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



NNAAMMEE
       _i_._g_e_n_s_i_g_s_e_t  - generate statistics for _i_._s_m_a_p from raster map layer.
       _(_G_R_A_S_S _I_m_a_g_e_r_y _P_r_o_g_r_a_m_)

SSYYNNOOPPSSIISS
       ii..ggeennssiiggsseett
       ii..ggeennssiiggsseett hheellpp
       ii..ggeennssiiggsseett   ttrraaiinniinnggmmaapp==_n_a_m_e	ggrroouupp==_n_a_m_e   ssuubbggrroouupp==_n_a_m_e  ssiiggnnaattuurree--
       ffiillee==_n_a_m_e [mmaaxxssiigg==_v_a_l_u_e]

DDEESSCCRRIIPPTTIIOONN
       _i_._g_e_n_s_i_g_s_e_t is a  non-interactive  method  for  generating  input  into
       _i_._s_m_a_p_.	 It is used as the first pass in the a two-pass classification
       process.  It reads a raster map layer, called the training  map,  which
       has some of the pixels or regions already classified.  _i_._g_e_n_s_i_g_s_e_t will
       then extract spectral signatures from an image based on the classifica-
       tion of the pixels in the training map and make these signatures avail-
       able to _i_._s_m_a_p_.


       The user would then execute the GRASS  program  _i_._s_m_a_p  to  create  the
       final classified map.

OOPPTTIIOONNSS
   PPaarraammeetteerrss
       ttrraaiinniinnggmmaapp==_n_a_m_e
	      ground truth training map


       This  raster layer, supplied as input by the user, has some of its pix-
       els already classified, and the rest  (probably	most)  of  the	pixels
       unclassified.  Classified means that the pixel has a non-zero value and
       unclassified means that the pixel has a zero value.


       This map must be prepared by the user in advance.  The  user  must  use
       _r_._d_i_g_i_t,  a  combination  of  _v_._d_i_g_i_t  and  _v_._t_o_._r_a_s_t,  or  some  other
       import/developement process (e.g., _v_._i_n_._t_r_a_n_s_e_c_t_s_) to define the  areas
       representative of the classes in the image.


       At  present,  there  is no fully-interactive tool specifically designed
       for producing this layer.

       ggrroouupp==_n_a_m_e
	      imagery group


       This is the name of the group that contains the band files  which  com-
       prise  the  image  to  be analyzed. The _i_._g_r_o_u_p command is used to con-
       struct groups of raster layers which comprise an image.



       ssuubbggrroouupp==_n_a_m_e
	      subgroup containing image files


       This names the subgroup within the group that selects a subset  of  the
       bands  to be analyzed. The _i_._g_r_o_u_p command is also used to prepare this
       subgroup.  The subgroup mechanism allows the user to select a subset of
       all the band files that form an image.

       ssiiggnnaattuurreeffiillee==_n_a_m_e
	      resultant signature file


       This  is the resultant signature file (containing the means and covari-
       ance matrices) for each class in the training map  that	is  associated
       with the band files in the subgroup selected.



       mmaaxxssiigg==_v_a_l_u_e
	      maximum number of sub-signatures in any class
	      default: 10


       The  spectral signatures which are produced by this program are "mixed"
       signatures (see NOTES).	Each signature contains one or more  subsigna-
       tures  (represeting  subclasses).  The algorithm in this program starts
       with a maximum number of subclasses and reduces this number to a  mini-
       mal  number  of subclasses which are spectrally distinct.  The user has
       the option to set this starting value with this option.


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


       It  should  be  noted that interactive mode here only means interactive
       prompting for maps and files.  It does not mean	visualization  of  the
       signatures that result from the process.



NNOOTTEESS
       The  algorithm  in  _i_._g_e_n_s_i_g_s_e_t determines the parameters of a spectral
       class model known as a Gaussian mixture distribution.   The  parameters
       are  estimated  using multispectral image data and a training map which
       labels the class of a subset of the image pixels.   The	mixture  class
       parameters are stored as a class signature which can be used for subse-
       quent segmentation (i.e., classification) of the multispectral image.


       The Gaussian mixture class is a useful model because it can be used  to
       describe  the  behavior	of  an information class which contains pixels
       with a variety of distinct spectral characteristics.  For example, for-
       est, grasslands or urban areas are examples of information classes that
       a user may wish to separate in an image.  However, each of these infor-
       mation  classes	may  contain  subclasses each with its own distinctive
       spectral characteristic.  For example, a forest may contain  a  variety
       of different tree species each with its own spectral behavior.


       The objective of mixture classes is to improve segmentation performance
       by modeling each information class as a probabilistic  mixture  with  a
       variety	of  subclasses.  The mixture class model also removes the need
       to perform an initial unsupervised segmentation	for  the  purposes  of
       identifying  these  subclasses.	 However, if misclassified samples are
       used in the training process, these erroneous samples may be grouped as
       a separate undesired subclass.  Therefore, care should be taken to pro-
       vided accurate training data.


       This clustering algorithm estimates both the number  of	distinct  sub-
       classes	in  each  class, and the spectral mean and covariance for each
       subclass.  The number of subclasses is estimated using Rissanen's mini-
       mum  description  length (MDL) criteria [1].  This criteria attempts to
       determine the number of subclasses which "best" describe the data.  The
       approximate  maximum likelihood estimates of the mean and covariance of
       the subclasses are computed using  the  expectation  maximization  (EM)
       algorithm [3].

RREEFFEERREENNCCEESS
	      J.  Rissanen,  "A Universal Prior for Integers and Estimation by
	      Minimum Description Length," _A_n_n_a_l_s _o_f _S_t_a_t_i_s_t_i_c_s_, vol. 11,  no.
	      2, pp. 417-431, 1983.

	      A.  Dempster,  N.  Laird	and D. Rubin, "Maximum Likelihood from
	      Incomplete Data via the EM Algorithm," _J_. _R_o_y_. _S_t_a_t_i_s_t_. _S_o_c_.  _B_,
	      vol. 39, no. 1, pp. 1-38, 1977.

	      E.  Redner and H. Walker, "Mixture Densities, Maximum Likelihood
	      and the EM Algorithm," _S_I_A_M _R_e_v_i_e_w_, vol. 26, no. 2, April  1984.


SSEEEE AALLSSOO
       _i_._g_r_o_u_p for creating groups and subgroups


       _v_._d_i_g_i_t and _r_._d_i_g_i_t for interactively creating the training map.


       _i_._s_m_a_p  for  creating  a final classification layer from the signatures
       generated by _i_._g_e_n_s_i_g_s_e_t_.

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.gensigset(1)
