Scene Segmentations: SMAP, ECHO and Maximum Likelyhood", IEEE Trans. on Geoscience and Remote Sensing, 33(6): 1313-1316.

SEE ALSO

r.mapcalc to copy classification result in order to cut out MASKed subareas

i.gensigset to generate the signature file required by this program

AUTHORS

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

Last changed: $Date: 2014-06-26 15:25:51 -0700 (Thu, 26 Jun 2014) $

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