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 ssiigg--
       nnaattuurreeffiillee==_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 pix-
       els  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
       classification  of the pixels in the training map and make
       these signatures available 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 pixels 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 specifi-
       cally 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  comprise the image to be analyzed. The _i_._g_r_o_u_p com-
       mand is used to construct 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 mecha-
       nism 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 covariance 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 con-
       tains one or more subsignatures (represeting  subclasses).
       The algorithm in this program starts with a maximum number
       of subclasses and reduces this number to a minimal  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 distri-
       bution.	 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 sub-
       sequent	segmentation (i.e., classification) of the multi-
       spectral 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, forest, grasslands
       or urban areas are examples of information classes that	a
       user  may  wish to separate in an image.  However, each of
       these information 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 segmenta-
       tion 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	provided accurate
       training data.


       This clustering algorithm estimates  both  the  number  of
       distinct  subclasses  in each class, and the spectral mean
       and covariance for each	subclass.   The  number  of  sub-
       classes	is estimated using Rissanen's minimum 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  Like-
	      lihood  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, Maxi-
	      mum 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 train-
       ing 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)
