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  computer  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.  Tex-
       tural  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  Agricul-
       tural  Experiment Station, employer for hire of James Dar-
       rell McCauley).

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


       The method for finding (14) the maximal correlation  coef-
       ficient,  which requires finding the second largest eigen-
       value of a matrix Q, does not always converge.


       It would be interesting to write raster files to map  fea-
       tures for neighborhoods, with some sort of quantization to
       record category values.	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)
