
NAME
r.texture - Generate images with textural features from a raster map.
KEYWORDS
raster
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 map raster with textural features for
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).
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.
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). Be
sure to carefully set your resolution (using
g.region) before running this program, or else your
computer could run out of memory. Also, make sure that your raster map has
no more than 255 categories. 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 are brief explanations of texture measures:
- 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 (Contr):
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 (Corr):
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.
- Variance (Var): A measure of gray tone variance within the moving
window (second-order moment about the mean)
- Difference Variance (DV): ...
- Sum Variance (SV): ...
- Sum Average (SA): ...
- Entropy (Entr):
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): ...
- Information Measures of Correlation (MOC): ...
- Maximal Correlation Coefficient (MCC): ...
NOTES
Algorithm taken from:
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.
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).
Man page of pgmtexture
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.
REFERENCES
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.,(March
1994).A Multiscale Random Field Model for Bayesian Image
Segmentation, IEEE Trans. on Image Processing, vol. 3, no.2.
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
SEE ALSO
i.smap,
i.gensigset,
i.pca,
r.digit,
i.group
AUTHOR
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: 2008-11-01 23:51:16 -0800 (Sat, 01 Nov 2008) $
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