i.gensigset - Generate statistics for i.smap from raster map layer.

i.gensigset

i.gensigset help

i.gensigset trainingmap=string group=string subgroup=string signaturefile=string [maxsig=integer]

Parameters:

i.gensigset is a non-interactive method for generating input into i.smap. 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. i.gensigset 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 i.smap.

The user would then execute the GRASS program i.smap to create the final classified map.

Parameters

If none of the arguments are specified on the command line, i.gensigset will interactively prompt for the names of these maps and files.

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.

The algorithm in i.gensigset 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.

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.

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.

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 [1]. 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 [3].

J. Rissanen, A Universal Prior for Integers and Estimation by Minimum Description Length," Annals of Statistics, vol. 11, no. 2, pp. 417-431, 1983.

A. Dempster, N. Laird and D. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm," J. Roy. Statist. Soc. B, vol. 39, no. 1, pp. 1-38, 1977.

E. Redner and H. Walker, Mixture Densities, Maximum Likelihood and the EM Algorithm," SIAM Review, vol. 26, no. 2, April 1984.

i.group for creating groups and subgroups

v.digit and r.digit for interactively creating the training map.

i.smap for creating a final classification layer from the signatures generated by i.gensigset.

Charles Bouman, School of Electrical Engineering, Purdue University

Michael Shapiro, U.S.Army Construction Engineering Research Laboratory

Last changed: $Date: 2003/04/17 14:49:32 $

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