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

name
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synopsis
description
notes
example
bugs
references
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authors

NAME

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

KEYWORDS

       raster, statistics

SYNOPSIS

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

   Flags:
       -q
           Quiet

       -a
           Angular Second Moment

       -c
           Contrast

       -k
           Correlation

       -v
           Variance

       -i
           Inverse Diff Moment

       -s
           Sum Average

       -w
           Sum Variance

       -x
           Sum Entropy

       -e
           Entropy

       -d
           Difference Variance

       -p
           Difference Entropy

       -m
           Measure of Correlation-1

       -n
           Measure of Correlation-2

       -o
           Max Correlation Coeff

       --overwrite
           Allow output files to overwrite existing files

       --verbose
           Verbose module output

       --quiet
           Quiet module output

   Parameters:
       input=name
           Name of input raster map

       prefix=string
           Prefix for output raster map(s)

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

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

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 has to be rescaled to 0 to 255
beforehand if the input map range is outside of this range by using r.rescale.

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 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. If needed, a map with more
       categories can be rescaled using r.rescale.

EXAMPLE

       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.

BUGS

       - 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.

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

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