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

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Provided by: grass-doc_7.0.3-1build1_all

NAME

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

KEYWORDS

       raster, algebra, statistics, texture

SYNOPSIS

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

   Flags:
       -s
           Separate output for each angle (0, 45, 90, 135)

       -a
           Calculate all textural measurements

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       input=name [required]
           Name of input raster map

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

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

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

       method=string[,string,...]
           Textural measurement method
           Options: asm, contrast, corr, var, idm, sa, se, sv, entr, dv, de, moc1, moc2

DESCRIPTION

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 is automatically rescaled to 0
to 255 if the input map range is outside of this range.

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), and writes out by default the average over all angles for each
measure. Optionally, using flag -s the output consists of four images for each textural feature, one for
every direction (0, 45, 90, 135).

The user must carefully set the resolution (using g.region) before running this program, or the computer
may run out of memory.

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 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 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)

NOTES

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

EXAMPLE

       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.

KNOWN ISSUES

       The program can run incredibly slow for large raster maps.

REFERENCES

       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, SMC-3(6):610-621.

           •   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

AUTHORS

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

       Last changed: $Date: 2015-12-03 17:34:44 +0100 (Thu, 03 Dec 2015) $

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