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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 ouput 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).

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:

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

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

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: 2007-07-13 07:18:35 -0700 (Fri, 13 Jul 2007) $


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