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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.

SEE ALSO

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

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

AUTHORS

       Charles Bouman, School of Electrical Engineering, Purdue University

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

       Last changed: $Date: 2014-12-19 22:17:36 +0100 (Fri, 19 Dec 2014) $

SOURCE CODE

       Available at: i.smap source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.6.1 Reference Manual