Provided by: grass-doc_6.4.3-3_all

**NAME**

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

**KEYWORDS**

raster, statistics

**SYNOPSIS**

r.texturer.texturehelpr.texture[-qackviswxedpmno]input=nameprefix=string[size=value] [distance=value] [--overwrite] [--verbose] [--quiet]Flags:-qQuiet-aAngular Second Moment-cContrast-kCorrelation-vVariance-iInverse Diff Moment-sSum Average-wSum Variance-xSum Entropy-eEntropy-dDifference Variance-pDifference Entropy-mMeasure of Correlation-1-nMeasure of Correlation-2-oMax Correlation Coeff--overwriteAllow output files to overwrite existing files--verboseVerbose module output--quietQuiet module outputParameters:input=nameName of input raster mapprefix=stringPrefix for output raster map(s)size=valueThe size of sliding window (odd and >= 3) Default:3distance=valueThe distance between two samples (>= 1) Default:1

**DESCRIPTION**

r.texturecreates 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 adistance(default = 1).r.textureassumes 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 usingr.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.texturereads 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 (usingg.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-orderstatisticsinthespatialdomainSum 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 inr.neighbors.Second-orderstatisticsinthespatialdomainThe 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 thedistance. 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 thedistance. 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 usingr.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 frompgmtexture, 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.IEEETransactionsonSystems,Man,andCybernetics, 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).Statisticalandstructuralapproachestotexture, 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, BeneventoLastchanged:$Date:2011-11-2706:07:24-0800(Sun,27Nov2011)$Full index © 2003-2013 GRASS Development Team