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
SEE ALSO¶
i.smap, i.gensigset, i.pca, r.neighbors,
r.rescale
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 15:07:24 +0100 (Sun, 27 Nov 2011) $
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