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



NNAAMMEE
       ii..tteexxttuurree  - Calculate textural features on a raster map
       _(_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..tteexxttuurree
       ii..tteexxttuurree hheellpp
       ii..tteexxttuurree rraasstt==_n_a_m_e

DDEESSCCRRIIPPTTIIOONN
       Reads  a GRASS raster map as input.  Calculates textural features based
       on spatial dependence matrices for north-south,	east-west,  northwest,
       and  southwest directions using a 3x3 neighborhood (i.e., a distance of
       1).  Writes to standard output.	Be sure to carefully set your  resolu-
       tion  (using  _g_._r_e_g_i_o_n)	before running this program, or else your com-
       puter could run out of memory.  Also, make sure that  your  raster  map
       has no more than 255 categories.

OOPPTTIIOONNSS
   PPaarraammeetteerr::
       rraasstt==_n_a_m_e
	      Raster map name.


NNOOTTEESS
       Textural features include:

	      Angular Second Moment,

	       Contrast,

	       Correlation,

	       Variance,

	       Inverse Difference Moment,

	       Sum Average,

	       Sum Variance,

	       Sum Entropy,

	       Entropy,

	       Difference Variance,

	       Difference Entropy,

	       Information Measure of Correlation,

	       Another Information Measure of Correlation, and

	       Maximal Correlation Coefficient.

       Algorithm taken from:
       Haralick,  R.M., K. Shanmugam, and I. Dinstein. 1973. Textural features
       for image classification.   _I_E_E_E  _T_r_a_n_s_a_c_t_i_o_n_s  _o_n  _S_y_s_t_e_m_s_,  _M_a_n,  _a_n_d
       _C_y_b_e_r_t_i_n_e_t_i_c_s, SMC-3(6):610-621.


       The  code  was  taken  by  permission  from _p_g_m_t_e_x_t_u_r_e, part of PBMPLUS
       (Copyright 1991, Jef Poskanser and Texas Agricultural  Experiment  Sta-
       tion, employer for hire of James Darrell McCauley).

BBUUGGSS
       The program can run incredibly slow for large raster files (larger than
       64 x 64) and command line options are limited.


       The method for finding (14) the maximal correlation coefficient,  which
       requires  finding the second largest eigenvalue of a matrix Q, does not
       always converge.


       It would be interesting to write  raster  files	to  map  features  for
       neighborhoods,  with  some sort of quantization to record category val-
       ues.  This may be useful for image  classification  schemes,  but  this
       exercise is left to the reader (the changes would be fairly trivial).

RREEFFEERREENNCCEESS
       _I_E_E_E _T_r_a_n_s_a_c_t_i_o_n_s _o_n _S_y_s_t_e_m_s_, _M_a_n_, _a_n_d _C_y_b_e_r_t_i_n_e_t_i_c_s, SMC-3(6):610-621.

SSEEEE AALLSSOO
       _g_._r_e_g_i_o_n

       _r_._r_e_c_l_a_s_s


AAUUTTHHOORR
       James Darrell McCauley, Agricultural Engineering, Purdue University

       _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.texture(1)
