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

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) $

       Full index

       © 2003-2013 GRASS Development Team