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 spectral  signature  informa-
       tion 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 classification.


       Either image classification methods are performed in  two  steps.   The
       first  step  in	an  unsupervised  image classification 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
       classification 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 sig-
       nature 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 gener-
       ated 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  spe-
       cific land cover types on the ground.


       The  program will run non-interactively if the user specifies the names
       of raster map layers, i.e., group and subgroup  names,  seed  signature
       file name, result classification 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 out-
	      put.


   PPaarraammeetteerrss::
       ggrroouupp==_n_a_m_e
	      The imagery group contains the subgroup to be classified.

       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  classification.
	      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 spectral  sig-
	      natures  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 categories 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  dis-
	      criminant  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 spectral 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, however, 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 without 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, Illinois

       _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)
