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


       imagery, classification, supervised, SMAP


       i.smap help
       i.smap [-mq] group=name subgroup=name signaturefile=name output=name   [blocksize=integer]
       [--overwrite]  [--verbose]  [--quiet]

           Use maximum likelihood estimation (instead of smap)

           Run quietly

           Allow output files to overwrite existing files

           Verbose module output

           Quiet module output

           Name of input imagery group

           Name of input imagery subgroup

           Name of file containing signatures
           Generated by i.gensigset

           Name for output raster map

           Size of submatrix to process at one time
           Default: 128


       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  [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).


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

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

              imagery group
              The imagery group that defines the image to be classified.

              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.

              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).

              size of submatrix to process at one time
              default: 128
              This option specifies the size of the "window" to be used when  reading  the  image

       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 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.


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


       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

       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"


       Supervised classification of LANDSAT
       g.region rast=lsat7_2002_10 -p
       # store VIZ, NIR, MIR into group/subgroup group=my_lsat7_2002 subgroup=my_lsat7_2002 \
       # Now digitize training areas "training" with the digitizer
       # and convert to raster model with 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 \
       # 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


                     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,

                     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 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


       Charles Bouman, School of Electrical Engineering, Purdue University

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

       Last changed: $Date: 2012-12-16 04:47:36 -0800 (Sun, 16 Dec 2012) $

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