[GRASS-SVN] r55013 - grass/trunk/imagery/i.segment
svn_grass at osgeo.org
svn_grass at osgeo.org
Tue Feb 12 03:44:03 PST 2013
Author: mmetz
Date: 2013-02-12 03:44:03 -0800 (Tue, 12 Feb 2013)
New Revision: 55013
Added:
grass/trunk/imagery/i.segment/i.segment.html
Log:
i.segment manual update
Copied: grass/trunk/imagery/i.segment/i.segment.html (from rev 54999, grass/trunk/imagery/i.segment/i.segment.xl.html)
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--- grass/trunk/imagery/i.segment/i.segment.html (rev 0)
+++ grass/trunk/imagery/i.segment/i.segment.html 2013-02-12 11:44:03 UTC (rev 55013)
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+<h2>DESCRIPTION</h2>
+Image segmentation or object recognition is the process of grouping
+similar pixels into unique segments, also refered to as objects.
+Boundary and region based algorithms are described in the literature,
+currently a region growing and merging algorithm is implemented. Each
+object found during the segmentation process is given a unique ID and
+is a collection of contiguous pixels meeting some criteria. Note the
+contrast with image classification where all pixels similar to each
+other are assigned to the same class and do not need to be contiguous.
+The image segmentation results can be useful on their own, or used as a
+preprocessing step for image classification. The segmentation
+preprocessing step can reduce noise and speed up the classification.
+
+<H2>NOTES</h2>
+
+<h3>Region Growing and Merging</h3>
+This segmentation algorithm sequentially examines all current
+segments in the map. The similarity between the current segment and
+each of its neighbors is calculated according to the given distance
+formula. Segments will be merged if they meet a number of criteria,
+including: 1. The pair is mutually most similar to each other (the
+similarity distance will be smaller than to any other neighbor), and
+2. The similarity must be lower than the input threshold. The process
+is repeated until no merges are made during a complete pass.
+
+<h3>Similarity and Threshold</h3>
+The similarity between segments and unmerged objects is used to
+determine which objects are merged. Smaller distance values indicate a
+closer match, with a similarity score of zero for identical pixels.
+<p>
+During normal processing, merges are only allowed when the
+similarity between two segments is lower than the givem
+threshold value. During the final pass, however, if a minimum
+segment size of 2 or larger is given with the <em>minsize</em>
+parameter, segments with a smaller pixel count will be merged with
+their most similar neighbor even if the similarity is greater than
+the threshold.
+<p>
+The threshold must be larger than 0.0 and smaller than 1.0. A threshold
+of 0 would allow only identical valued pixels to be merged, while a
+threshold of 1 would allow everything to be merged. Initial empirical
+tests indicate threshold values of 0.01 to 0.05 are reasonable values
+to start. It is recommended to start with a low value, e.g. 0.01, and
+then perform hierachical segmentation by using the output of the last
+run as seeds for the next run.
+
+<h4>Calculation Formulas</h4>
+Both Euclidean and Manhattan distances use the normal definition,
+considering each raster in the image group as a dimension.
+
+In future , the distance calculation will also take into account the
+shape characteristics of the segments. The normal distances are then
+multiplied by the input radiometric weight. Next an additional
+contribution is added: (1-radioweight) * {smoothness * smoothness
+weight + compactness * (1-smoothness weight)}, where compactness =
+the Perimeter Length / sqrt( Area ) and smoothness = Perimeter
+Length / the Bounding Box. The perimeter length is estimated as the
+number of pixel sides the segment has.
+
+<h3>Seeds</h3>
+The seeds map can be used to provide either seed pixels (random or
+selected points from which to start the segmentation process) or
+seed segments (results of previous segmentations or
+classifications). The different approaches are automatically
+detected by the program: any pixels that have identical seed values
+and are contiguous will be assigned a unique segment ID.
+<p>
+It is expected that the <em>minsize</em> will be set to 1 if a seed
+map is used, but the program will allow other values to be used. If
+both options are used, the final iteration that ignores the
+threshold also will ignore the seed map and force merges for all
+pixels (not just segments that have grown/merged from the seeds).
+
+<h3>Maximum number of starting segments</h3>
+For the region growing algorithm without starting seeds, each pixel
+is sequentially numbered. The current limit with CELL storage is 2
+billion starting segment IDs. If the initial map has a larger
+number of non-null pixels, there are two workarounds:
+<p>
+1. Use starting seed pixels. (Maximum 2 billion pixels can be
+labeled with positive integers.)
+<p>
+2. Use starting seed segments. (By initial classification or other
+methods.)
+
+<h3>Boundary Constraints</h3>
+Boundary constraints limit the adjacency of pixels and segments.
+Each unique value present in the <em>bounds</em> raster are
+considered as a MASK. Thus no segments in the final segmentated map
+will cross a boundary, even if their spectral data is very similar.
+
+<h3>Minimum Segment Size</h3>
+To reduce the salt and pepper affect, a <em>minsize</em> greater
+than 1 will add one additional pass to the processing. During the
+final pass, the threshold is ignored for any segments smaller then
+the set size, thus forcing very small segments to merge with their
+most similar neighbor.
+
+<h2>EXAMPLES</h2>
+This example uses the ortho photograph included in the NC Sample
+Dataset. Set up an imagery group:<br>
+<div class="code"><pre>
+i.group group=ortho_group input=ortho_2001_t792_1m at PERMANENT
+</pre></div>
+
+<p>Set the region to a smaller test region. <br>
+
+<div class="code"><pre>
+g.region -p n=220446 s=220075 e=639151 w=638592
+</pre></div>
+
+Try out a low threshold and check the results.<br>
+<div class="code"><pre>
+i.segment group=ortho_group output=ortho_segs_l1 threshold=0.02
+</pre></div>
+<p></p>
+From a visual inspection, it seems this results in too many segments.
+Increasing the threshold, using the previous results as seeds,
+and setting a minimum size of 2: <br>
+<div class="code"><pre>
+i.segment group=ortho_group output=ortho_segs_l2 threshold=0.05 \
+ seeds=ortho_segs_l1 min=2
+
+i.segment group=ortho_group output=ortho_segs_l3 threshold=0.1 \
+ seeds=ortho_segs_l2
+
+i.segment group=ortho_group output=ortho_segs_l4 threshold=0.2 \
+ seeds=ortho_segs_l3
+
+i.segment group=ortho_group output=ortho_segs_l5 threshold=0.3 \
+ seeds=ortho_segs_l4
+</pre></div>
+<p>
+The output ortho_segs_l4 with threshold=0.2 looks best. There is some
+noise in the image, lets next force all segments smaller than 5 pixels
+to be merged into their most similar neighbor (even if they are less
+similar then required by our threshold):<br>
+
+<p>Set the region to match the entire map(s) in the group. <br>
+<div class="code"><pre>
+g.region -p rast=ortho_2001_t792_1m at PERMANENT
+</pre></div>
+
+Run i.segment on the full map:
+
+<div class="code"><pre>
+i.segment group=ortho_group output=ortho_segs_final \
+ threshold=0.2 min=5
+</pre></div>
+<p></p>
+Processing the entire ortho image with nearly 10 million pixels took about
+15 minutes for the first run.
+
+<h2>TODO</h2>
+<h3>Functionality</h3>
+<ul>
+<li>Further testing of the shape characteristics (smoothness,
+compactness), if it looks good it should be added.
+<b>in progress</b></li>
+<li>Malahanobis distance for the similarity calculation.</li>
+</ul>
+<h3>Use of Segmentation Results</h3>
+<ul>
+<li>Improve the optional output from this module, or better yet, add a
+module for <em>i.segment.metrics</em>.</li>
+<li>Providing updates to i.maxlik to ensure the segmentation output can
+be used as input for the existing classification functionality.</li>
+<li>Integration/workflow for <em>r.fuzzy</em>.</li>
+</ul>
+<h3>Speed</h3>
+<ul>
+<li>See create_isegs.c</li>
+</ul>
+<H2>REFERENCES</h2>
+This project was first developed during GSoC 2012. Project documentation,
+Image Segmentation references, and other information is at the
+<a href="http://grass.osgeo.org/wiki/GRASS_GSoC_2012_Image_Segmentation">project wiki</a>.
+<p>
+Information about
+<a href="http://grass.osgeo.org/wiki/Image_classification">classification in GRASS</a>
+is at available on the wiki.
+</p>
+<h2>SEE ALSO</h2>
+<em>
+<a href="i.group.html">i.group</a>,
+<a href="i.maxlik.html">i.maxlik</a>,
+<a href="r.fuzzy">r.fuzzy</a>,
+<a href="i.smap.html">i.smap</a>,
+<a href="r.seg.html">r.seg</a> (Addon)
+</em>
+
+<h2>AUTHORS</h2>
+Eric Momsen - North Dakota State University<br>
+Markus Metz (GSoC Mentor)
+
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