i.smap(1)	       Grass User's Manual		i.smap(1)



NNAAMMEE
       ii..ssmmaapp	-  An  imagery	function that performs contextual
       image classification using sequential maximum a posteriori
       (SMAP) estimation.
       _(_G_R_A_S_S _I_m_a_g_e_r_y _P_r_o_g_r_a_m_)

SSYYNNOOPPSSIISS
       ii..ssmmaapp
       ii..ssmmaapp hheellpp
       ii..ssmmaapp  [--mmqq]  ggrroouupp==_n_a_m_e ssuubbggrroouupp==_n_a_m_e ssiiggnnaattuurreeffiillee==_n_a_m_e
       [bblloocckkssiizzee==_v_a_l_u_e] oouuttppuutt==_n_a_m_e

DDEESSCCRRIIPPTTIIOONN
       The _i_._s_m_a_p 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 param-
       eters.


       _i_._s_m_a_p 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  likeli-
       hood (ML) classification which classifies each pixel sepa-
       rately, but requires somewhat less computation. This  mode
       is selected with the --mm flag (see below).

OOPPTTIIOONNSS
   FFllaaggss::
       --mm     Use   maximum  likelihood  estimation  (instead  of
	      smap).  Normal operation is to use SMAP  estimation
	      (see NOTES).

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


   PPaarraammeetteerrss::
       ggrroouupp==_n_a_m_e
	      imagery group

	      The  imagery  group  that  defines  the image to be
	      classified.

       ssuubbggrroouupp==_n_a_m_e
	      imagery subgroup

	      The subgroup within the group specified that speci-
	      fies  the  subset  of the band files that are to be
	      used as image data to be classified.

       ssiiggnnaattuurreeffiillee==_n_a_m_e
	      imagery signaturefile

	      The signature file that contains the spectral  sig-
	      natures  (i.e.,  the statistics) for the classes to
	      be identified in the image.  This signature file is
	      produced by the program _i_._g_e_n_s_i_g_s_e_t (see NOTES).

       bblloocckkssiizzee==_v_a_l_u_e
	      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 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 depend-
       ing 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.

       oouuttppuutt==_n_a_m_e
	      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.


IINNTTEERRAACCTTIIVVEE MMOODDEE
       If  none  of  the  arguments  are specified on the command
       line, _i_._s_m_a_p will interactively prompt for  the	names  of
       the maps and files.

NNOOTTEESS
       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 course scale segmentations to	guide  the  finer
       scale  segmentations.   In addition to reducing the number
       of misclassifications, the SMAP algorithm  generally  pro-
       duces  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 segmenta-
       tion  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.

RREEFFEERREENNCCEESS
	      C. Bouman and M. Shapiro, "Multispectral Image Seg-
	      mentation using a Multiscale Image Model," _P_r_o_c_. _o_f
	      _I_E_E_E _I_n_t_'_l _C_o_n_f_. _o_n _A_c_o_u_s_t_._, _S_p_e_e_c_h _a_n_d _S_i_g_. _P_r_o_c_._,
	      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,_"  _I_E_E_E
	      _T_r_a_n_s_. _o_n _I_m_a_g_e _P_r_o_c_e_s_s_i_n_g_._, _3_(_2_)_, _1_6_2_-_1_7_7_"

	      McCauley,  J.D. and B.A. Engel 1995, "Comparison of
	      Scene Segmentations: SMAP, ECHO and Maximum Likely-
	      hood,_"  _I_E_E_E  _T_r_a_n_s_. _o_n _G_e_o_s_c_i_e_n_c_e _a_n_d _R_e_m_o_t_e _S_e_n_s_-
	      _i_n_g_, _3_3_(_6_)_: _1_3_1_3_-_1_3_1_6_.


SSEEEE AALLSSOO
       _i_._g_r_o_u_p for creating groups and subgroups


       _i_._g_e_n_s_i_g_s_e_t to generate the  signature  file  required  by
       this program

AAUUTTHHOORRSS
       Charles	Bouman,  School of Electrical Engineering, Purdue
       University
       Michael	 Shapiro,   U.S.Army   Construction   Engineering
       Research Laboratory

       _L_a_s_t _c_h_a_n_g_e_d_: _$_D_a_t_e_: _2_0_0_2_/_0_1_/_2_5 _0_5_:_4_5_:_3_3 _$



GRASS 5.0						i.smap(1)
