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


       raster, algebra, statistics, texture


       r.texture --help
       r.texture    [-san]    input=name    output=basename     [size=value]     [distance=value]
       [method=string[,string,...]]   [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

           Separate output for each angle (0, 45, 90, 135)
           Angles are counterclockwise from east:  0  is  East  to  West,  45  is  North-East  to

           Calculate all textural measurements

           Allow NULL cells in a moving window
           This will also avoid cropping along edges of the current region

           Allow output files to overwrite existing files

           Print usage summary

           Verbose module output

           Quiet module output

           Force launching GUI dialog

       input=name [required]
           Name of input raster map

       output=basename [required]
           Name for output basename raster map(s)

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

           The distance between two samples (>= 1)
           The distance must be smaller than the size of the moving window
           Default: 1

           Textural measurement method
           Options: asm, contrast, corr, var, idm, sa, sv, se, entr, dv, de, moc1, moc2


       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.

       In  order  to  take into account the scale of the texture to be measured, r.texture allows
       the user to define the size of the moving window and the  distance  at  which  to  compare
       pixel  grey  values.   By default the module averages the results over the 4 orientations,
       but the user can also request output of the texture variables in 4 different  orientations
       (flag  -s).  Please  note  that  angles  are  defined in degrees of east and they increase
       counterclockwise, so 0 is East - West, 45 is North-East - South-West, 90 is North - South,
       135 is North-West - South-East.

       The  user  can  either  chose  one  or  several  texture  measures  (see  below  for their
       description) using the method parameter, or can request  the  creating  of  maps  for  all
       available methods with the -a.

       r.texture  assumes grey levels ranging from 0 to 255 as input.  The input is automatically
       rescaled to 0 to 255 if the input map range is outside of this range.  In order to  reduce
       noise  in  the input data (thus generally reinforcing the textural features), and to speed
       up processing, it is recommended that the user recode  the  data  using  equal-probability
       quantization.   Quantization  rules for r.recode can be generated with r.quantile -r using
       e.g 16 or 32 quantiles (see example below).


       Texture is a feature  of  specific  land  cover  classes  in  satellite  imagery.   It  is
       particularly  useful  in  situations where spectral differences between classes are small,
       but classes are distinguishable by  their  organisation  on  the  ground,  often  opposing
       natural  to  human-made  spaces:  cultivated  fields vs meadows or golf courses, palm tree
       plantations vs natural rain forest, but texture can  also  be  a  natural  phenomen:  dune
       fields,  different  canopies  due  to  different  tree  species. The usefulness and use of
       texture is highly dependent on the resolution of satellite imagery and on the scale of the
       human  intervention or the phenomenon that created the texture (also see the discussion of
       scale dependency below). The user should observe  the  phenomenon  visually  in  order  to
       determine an adequat setting of the size parameter.

       The output of r.texture can constitute very useful additional variables as input for image
       classification or image segmentation (object recognition).  It can be used  in  supervised
       classification algorithms such as i.maxlik or i.smap, or for the identification of objects
       in i.segment, and/or for the characterization of these objects and thus, for  example,  as
       one of the raster inputs of the i.segment.stats addon.

       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  uses  the  common texture model based on the so-called grey level co-occurrence
       matrix as described by Haralick et al (1973). 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 the Haralick et al 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 uniform).

           ·   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

           ·   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)

       The  computational region should be set to the input map with g.region raster=<input map>,
       or aligned to the input map with g.region align=<input map> if only a subregion should  be

       Note  that  the output of r.texture will always be smaller than the current region as only
       cells for which there are no null cells and for which all cells of the moving  window  are
       within the current region will contain a value. The output will thus appear cropped at the

       Importantly, the input raster map cannot have more than 255 categories.


       Calculation of Angular Second Moment of B/W orthophoto (North Carolina data set):
       g.region raster=ortho_2001_t792_1m -p
       # set grey level color table 0% black 100% white
       r.colors ortho_2001_t792_1m color=grey
       # extract grey levels
       r.mapcalc "ortho_2001_t792_1m.greylevel = ortho_2001_t792_1m"
       # texture analysis
       r.texture ortho_2001_t792_1m.greylevel prefix=ortho_texture method=asm -s
       # display
       g.region n=221461 s=221094 w=638279 e=638694
       d.shade color=ortho_texture_ASM_0 shade=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.  Reducing the number of
       gray levels (equal-probability quantizing):
       g.region -p raster=ortho_2001_t792_1m
       # enter as one line or with \
       r.quantile input=ortho_2001_t792_1m quantiles=16 -r | r.recode \
                  input=ortho_2001_t792_1m output=ortho_2001_t792_1m_q16 rules=-
       The recoded raster map can then be used as input for r.texture as before.

       Second example: analysis of IDM (homogeneity) on a simple  raster  with  North-South  line
       # import raster in=- output=lines << EOF
       north: 9
       south: 0
       east: 9
       west: 0
       rows: 9
       cols: 9
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       0 0 0 1 0 0 0 1 0
       # adjust region to raster
       g.region raster=lines
       # calculate IDM (homogeneity) in all directions
       r.texture -s lines method=idm output=text_lines

       The  following  image  shows  the  original map, the result in East-West direction and the
       result in North-South direction, showing how texture can depend on direction, with texture
       perfectly  homogeneous  (value=1) in the North-South direction, but quite heterogeneous in
       East-West direction, except for those areas where there are three columns of equal  values
       (as  size=3).   The  overlaid  grid  highlights  that the texture measures output maps are
       cropped at the margins.
       IDM textures according to direction


       The program can run incredibly slow for large raster maps and large moving  windows  (size


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

       The  original  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.   Over  the  years,  the source code of
       r.texture was further improved.

           ·   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

           ·   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.maxlik, i.gensig, i.smap, i.gensigset, i.segment.stats, 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
       Markus Metz (correction and optimization of the initial version)
       Moritz Lennert (documentation)

       Last changed: $Date: 2018-10-18 21:05:15 +0200 (Thu, 18 Oct 2018) $


       Available at: r.texture source code (history)

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