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       r.texture  - Generate images with textural features from a raster map.


       raster, statistics


       r.texture help
       r.texture  [-qackviswxedpmno]  input=name  prefix=string   [size=value]   [distance=value]
       [--overwrite]  [--verbose]  [--quiet]


           Angular Second Moment




           Inverse Diff Moment

           Sum Average

           Sum Variance

           Sum Entropy


           Difference Variance

           Difference Entropy

           Measure of Correlation-1

           Measure of Correlation-2

           Max Correlation Coeff

           Allow output files to overwrite existing files

           Verbose module output

           Quiet module output

           Name of input raster map

           Prefix for output raster map(s)

           The size of sliding window (odd and >= 3)
           Default: 3

           The distance between two samples (>= 1)
           Default: 1


       r.texture creates raster maps with textural features  from  a  user-specified  raster  map
       layer.  The module calculates textural features based on spatial dependence matrices at 0,
       45, 90, and 135 degrees for a distance (default = 1).

       r.texture assumes grey levels ranging from 0 to  255  as  input.   The  input  has  to  be
       rescaled  to  0 to 255 beforehand if the input map range is outside of this range by using

       In general, several variables  constitute  texture:  differences  in  grey  level  values,
       coarseness  as  scale  of  grey  level differences, presence or lack of directionality and
       regular patterns. A texture can be characterized by tone (grey level intensity properties)
       and  structure  (spatial  relationships).  Since  textures  are  highly  scale  dependent,
       hierarchical textures may occur.

       r.texture reads a GRASS raster map as input and  calculates  textural  features  based  on
       spatial   dependence   matrices  for  north-south,  east-west,  northwest,  and  southwest
       directions using a side by side neighborhood (i.e., a distance of 1). The user  should  be
       sure  to carefully set the resolution (using g.region) before running this program, or the
       computer may run out of memory. The output consists into four  images  for  each  textural
       feature, one for every direction.

       A  commonly  used texture model is based on the so-called grey level co-occurrence matrix.
       This matrix is a two-dimensional histogram of grey levels for a pair of pixels  which  are
       separated  by a fixed spatial relationship.  The matrix approximates the joint probability
       distribution of a pair of pixels.  Several texture measures are directly computed from the
       grey level co-occurrence matrix.

       The following part offers brief explanations of texture measures (after Jensen 1996).

   First-order statistics in the spatial domain
                      Sum Average (SA)

                      Entropy  (ENT):  This  measure analyses the randomness. It is high when the
                     values of the moving window have similar values. It is low when  the  values
                     are  close  to  either  0 or 1 (i.e. when the pixels in the local window are

                      Difference Entropy (DE)

                      Sum Entropy (SE)

                      Variance (VAR): A measure of gray tone variance within  the  moving  window
                     (second-order moment about the mean)

                      Difference Variance (DV)

                      Sum Variance (SV)
       Note  that  measures "mean", "kurtosis", "range", "skewness", and "standard deviation" are
       available in r.neighbors.

   Second-order statistics in the spatial domain
       The second-order statistics texture model  is  based  on  the  so-called  grey  level  co-
       occurrence matrices (GLCM; after Haralick 1979).

                      Angular  Second  Moment (ASM, also called Uniformity): This is a measure of
                     local homogeneity and the opposite of Entropy.  High  values  of  ASM  occur
                     when the pixels in the moving window are very similar.
                     Note: The square root of the ASM is sometimes used as a texture measure, and
                     is called Energy.

                      Inverse Difference Moment (IDM,  also  called  Homogeneity):  This  measure
                     relates  inversely  to  the  contrast measure. It is a direct measure of the
                     local homogeneity of a digital image. Low values  are  associated  with  low
                     homogeneity and vice versa.

                      Contrast  (CON):  This  measure  analyses the image contrast (locally gray-
                     level variations) as the linear dependency of  grey  levels  of  neighboring
                     pixels  (similarity).  Typically  high,  when  the scale of local texture is
                     larger than the distance.

                      Correlation (COR): This measure  analyses the  linear  dependency  of  grey
                     levels  of  neighboring  pixels.  Typically  high,  when  the scale of local
                     texture is larger than the distance.

                      Information Measures of Correlation (MOC)

                      Maximal Correlation Coefficient (MCC)


       Importantly, the input raster map cannot have more than 255 categories. If needed,  a  map
       with more categories can be rescaled using r.rescale.


       Calculation of Angular Second Moment of B/W orthophoto (North Carolina data set):
       g.region rast=ortho_2001_t792_1m -p
       r.texture -a ortho_2001_t792_1m prefix=ortho_texture
       # display
       g.region n=221461 s=221094 w=638279 e=638694
       d.shadedmap drape=ortho_texture_ASM_0 rel=ortho_2001_t792_1m
         This calculates four maps (requested texture at four orientations): ortho_texture_ASM_0,
       ortho_texture_ASM_45, ortho_texture_ASM_90, ortho_texture_ASM_135.


       - The program can run incredibly slow for large raster maps.

       - The method for finding the maximal correlation coefficient, which requires  finding  the
       second  largest  eigenvalue of a matrix Q, does not always converge. This is a known issue
       with this measure in general.


       The algorithm was implemented after Haralick et al., 1973 and 1979.

       The code was taken by permission from pgmtexture, part of  PBMPLUS  (Copyright  1991,  Jef
       Poskanser  and  Texas  Agricultural Experiment Station, employer for hire of James Darrell
       McCauley). Manual page of pgmtexture.

                     Haralick, R.M., K. Shanmugam, and I. Dinstein (1973). Textural features  for
                     image  classification.  IEEE  Transactions on Systems, Man, and Cybernetics,

                     Bouman, C. A., Shapiro, M. (1994).  A  Multiscale  Random  Field  Model  for
                     Bayesian Image Segmentation, IEEE Trans. on Image Processing, vol. 3, no. 2.

                     Jensen,  J.R.  (1996). Introductory digital image processing. Prentice Hall.
                     ISBN 0-13-205840-5

                     Haralick, R. (May 1979). Statistical and structural approaches  to  texture,
                     Proceedings of the IEEE, vol. 67, No.5, pp. 786-804

                     Hall-Beyer, M. (2007). The GLCM Tutorial Home Page (Grey-Level Co-occurrence
                     Matrix texture measurements). University of Calgary, Canada


        i.smap, i.gensigset, i.pca, r.neighbors, r.rescale


       G. Antoniol - RCOST (Research Centre on  Software  Technology  -  Viale  Traiano  -  82100
       C.  Basco  -   RCOST  (Research  Centre  on  Software  Technology  - Viale Traiano - 82100
       M. Ceccarelli - Facolta di Scienze, Universita del Sannio, Benevento

       Last changed: $Date: 2011-11-27 06:07:24 -0800 (Sun, 27 Nov 2011) $

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