[GRASS-SVN] r54314 - in grass/branches/releasebranch_6_4: imagery/i.cluster imagery/i.maxlik imagery/i.smap/shapiro raster/r.kappa
svn_grass at osgeo.org
svn_grass at osgeo.org
Sun Dec 16 04:47:36 PST 2012
Author: neteler
Date: 2012-12-16 04:47:36 -0800 (Sun, 16 Dec 2012)
New Revision: 54314
Modified:
grass/branches/releasebranch_6_4/imagery/i.cluster/description.html
grass/branches/releasebranch_6_4/imagery/i.maxlik/description.html
grass/branches/releasebranch_6_4/imagery/i.smap/shapiro/description.html
grass/branches/releasebranch_6_4/raster/r.kappa/description.html
Log:
examples added; HTML cosmetics
Modified: grass/branches/releasebranch_6_4/imagery/i.cluster/description.html
===================================================================
--- grass/branches/releasebranch_6_4/imagery/i.cluster/description.html 2012-12-16 12:17:33 UTC (rev 54313)
+++ grass/branches/releasebranch_6_4/imagery/i.cluster/description.html 2012-12-16 12:47:36 UTC (rev 54314)
@@ -1,6 +1,5 @@
<h2>DESCRIPTION</h2>
-
<em>i.cluster</em>
performs the first pass in the GRASS two-pass unsupervised
classification of imagery, while the GRASS program <em>
@@ -9,7 +8,6 @@
classification.
<p>
-
<em>i.cluster</em> is a clustering algorithm that reads
through the (raster) imagery data and builds pixel clusters
based on the spectral reflectances of the pixels (see Figure).
@@ -223,10 +221,8 @@
included are the resulting percent convergence for the
clusters, the number of iterations that was required to
achieve the convergence, and the separability matrix.
-
</dl>
-
<h2>NOTES</h2>
Running in command line mode, <em>i.cluster</em> will
@@ -234,6 +230,24 @@
required by the user) without prompting if the files
existed.
+<h2>EXAMPLE</h2>
+
+Preparing the statistics for unsupervised classification of
+a LANDSAT subscene in North Carolina:
+
+<div class="code"><pre>
+g.region rast=lsat7_2002_10 -p
+
+# store VIZ, NIR, MIR into group/subgroup
+i.group group=my_lsat7_2002 subgroup=my_lsat7_2002 \
+ input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
+
+i.cluster group=my_lsat7_2002 subgroup=my_lsat7_2002 sigfile=sig_clust_lsat2002 \
+ classes=10 report=rep_clust_lsat2002.txt
+</pre></div>
+
+To complete the unsupervised classification, <em>i.maxlik</em> is subsequently used.
+
<h2>SEE ALSO</h2>
The GRASS 4 <em>
Modified: grass/branches/releasebranch_6_4/imagery/i.maxlik/description.html
===================================================================
--- grass/branches/releasebranch_6_4/imagery/i.maxlik/description.html 2012-12-16 12:17:33 UTC (rev 54313)
+++ grass/branches/releasebranch_6_4/imagery/i.maxlik/description.html 2012-12-16 12:47:36 UTC (rev 54314)
@@ -68,16 +68,6 @@
<h2>OPTIONS</h2>
-<h3>Flags:</h3>
-
-<dl>
-
-<dt><b>-q</b>
-
-<dd>Run quietly, without printing program messages to standard output.
-
-</dl>
-
<h3>Parameters:</h3>
<dl>
@@ -153,6 +143,22 @@
classified raster map without prompting if this map
existed.
+<h2>EXAMPLE</h2>
+
+Completion of the unsupervised classification of
+a LANDSAT subscene (VIZ, NIR, MIR channels) in North Carolina
+(see <em>i.cluster</em> manual page for the first part):
+
+<div class="code"><pre>
+i.maxlik group=my_lsat7_2002 subgroup=my_lsat7_2002 sigfile=sig_clust_lsat2002 \
+ class=lsat7_2002_clust_classes reject=lsat7_2002_clust_classes.rej
+
+# Visually check result
+d.mon x0
+d.rast.leg lsat7_2002_clust_classes
+d.rast.leg lsat7_2002_clust_classes.rej
+</pre></div>
+
<h2>SEE ALSO</h2>
The GRASS 4 <em>
@@ -161,10 +167,11 @@
<p>
<em>
-<a href="i.class.html">i.class</a><br>
-<a href="i.cluster.html">i.cluster</a><br>
-<a href="i.gensig.html">i.gensig</a><br>
-<a href="i.group.html">i.group</a>
+<a href="i.class.html">i.class</a>,
+<a href="i.cluster.html">i.cluster</a>,
+<a href="i.gensig.html">i.gensig</a>,
+<a href="i.group.html">i.group</a>,
+<a href="r.kappa.html">r.kappa</a>
</em>
<h2>AUTHORS</h2>
Modified: grass/branches/releasebranch_6_4/imagery/i.smap/shapiro/description.html
===================================================================
--- grass/branches/releasebranch_6_4/imagery/i.smap/shapiro/description.html 2012-12-16 12:17:33 UTC (rev 54313)
+++ grass/branches/releasebranch_6_4/imagery/i.smap/shapiro/description.html 2012-12-16 12:47:36 UTC (rev 54314)
@@ -1,7 +1,6 @@
-<H2>DESCRIPTION</H2>
+<h2>DESCRIPTION</h2>
-
-The <EM>i.smap</EM> program is used to segment
+The <em>i.smap</em> program is used to segment
multispectral images using a spectral class model known as
a Gaussian mixture distribution. Since Gaussian mixture
distributions include conventional multivariate Gaussian
@@ -9,78 +8,73 @@
multispectral images based on simple spectral mean and
covariance parameters.
-<P>
-
-<EM>i.smap</EM> has two modes of operation. The first mode
+<p><em>i.smap</em> has two modes of operation. The first mode
is the sequential maximum a posteriori (SMAP) mode
-[<A HREF="#ref1">1</A>,<A HREF="#ref2">2</A>]. The SMAP
+[<a href="#ref1">1</a>,<a href="#ref2">2</a>]. The SMAP
segmentation algorithm attempts to improve segmentation
accuracy by segmenting the image into regions rather than
segmenting each pixel separately
-(see <A HREF="#notes">NOTES</A>).
+(see <a href="#notes">NOTES</a>).
-<P>
-
-The second mode is the more conventional maximum likelihood (ML)
+<p>The second mode is the more conventional maximum likelihood (ML)
classification which classifies each pixel separately,
but requires somewhat less computation. This mode is selected with
-the <B>-m</B> flag (see <A HREF="#mflag.html">below</A>).
+the <b>-m</b> flag (see <a href="#mflag.html">below</a>).
-<H2>OPTIONS</H2>
+<h2>OPTIONS</h2>
-<H3>Flags:</H3>
+<h3>Flags:</h3>
-<DL>
+<dl>
-<DT><B>-m</B></A>
+<dt><b>-m</b></a>
-<DD>Use maximum likelihood estimation (instead of smap).
+<dd>Use maximum likelihood estimation (instead of smap).
Normal operation is to use SMAP estimation (see
-<A HREF="#notes">NOTES</A>).
+<a href="#notes">NOTES</a>).
-<DT><B>-q</B>
+<dt><b>-q</b>
-<DD>Run quietly, without printing messages about program
+<dd>Run quietly, without printing messages about program
progress. Without this flag, messages will be printed (to
stderr) as the program progresses.
-</DL>
+</dl>
-<H3>Parameters:</H3>
+<h3>Parameters:</h3>
-<DL>
-<DT><B>group=</B><EM>name</EM>
+<dl>
+<dt><b>group=</b><em>name</em>
-<DD>imagery group<BR>
+<dd>imagery group<br>
The imagery group that defines the image to be classified.
-<DT><B>subgroup=</B><EM>name</EM>
+<dt><b>subgroup=</b><em>name</em>
-<DD>imagery subgroup<BR>
+<dd>imagery subgroup<br>
The subgroup within the group specified that specifies the
subset of the band files that are to be used as image data
to be classified.
-<DT><B>signaturefile=</B><EM>name</EM>
+<dt><b>signaturefile=</b><em>name</em>
-<DD>imagery signaturefile<BR>
+<dd>imagery signaturefile<br>
The signature file that contains the spectral signatures (i.e., the
statistics) for the classes to be identified in the image.
This signature file is produced by the program
-<EM><A HREF="i.gensigset.html">i.gensigset</A></EM>
-(see <A HREF="#notes">NOTES</A>).
+<em><a href="i.gensigset.html">i.gensigset</a></em>
+(see <a href="#notes">NOTES</a>).
-<DT><B>blocksize=</B><EM>value</EM>
+<dt><b>blocksize=</b><em>value</em>
-<DD>size of submatrix to process at one time<BR>
-default: 128<BR>
+<dd>size of submatrix to process at one time<br>
+default: 128<br>
This option specifies the size of the "window" to be used when
reading the image data.
-<P>
-
+<p>
This program was written to be nice about memory usage
without influencing the resultant classification. This
option allows the user to control how much memory is used.
@@ -88,8 +82,7 @@
on how much real memory your machine has and how much
virtual memory the program uses.
-<P>
-
+<p>
The size of the submatrix used in segmenting the image has
a principle function of controlling memory usage; however,
it also can have a subtle effect on the quality of the
@@ -102,28 +95,27 @@
parameters may be used for each distinctive region of the
image.
-<P>
-
+<p>
The submatrix size has no effect on the performance of the
ML segmentation method.
-<DT><B>output=</B><EM>name</EM>
+<dt><b>output=</b><em>name</em>
-<DD>output raster map.<BR>
+<dd>output raster map.<br>
The name of a raster map that will contain the
classification results. This new raster map layer will
contain categories that can be related to landcover
categories on the ground.
-</DL>
+</dl>
-<H2>INTERACTIVE MODE</H2>
+<h2>INTERACTIVE MODE</h2>
If none of the arguments are specified on the command line,
-<EM>i.smap</EM> will interactively prompt for the names of
+<em>i.smap</em> will interactively prompt for the names of
the maps and files.
-<A NAME="notes"><H2>NOTES</H2></A>
+<a name="notes"><h2>NOTES</h2></a>
The SMAP algorithm exploits the fact that nearby pixels in
an image are likely to have the same class. It works by
@@ -134,8 +126,7 @@
segmentations with larger connected regions of a fixed
class which may be useful in some applications.
-<P>
-
+<p>
The amount of smoothing that is performed in the
segmentation is dependent of the behavior of the data in
the image. If the data suggests that the nearby pixels
@@ -143,16 +134,46 @@
reduce the amount of smoothing. This ensures that
excessively large regions are not formed.
-<P>
-
-The module i.smap does not support MASKed or NULL cells. Therefore
+<p>
+The module <em>i.smap</em> does not support MASKed or NULL cells. Therefore
it might be necessary to create a copy of the classification results
-using e.g. r.mapcalc.
-<p>
-r.mapcalc MASKed_map=classification results
+using e.g. r.mapcalc:
+<p><div class="code"><pre>
+r.mapcalc "MASKed_map = classification_results"
+</pre></div>
-<H2>REFERENCES</H2>
+<h2>EXAMPLE</h2>
+Supervised classification of LANDSAT
+
+<div class="code"><pre>
+g.region rast=lsat7_2002_10 -p
+
+# store VIZ, NIR, MIR into group/subgroup
+i.group group=my_lsat7_2002 subgroup=my_lsat7_2002 \
+ input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
+
+# Now digitize training areas "training" with the digitizer
+# and convert to raster model with v.to.rast
+v.to.rast training out=training use=cat labelcolumn=label
+
+# calculate statistics
+i.gensigset trainingmap=training group=my_lsat7_2002 subgroup=my_lsat7_2002 \
+ signaturefile=my_smap_lsat7_2002 maxsig=5
+
+i.smap group=my_lsat7_2002 subgroup=my_lsat7_2002 signaturefile=my_smap_lsat7_2002 \
+ output=lsat7_2002_smap_classes
+
+# Visually check result
+d.mon x0
+d.rast.leg lsat7_2002_smap_classes
+
+# Statistically check result
+r.kappa -w classification=lsat7_2002_smap_classes reference=training
+</pre></div>
+
+<h2>REFERENCES</h2>
+
<ul>
<li>C. Bouman and M. Shapiro,
"Multispectral Image Segmentation using a Multiscale Image Model",
@@ -169,29 +190,28 @@
<em>IEEE Trans. on Geoscience and Remote Sensing, 33(6): 1313-1316.</em>
</ul>
-<H2>SEE ALSO</H2>
+<h2>SEE ALSO</h2>
-<EM><A HREF="i.group.html">i.group</A></EM>
+<em><a href="i.group.html">i.group</a></em>
for creating groups and subgroups
-<P>
-
-<EM><A HREF="r.mapcalc.html">r.mapcalc</A></EM>
+<p>
+<em><a href="r.mapcalc.html">r.mapcalc</a></em>
to copy classification result in order to cut out MASKed subareas
-<P>
-
-<EM><A HREF="i.gensigset.html">i.gensigset</A></EM>
+<p>
+<em><a href="i.gensigset.html">i.gensigset</a></em>
to generate the signature file required by this program
-<H2>AUTHORS</H2>
+<h2>AUTHORS</h2>
-<a href=http://dynamo.ecn.purdue.edu/~bouman/software/segmentation/>Charles Bouman,
+<a href="http://dynamo.ecn.purdue.edu/~bouman/software/segmentation/">Charles Bouman,
School of Electrical Engineering, Purdue University</a>
-<BR>
+<p>
Michael Shapiro,
U.S.Army Construction Engineering
Research Laboratory
-<p><i>Last changed: $Date$</i>
+<p>
+<i>Last changed: $Date$</i>
Modified: grass/branches/releasebranch_6_4/raster/r.kappa/description.html
===================================================================
--- grass/branches/releasebranch_6_4/raster/r.kappa/description.html 2012-12-16 12:17:33 UTC (rev 54313)
+++ grass/branches/releasebranch_6_4/raster/r.kappa/description.html 2012-12-16 12:47:36 UTC (rev 54314)
@@ -1,6 +1,5 @@
<h2>DESCRIPTION</h2>
-
<em>r.kappa</em> tabulates the error matrix of classification result by
crossing classified map layer with respect to reference map layer. Both
overall <em>kappa</em> (accompanied by its <em>variance</em>) and
@@ -21,7 +20,6 @@
plain text format and named by user at prompt of running
the program.
-
<p>
The body of the report is arranged in panels. The
classified result map layer categories is arranged along
@@ -45,10 +43,17 @@
information for each and every category.
<p>
-
<em>NA</em>'s in output file mean non-applicable in case
<em>MASK</em> exists.
+<H2>EXAMPLE</H2>
+
+Verification of classified LANDSAT scene against training areas:
+
+<div class="code"><pre>
+r.kappa -w classification=lsat7_2002_classes reference=training
+</pre></div>
+
<h2>SEE ALSO</h2>
<em><a href="g.region.html">g.region</a></em>,
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