Provided by: grass-doc_7.8.2-1build3_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 [-san] 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)
Angles are counterclockwise from east: 0 is East to West, 45 is North-East to South-West
-a
Calculate all textural measurements
-n
Allow NULL cells in a moving window
This will also avoid cropping along edges of the current region
--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)
The distance must be smaller than the size of the moving window
Default: 1
method=string[,string,...]
Textural measurement method
Options: asm, contrast, corr, var, idm, sa, sv, se, 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.
In order to take into account the scale of the texture to be measured, r.texture allows the user to
define the size of the moving window and the distance at which to compare pixel grey values. By default
the module averages the results over the 4 orientations, but the user can also request output of the
texture variables in 4 different orientations (flag -s). Please note that angles are defined in degrees
of east and they increase counterclockwise, so 0 is East - West, 45 is North-East - South-West, 90 is
North - South, 135 is North-West - South-East.
The user can either chose one or several texture measures (see below for their description) using the
method parameter, or can request the creating of maps for all available methods with the -a.
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 order to reduce noise in the input data (thus
generally reinforcing the textural features), and to speed up processing, it is recommended that the user
recode the data using equal-probability quantization. Quantization rules for r.recode can be generated
with r.quantile -r using e.g 16 or 32 quantiles (see example below).
NOTES
Texture is a feature of specific land cover classes in satellite imagery. It is particularly useful in
situations where spectral differences between classes are small, but classes are distinguishable by their
organisation on the ground, often opposing natural to human-made spaces: cultivated fields vs meadows or
golf courses, palm tree plantations vs natural rain forest, but texture can also be a natural phenomen:
dune fields, different canopies due to different tree species. The usefulness and use of texture is
highly dependent on the resolution of satellite imagery and on the scale of the human intervention or the
phenomenon that created the texture (also see the discussion of scale dependency below). The user should
observe the phenomenon visually in order to determine an adequat setting of the size parameter.
The output of r.texture can constitute very useful additional variables as input for image classification
or image segmentation (object recognition). It can be used in supervised classification algorithms such
as i.maxlik or i.smap, or for the identification of objects in i.segment, and/or for the characterization
of these objects and thus, for example, as one of the raster inputs of the i.segment.stats addon.
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 uses the common texture model based on the so-called grey level co-occurrence matrix as
described by Haralick et al (1973). 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 the Haralick et al 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)
The computational region should be set to the input map with g.region raster=<input map>, or aligned to
the input map with g.region align=<input map> if only a subregion should be analyzed.
Note that the output of r.texture will always be smaller than the current region as only cells for which
there are no null cells and for which all cells of the moving window are within the current region will
contain a value. The output will thus appear cropped at the margins.
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. Reducing the number of gray levels
(equal-probability quantizing):
g.region -p raster=ortho_2001_t792_1m
# enter as one line or with \
r.quantile input=ortho_2001_t792_1m quantiles=16 -r | r.recode \
input=ortho_2001_t792_1m output=ortho_2001_t792_1m_q16 rules=-
The recoded raster map can then be used as input for r.texture as before.
Second example: analysis of IDM (homogeneity) on a simple raster with North-South line pattern.
# import raster
r.in.ascii in=- output=lines << EOF
north: 9
south: 0
east: 9
west: 0
rows: 9
cols: 9
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
0 0 0 1 0 0 0 1 0
EOF
# adjust region to raster
g.region raster=lines
# calculate IDM (homogeneity) in all directions
r.texture -s lines method=idm output=text_lines
The following image shows the original map, the result in East-West direction and the result in
North-South direction, showing how texture can depend on direction, with texture perfectly homogeneous
(value=1) in the North-South direction, but quite heterogeneous in East-West direction, except for those
areas where there are three columns of equal values (as size=3). The overlaid grid highlights that the
texture measures output maps are cropped at the margins.
IDM textures according to direction
KNOWN ISSUES
The program can run incredibly slow for large raster maps and large moving windows (size option).
REFERENCES
The algorithm was implemented after Haralick et al., 1973 and 1979.
The original 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. Over the years, the source code of r.texture was further improved.
• 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.maxlik, i.gensig, i.smap, i.gensigset, i.segment.stats, 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
Markus Metz (correction and optimization of the initial version)
Moritz Lennert (documentation)
SOURCE CODE
Available at: r.texture source code (history)
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