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<h2>NAME</h2>
<em><b>r.texture</b></em> - Generate images with textural features from a raster map.
<h2>KEYWORDS</h2>
raster
<h2>SYNOPSIS</h2>
<b>r.texture</b><br>
<b>r.texture help</b><br>
<b>r.texture</b> [-<b>qackviswxedpmno</b>] <b>input</b>=<em>name</em> <b>prefix</b>=<em>string</em> [<b>size</b>=<em>value</em>] [<b>distance</b>=<em>value</em>] [--<b>overwrite</b>] [--<b>verbose</b>] [--<b>quiet</b>]
<h3>Flags:</h3>
<DL>
<DT><b>-q</b></DT>
<DD>Quiet</DD>
<DT><b>-a</b></DT>
<DD>Angular Second Moment</DD>
<DT><b>-c</b></DT>
<DD>Contrast</DD>
<DT><b>-k</b></DT>
<DD>Correlation</DD>
<DT><b>-v</b></DT>
<DD>Variance</DD>
<DT><b>-i</b></DT>
<DD>Inverse Diff Moment</DD>
<DT><b>-s</b></DT>
<DD>Sum Average</DD>
<DT><b>-w</b></DT>
<DD>Sum Variance</DD>
<DT><b>-x</b></DT>
<DD>Sum Entropy</DD>
<DT><b>-e</b></DT>
<DD>Entropy</DD>
<DT><b>-d</b></DT>
<DD>Difference Variance</DD>
<DT><b>-p</b></DT>
<DD>Difference Entropy</DD>
<DT><b>-m</b></DT>
<DD>Measure of Correlation-1</DD>
<DT><b>-n</b></DT>
<DD>Measure of Correlation-2</DD>
<DT><b>-o</b></DT>
<DD>Max Correlation Coeff</DD>
<DT><b>--overwrite</b></DT>
<DD>Allow output files to overwrite existing files</DD>
<DT><b>--verbose</b></DT>
<DD>Verbose module output</DD>
<DT><b>--quiet</b></DT>
<DD>Quiet module output</DD>
</DL>
<h3>Parameters:</h3>
<DL>
<DT><b>input</b>=<em>name</em></DT>
<DD>Name of input raster map</DD>
<DT><b>prefix</b>=<em>string</em></DT>
<DD>Prefix for ouput raster map(s)</DD>
<DT><b>size</b>=<em>value</em></DT>
<DD>The size of sliding window (odd and >= 3)</DD>
<DD>Default: <em>3</em></DD>
<DT><b>distance</b>=<em>value</em></DT>
<DD>The distance between two samples (>= 1)</DD>
<DD>Default: <em>1</em></DD>
</DL>
<H2>DESCRIPTION</H2>
<em>r.texture</em> - 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 <em>distance</em> (default = 1).
<P>
<!--
<em>r.texture</em> uses the algorithms of <a href="i.texture.html">i.texture</a>.
-->
<em>r.texture</em> 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
<a href="g.region.html">g.region</a>) 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.</p>
<P>
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.
<P>
The following are brief explanations of texture measures:
<p>
<ul>
<li> Angular Second Moment:
This is a measure of local homogeneity and the opposite of Entropy.
It is high when the local window a few pixels with high values; low,
when the pixels are almost equal.
<li> Contrast:
This measure considers the amount of local variation and is the opposite of Homogeneity
(when high pixel values concentrate along the diagonal).
<li> Correlation:
This measure analyses the linear dependency of grey levels of neighboring
pixels. Typically high, when the scale of local texture is larger than the
<em>distance</em>.
<li> Entropy:
This measure is high when the values of the local 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).
</ul>
<h2>NOTES</h2>
Algorithm taken from:<br>
Haralick, R.M., K. Shanmugam, and I. Dinstein. 1973. Textural features for
image classification. <em>IEEE Transactions on Systems, Man, and
Cybernetics</em>, SMC-3(6):610-621.
<p>The code was taken by permission from <em>pgmtexture</em>, part of
PBMPLUS (Copyright 1991, Jef Poskanser and Texas Agricultural Experiment
Station, employer for hire of James Darrell McCauley). <br>
Man page of <a href="http://netpbm.sourceforge.net/doc/pgmtexture.html">pgmtexture</a></p>
<h2>BUGS</h2>
- The program can run incredibly slow for large raster maps.
<p>
- The method for finding the maximal correlation coefficient, which
requires finding the second largest eigenvalue of a matrix Q, does not
always converge.
<h2>REFERENCES</h2>
<b>Haralick, R.M., K. Shanmugam, and I. Dinstein</b> (1973). Textural features for
image classification. <em>IEEE Transactions on Systems, Man, and
Cybernetics</em>, SMC-3(6):610-621.
<P>
<b>Bouman C. A., Shapiro M.</b>,(March
1994).A Multiscale Random Field Model for Bayesian Image
Segmentation, IEEE Trans. on Image Processing, vol. 3, no.2.
<p>
<b>Haralick R.</b>, (May 1979). <i>Statistical and structural approaches to texture</i>,
Proceedings of the IEEE, vol. 67, No.5, pp. 786-804</p>
<h2>SEE ALSO</h2>
<em><a href="i.smap.html">i.smap</a></em>,
<em><a href="i.gensigset.html">i.gensigset</a></em>,
<em><a href="i.pca.html">i.pca</a></em>,
<em><a href="r.digit.html">r.digit</a></em>,
<em><a href="i.group.html">i.group</a></em>
<!-- <em><a href="i.texture.html">i.texture</a></em> -->
<h2>AUTHOR</h2>
<a href="mailto:antoniol@ieee.org">G. Antoniol</a> - RCOST (Research Centre on Software Technology - Viale Traiano - 82100 Benevento)<br>
<a href="mailto:basco@unisannio.it">C. Basco</a> - RCOST (Research Centre on Software Technology - Viale Traiano - 82100 Benevento)<br>
<a href="mailto:ceccarelli@unisannio.it">M. Ceccarelli</a> - Facolta di Scienze, Universita del Sannio, Benevento
<p><i>Last changed: $Date: 2007-07-13 16:18:35 +0200 (Fri, 13 Jul 2007) $</i></p>
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