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



NNAAMMEE
       ii..mmaaxxlliikk   -  An imagery function that classifies the cell
       spectral reflectances in imagery data based on  the  spec-
       tral  signature information generated by either _i_._c_l_u_s_t_e_r,
       _i_._c_l_a_s_s, or _i_._g_e_n_s_i_g.
       _(_G_R_A_S_S _I_m_a_g_e _P_r_o_c_e_s_s_i_n_g _P_r_o_g_r_a_m_)

SSYYNNOOPPSSIISS
       ii..mmaaxxlliikk
       ii..mmaaxxlliikk hheellpp
       ii..mmaaxxlliikk  [--qq]	ggrroouupp==_n_a_m_e   ssuubbggrroouupp==_n_a_m_e   ssiiggffiillee==_n_a_m_e
       ccllaassss==_n_a_m_e [rreejjeecctt==_n_a_m_e]

DDEESSCCRRIIPPTTIIOONN
       _i_._m_a_x_l_i_k  is  a	maximum-likelihood  discriminant analysis
       classifier.  It can be used to perform the second step  in
       either  an  unsupervised or a supervised image classifica-
       tion.


       Either image classification methods are performed  in  two
       steps.	The first step in an unsupervised image classifi-
       cation is performed by _i_._c_l_u_s_t_e_r;  the  first  step  in	a
       supervised classification is executed by the GRASS program
       _i_._c_l_a_s_s. In both cases, the second step in the image clas-
       sification procedure is performed by _i_._m_a_x_l_i_k.


       In  an unsupervised classification, the maximum-likelihood
       classifier uses the cluster means and covariance  matrices
       from  the  _i_._c_l_u_s_t_e_r  signature file to determine to which
       category (spectral class) each cell in the image  has  the
       highest	probability  of  belonging. In a supervised image
       classification, the maximum-likelihood classifier uses the
       region  means  and  covariance  matrices from the spectral
       signature file generated  by  _i_._c_l_a_s_s,  based  on  regions
       (groups	of image pixels) chosen by the user, to determine
       to which category each cell in the image has  the  highest
       probability of belonging.


       In either case, the raster map layer output by _i_._m_a_x_l_i_k is
       a classified image in which each cell has been assigned to
       a spectral class (i.e., a category).  The spectral classes
       (categories) can be related to specific land  cover  types
       on the ground.


       The  program will run non-interactively if the user speci-
       fies the names of raster map layers, i.e., group and  sub-
       group  names, seed signature file name, result classifica-
       tion  file  name,  and  any  combination  of  non-required
       options	in  the command line, using the form ii..mmaaxxlliikk[--qq]
       ggrroouupp==_n_a_m_e    ssuubbggrroouupp==_n_a_m_e    ssiiggffiillee==_n_a_m_e     ccllaassss==_n_a_m_e
       [rreejjeecctt==_n_a_m_e]

       where  each  flag  and  options	have  the meanings stated
       below.


       Alternatively, the user can simply type	_i_._m_a_x_l_i_k  in  the
       command	line  without program arguments. In this case the
       user will be prompted for the program parameter	settings;
       the program will run foreground.

OOPPTTIIOONNSS
   FFllaaggss::
       --qq     Run  quietly,  without printing program messages to
	      standard output.


   PPaarraammeetteerrss::
       ggrroouupp==_n_a_m_e
	      The imagery group contains the subgroup to be clas-
	      sified.

       ssuubbggrroouupp==_n_a_m_e
	      The  subgroup contains image files, which were used
	      to create the signature file in the program _i_._c_l_u_s_-
	      _t_e_r, _i_._c_l_a_s_s, or _i_._g_e_n_s_i_g to be classified.

       ssiiggffiillee==_n_a_m_e
	      The name of the signatures to be used for the clas-
	      sification. The signature file contains the cluster
	      and covariance matrices that were calculated by the
	      GRASS program _i_._c_l_u_s_t_e_r (or the  region  means  and
	      covariance  matrices  generated  by _i_._c_l_a_s_s, if the
	      user runs a supervised classification). These spec-
	      tral  signatures	are what determine the categories
	      (classes) to which image pixels  will  be  assigned
	      during the classification process.

       ccllaassss==_n_a_m_e
	      The  name  of a raster map holds the classification
	      results. This new raster	map  layer  will  contain
	      categories  that can be related to land cover cate-
	      gories on the ground.

       rreejjeecctt==_n_a_m_e
	      The optional name of a raster map holds the  reject
	      threshold  results.  This  is  the  result of a chi
	      square test on each discriminant result at  various
	      threshold levels of confidence to determine at what
	      confidence  level  each  cell  classified  (catego-
	      rized).  It  is the reject threshold map layer, and
	      contains one calculated confidence level	for  each
	      classified cell in the classified image. One of the
	      possible uses for this map layer is as a	mask,  to
	      identify	cells  in  the classified image that have
	      the lowest probability of  being	assigned  to  the
	      correct class.


NNOOTTEESS
       The  maximum-likelihood	classifier assumes that the spec-
       tral signatures for each class  (category)  in  each  band
       file  are normally distributed (i.e., Gaussian in nature).
       Algorithms, such as _i_._c_l_u_s_t_e_r, _i_._c_l_a_s_s, or _i_._g_e_n_s_i_g,  how-
       ever, can create signatures that are not valid distributed
       (more likely with _i_._c_l_a_s_s_)_.  If this occurs, _i_._m_a_x_l_i_k will
       reject them and display a warning message.


       This program runs interactively if the user types _i_._m_a_x_l_i_k
       only. If the user types _i_._m_a_x_l_i_k along with  all  required
       options, it will overwrite the classified raster map with-
       out prompting if this map existed.

SSEEEE AALLSSOO
       _G_R_A_S_S _T_u_t_o_r_i_a_l_: _I_m_a_g_e _P_r_o _c_e_s_s_i_n_g


       _i_._c_l_a_s_s

       _i_._c_l_u_s_t_e_r

       _i_._g_e_n_s_i_g

       _i_._g_r_o_u_p

       _r_._m_a_s_k

AAUUTTHHOORRSS
       Michael	 Shapiro,   U.S.Army   Construction   Engineering
       Research Laboratory
       Tao Wen, University of Illinois at Urbana-Champaign, Illi-
       nois

       _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.maxlik(1)
