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



NNAAMMEE
       ii..ssmmaapp	- An imagery function that performs contextual image classifi-
       cation 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   [bblloocckk--
       ssiizzee==_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 Gaus-
       sian distributions, this program may also be used to segment multispec-
       tral images based on simple spectral mean and covariance parameters.


       _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 likelihood (ML)  clas-
       sification  which  classifies each pixel separately, but requires some-
       what 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  program  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 specifies the  sub-
	      set  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 signatures	(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	influ-
       encing  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 sub-
       tle 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  qualita-
       tively  different behavior, (e.g., natural woodlands and man-made agri-
       cultural 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 segmenta-
       tion 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  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 depen-
       dent  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 exces-
       sively large regions are not formed.

RREEFFEERREENNCCEESS
	      C. Bouman and  M.  Shapiro,  "Multispectral  Image  Segmentation
	      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 Fran-
	      cisco, 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 Segmen-
	      tations: SMAP, ECHO and Maximum Likelyhood,_" _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)
