[GRASS-SVN] r49383 -
grass/branches/releasebranch_6_4/raster/r.texture
svn_grass at osgeo.org
svn_grass at osgeo.org
Sun Nov 27 09:02:11 EST 2011
Author: neteler
Date: 2011-11-27 06:02:11 -0800 (Sun, 27 Nov 2011)
New Revision: 49383
Modified:
grass/branches/releasebranch_6_4/raster/r.texture/description.html
Log:
restructured
Modified: grass/branches/releasebranch_6_4/raster/r.texture/description.html
===================================================================
--- grass/branches/releasebranch_6_4/raster/r.texture/description.html 2011-11-27 13:40:04 UTC (rev 49382)
+++ grass/branches/releasebranch_6_4/raster/r.texture/description.html 2011-11-27 14:02:11 UTC (rev 49383)
@@ -6,20 +6,24 @@
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 has to be rescaled to 0 to 255 before if needed.
+The input has to be rescaled to 0 to 255 beforehand if the input map range
+is outside of this range by using <em>r.rescale</em>.
<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.
+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.
+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
@@ -29,9 +33,40 @@
Several texture measures are directly computed from the grey level co-occurrence
matrix.
<p>
-The following are brief explanations of texture measures:
-<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
@@ -45,37 +80,19 @@
local homogeneity of a digital image. Low values are associated with low homogeneity
and vice versa.</li>
-<li> Contrast (Contr):
+<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 (Corr):
+<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> Variance (Var): A measure of gray tone variance within the moving
- window (second-order moment about the mean)</li>
+<li> Information Measures of Correlation (MOC)</li>
-<li> Difference Variance (DV): ...</li>
-
-<li> Sum Variance (SV): ... </li>
-
-<li> Sum Average (SA): ...</li>
-
-<li> Entropy (Entr):
- 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> Information Measures of Correlation (MOC): ...</li>
-
-<li> Maximal Correlation Coefficient (MCC): ...</li>
+<li> Maximal Correlation Coefficient (MCC)</li>
</ul>
<h2>NOTES</h2>
@@ -109,20 +126,22 @@
<h2>REFERENCES</h2>
-The algorithm was implemented after Haralick et al., 1973.
+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). <br>
-Manual page of <a href="http://netpbm.sourceforge.net/doc/pgmtexture.html">pgmtexture</a>
+Station, employer for hire of James Darrell McCauley). Manual page
+of <a href="http://netpbm.sourceforge.net/doc/pgmtexture.html">pgmtexture</a>.
-</ul>
+<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>
@@ -135,6 +154,7 @@
<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>
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