Ubuntu Manpage: i.smap - Performs contextual image classification using sequential maximum a posteriori (SMAP)

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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 better)

       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
       Gaussian  distributions,  this  program  may also be used to segment multispectral 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 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).

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

       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 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  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 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.

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)

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