<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <html> <head> <title>GRASS GIS: i.gensigset</title> <meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1"> <link rel="stylesheet" href="grassdocs.css" type="text/css"> </head> <body bgcolor="white"> <img src="grass_logo.png" alt="GRASS logo"><hr align=center size=6 noshade> <h2>NAME</h2> <em><b>i.gensigset</b></em> - Generate statistics for i.smap from raster map layer. <h2>KEYWORDS</h2> imagery <h2>SYNOPSIS</h2> <b>i.gensigset</b><br> <b>i.gensigset help</b><br> <b>i.gensigset</b> <b>trainingmap</b>=<em>string</em> <b>group</b>=<em>string</em> <b>subgroup</b>=<em>string</em> <b>signaturefile</b>=<em>string</em> [<b>maxsig</b>=<em>integer</em>] [--<b>verbose</b>] [--<b>quiet</b>] <h3>Parameters:</h3> <DL> <DT><b>trainingmap</b>=<em>string</em></DT> <DD>ground truth training map</DD> <DT><b>group</b>=<em>string</em></DT> <DD>imagery group</DD> <DT><b>subgroup</b>=<em>string</em></DT> <DD>subgroup containing image files</DD> <DT><b>signaturefile</b>=<em>string</em></DT> <DD>resultant signature file</DD> <DT><b>maxsig</b>=<em>integer</em></DT> <DD>maximum number of sub-signatures in any class</DD> <DD>Default: <em>10</em></DD> </DL> <H2>DESCRIPTION</H2> <EM>i.gensigset</EM> is a non-interactive method for generating input into <EM><A HREF="i.smap.html">i.smap</A>.</EM> It is used as the first pass in the a two-pass classification process. It reads a raster map layer, called the training map, which has some of the pixels or regions already classified. <EM>i.gensigset</EM> will then extract spectral signatures from an image based on the classification of the pixels in the training map and make these signatures available to <EM><A HREF="i.smap.html">i.smap</A>.</EM> <P> The user would then execute the GRASS program <EM> <A HREF="i.smap.html">i.smap</A></EM> to create the final classified map. <H2>OPTIONS</H2> <H3>Parameters</H3> <DL> <DT><B>trainingmap=</B><EM>name</EM> <DD>ground truth training map <P> This raster layer, supplied as input by the user, has some of its pixels already classified, and the rest (probably most) of the pixels unclassified. Classified means that the pixel has a non-zero value and unclassified means that the pixel has a zero value. <P> This map must be prepared by the user in advance. The user must use <EM><A HREF="r.digit.html">r.digit</A></EM>, a combination of <EM><A HREF="v.digit.html">v.digit</A></EM> and <EM><A HREF="v.to.rast.html">v.to.rast</A></EM>, or some other import/developement process (e.g., <EM><A HREF="v.in.transects.html">v.in.transects</A>)</EM> to define the areas representative of the classes in the image. <P> At present, there is no fully-interactive tool specifically designed for producing this layer. <DT><B>group=</B><EM>name</EM> <DD>imagery group <P> This is the name of the group that contains the band files which comprise the image to be analyzed. The <EM><A HREF="i.group.html">i.group</A></EM> command is used to construct groups of raster layers which comprise an image. <P> <DT><B>subgroup=</B><EM>name</EM> <DD>subgroup containing image files <P> This names the subgroup within the group that selects a subset of the bands to be analyzed. The <EM><A HREF="i.group.html">i.group</A></EM> command is also used to prepare this subgroup. The subgroup mechanism allows the user to select a subset of all the band files that form an image. <DT><B>signaturefile=</B><EM>name</EM> <DD>resultant signature file <P> This is the resultant signature file (containing the means and covariance matrices) for each class in the training map that is associated with the band files in the subgroup selected. <P> <DT><B>maxsig=</B><EM>value</EM> <DD>maximum number of sub-signatures in any class <BR> default: 10 <P> The spectral signatures which are produced by this program are "mixed" signatures (see <A HREF="#notes">NOTES</A>). Each signature contains one or more subsignatures (represeting subclasses). The algorithm in this program starts with a maximum number of subclasses and reduces this number to a minimal number of subclasses which are spectrally distinct. The user has the option to set this starting value with this option. </DL> <H2>INTERACTIVE MODE</H2> If none of the arguments are specified on the command line, <EM>i.gensigset</EM> will interactively prompt for the names of these maps and files. <P> It should be noted that interactive mode here only means interactive prompting for maps and files. It does not mean visualization of the signatures that result from the process. <P> <A NAME="notes"><H2>NOTES</H2></A> The algorithm in <EM>i.gensigset</EM> determines the parameters of a spectral class model known as a Gaussian mixture distribution. The parameters are estimated using multispectral image data and a training map which labels the class of a subset of the image pixels. The mixture class parameters are stored as a class signature which can be used for subsequent segmentation (i.e., classification) of the multispectral image. <P> The Gaussian mixture class is a useful model because it can be used to describe the behavior of an information class which contains pixels with a variety of distinct spectral characteristics. For example, forest, grasslands or urban areas are examples of information classes that a user may wish to separate in an image. However, each of these information classes may contain subclasses each with its own distinctive spectral characteristic. For example, a forest may contain a variety of different tree species each with its own spectral behavior. <P> The objective of mixture classes is to improve segmentation performance by modeling each information class as a probabilistic mixture with a variety of subclasses. The mixture class model also removes the need to perform an initial unsupervised segmentation for the purposes of identifying these subclasses. However, if misclassified samples are used in the training process, these erroneous samples may be grouped as a separate undesired subclass. Therefore, care should be taken to provided accurate training data. <P> This clustering algorithm estimates both the number of distinct subclasses in each class, and the spectral mean and covariance for each subclass. The number of subclasses is estimated using Rissanen's minimum description length (MDL) criteria [<A HREF="#rissanen83">1</A>]. This criteria attempts to determine the number of subclasses which "best" describe the data. The approximate maximum likelihood estimates of the mean and covariance of the subclasses are computed using the expectation maximization (EM) algorithm [<A HREF="#dempster77">2</A>,<A HREF="#redner84">3</A>]. <H2>REFERENCES</H2> <OL> <LI><A NAME="rissanen83">J. Rissanen,</A> "A Universal Prior for Integers and Estimation by Minimum Description Length," <EM>Annals of Statistics,</EM> vol. 11, no. 2, pp. 417-431, 1983. <LI><A NAME="dempster77">A. Dempster, N. Laird and D. Rubin,</A> "Maximum Likelihood from Incomplete Data via the EM Algorithm," <EM>J. Roy. Statist. Soc. B,</EM> vol. 39, no. 1, pp. 1-38, 1977. <LI><A NAME="redner84">E. Redner and H. Walker,</A> "Mixture Densities, Maximum Likelihood and the EM Algorithm," <EM>SIAM Review,</EM> vol. 26, no. 2, April 1984. </OL> <H2>SEE ALSO</H2> <EM><A HREF="i.group.html">i.group</A></EM> for creating groups and subgroups <P> <EM><A HREF="v.digit.html">v.digit</A></EM> and <EM><A HREF="r.digit.html">r.digit</A></EM> for interactively creating the training map. <P> <EM><A HREF="i.smap.html">i.smap</A></EM> for creating a final classification layer from the signatures generated by <EM>i.gensigset.</EM> <H2>AUTHORS</H2> Charles Bouman, School of Electrical Engineering, Purdue University <BR> Michael Shapiro, U.S.Army Construction Engineering Research Laboratory <p><i>Last changed: $Date: 2003-04-17 16:51:33 +0200 (Thu, 17 Apr 2003) $</i> <HR> <P><a href="index.html">Main index</a> - <a href="imagery.html">imagery index</a> - <a href="full_index.html">Full index</a></P> <P>© 2003-2008 <a href="http://grass.osgeo.org">GRASS Development Team</a></p> </body> </html>
