r.texture(1)

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 [-sa] 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)

-a
    Calculate all textural measurements

--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)
    Default: 1

method=string[,string,...]
    Textural measurement method
    Options: asm, contrast, corr, var, idm, sa, se, sv, 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 for a distance (default = 1).

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 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), and writes out by default the average over all angles for each measure. Optionally, using flag -s the output consists of four images for each textural feature, one for every direction (0, 45, 90, 135).

The user must carefully set the resolution (using g.region) before running this program, or the computer may run out of memory.

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.

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.

KNOWN ISSUES
The program can run incredibly slow for large raster maps.

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: 2015-12-03 17:34:44 +0100 (Thu, 03 Dec 2015) $

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