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List:       grass-commit
Subject:    [GRASS-SVN] r58109 - grass/branches/develbranch_6/imagery/i.smap/shapiro
From:       svn_grass () osgeo ! org
Date:       2013-10-27 22:28:53
Message-ID: 20131027222853.50A1B390651 () trac ! osgeo ! org
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Author: hamish
Date: 2013-10-27 15:28:53 -0700 (Sun, 27 Oct 2013)
New Revision: 58109

Modified:
   grass/branches/develbranch_6/imagery/i.smap/shapiro/description.html
Log:
fix two html bugs

Modified: grass/branches/develbranch_6/imagery/i.smap/shapiro/description.html
===================================================================
--- grass/branches/develbranch_6/imagery/i.smap/shapiro/description.html	2013-10-27 \
                22:27:49 UTC (rev 58108)
+++ grass/branches/develbranch_6/imagery/i.smap/shapiro/description.html	2013-10-27 \
22:28:53 UTC (rev 58109) @@ -8,7 +8,8 @@
 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
 segmentation algorithm attempts to improve segmentation
@@ -17,18 +18,20 @@
 (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>).
 
+
 <h2>OPTIONS</h2>
 
 <h3>Flags:</h3>
 
 <dl>
 
-<dt><b>-m</b></a>
+<dt><b>-m</b>
 
 <dd>Use maximum likelihood estimation (instead of smap).
 Normal operation is to use SMAP estimation (see
@@ -109,14 +112,17 @@
 
 </dl>
 
+
 <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
 the maps and files.
 
-<a name="notes"><h2>NOTES</h2></a>
 
+<a name="notes"></a>
+<h2>NOTES</h2>
+
 The SMAP algorithm exploits the fact that nearby pixels in
 an image are likely to have the same class.  It works by
 segmenting the image at various scales or resolutions and
@@ -142,6 +148,7 @@
 r.mapcalc "MASKed_map = classification_results"
 </pre></div>
 
+
 <h2>EXAMPLE</h2>
 
 Supervised classification of LANDSAT
@@ -172,6 +179,7 @@
 r.kappa -w classification=lsat7_2002_smap_classes reference=training
 </pre></div>
 
+
 <h2>REFERENCES</h2>
 
 <ul>
@@ -190,6 +198,7 @@
 <em>IEEE Trans. on Geoscience and Remote Sensing, 33(6): 1313-1316.</em>
 </ul>
 
+
 <h2>SEE ALSO</h2>
 
 <em><a href="i.group.html">i.group</a></em>
@@ -203,6 +212,7 @@
 <em><a href="i.gensigset.html">i.gensigset</a></em>
 to generate the signature file required by this program
 
+
 <h2>AUTHORS</h2>
 
 <a href="http://dynamo.ecn.purdue.edu/~bouman/software/segmentation/">Charles \
Bouman, 

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