Geografic-Information-System/raster/r.texture/r.texture.html

<h2>DESCRIPTION</h2>

<em>r.texture</em> 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 <em>distance</em> (default = 1).
<p>
<em>r.texture</em> 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.
<p>
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.
<p>
<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). The user
should be sure to carefully set the resolution (using <em>g.region</em>) 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.

<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 part offers brief explanations of texture measures (after
Jensen 1996).

<h3>First-order statistics in the spatial domain</h3>
<ul>
<li> Sum Average (SA)</li>

<li> 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).</li>

<li> Difference Entropy (DE)</li>

<li> Sum Entropy (SE)</li>

<li> Variance (VAR):
  A measure of gray tone variance within the moving window (second-order
moment about the mean)</li>

<li> Difference Variance (DV)</li>

<li> Sum Variance (SV)</li>
</ul>

Note that measures "mean", "kurtosis", "range", "skewness", and "standard
deviation" are available in <em>r.neighbors</em>.

<h3>Second-order statistics in the spatial domain</h3>

The second-order statistics texture model is based on the so-called grey
level co-occurrence matrices (GLCM; after Haralick 1979).

<ul>
<li> 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.
 <br>
 Note: The square root of the ASM is sometimes used as a texture measure,
 and is called Energy.</li>

<li> 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.</li>

<li> 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 <em>distance</em>.</li>

<li> 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
 <em>distance</em>.</li>

<li> Information Measures of Correlation (MOC)</li>

<li> Maximal Correlation Coefficient (MCC)</li>
</ul>
   
<h2>NOTES</h2>

Importantly, the input raster map cannot have more than 255 categories.

<h2>EXAMPLE</h2>

Calculation of Angular Second Moment of B/W orthophoto (North Carolina data set):

<div class="code"><pre>
g.region rast=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 measure=asm -s 
# display
g.region n=221461 s=221094 w=638279 e=638694
d.shadedmap drape=ortho_texture_ASM_0 rel=ortho_2001_t792_1m
</pre></div>

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.

<h2>BUGS</h2>
The program can run incredibly slow for large raster maps.

<h2>REFERENCES</h2>

The algorithm was implemented after Haralick et al., 1973 and 1979.

<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). Manual page 
of <a href="http://netpbm.sourceforge.net/doc/pgmtexture.html">pgmtexture</a>.

<ul> 
<li>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.</li>
<li>Bouman, C. A., Shapiro, M. (1994). A Multiscale Random Field Model for
 Bayesian Image Segmentation, IEEE Trans. on Image Processing, vol. 3, no. 2.</li>
<li>Jensen, J.R. (1996). Introductory digital image processing. Prentice Hall.
  ISBN 0-13-205840-5 </li>
<li>Haralick, R. (May 1979). <i>Statistical and structural approaches to texture</i>,
   Proceedings of the IEEE, vol. 67, No.5, pp. 786-804</li>
<li>Hall-Beyer, M. (2007). <a href="http://www.fp.ucalgary.ca/mhallbey/tutorial.htm">The GLCM Tutorial Home Page</a>
  (Grey-Level Co-occurrence Matrix texture measurements). University of Calgary, Canada
</ul>

<h2>SEE ALSO</h2>

<em>
<a href="i.smap.html">i.smap</a>,
<a href="i.gensigset.html">i.gensigset</a>,
<a href="i.pca.html">i.pca</a>,
<a href="r.neighbors.html">r.neighbors</a>,
<a href="r.rescale.html">r.rescale</a>
</em>

<h2>AUTHORS</h2>
<a href="mailto:antoniol@ieee.org">G. Antoniol</a> - RCOST (Research Centre on Software Technology - Viale Traiano - 82100 Benevento)<br>
C. Basco -  RCOST (Research Centre on Software Technology - Viale Traiano - 82100 Benevento)<br>
M. Ceccarelli - Facolta di Scienze, Universita del Sannio, Benevento

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