1i.gensigset(1) GRASS GIS User's Manual i.gensigset(1)
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6 i.gensigset - Generates statistics for i.smap from raster map.
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9 imagery, classification, supervised classification, SMAP, signatures
10
12 i.gensigset
13 i.gensigset --help
14 i.gensigset trainingmap=name group=name subgroup=name signature‐
15 file=name [maxsig=integer] [--overwrite] [--help] [--verbose]
16 [--quiet] [--ui]
17 Flags:
19 --overwrite
20 Allow output files to overwrite existing files
21 --help
23 Print usage summary
24 --verbose
26 Verbose module output
27 --quiet
29 Quiet module output
30 --ui
32 Force launching GUI dialog
33 Parameters:
35 trainingmap=name [required]
36 Ground truth training map
37 group=name [required]
39 Name of input imagery group
40 subgroup=name [required]
42 Name of input imagery subgroup
43 signaturefile=name [required]
45 Name for output file containing result signatures
46 maxsig=integer
48 Maximum number of sub-signatures in any class
49 Default: 5
50
52 i.gensigset is a non-interactive method for generating input into
53 i.smap. It is used as the first pass in the a two-pass classification
54 process. It reads a raster map layer, called the training map, which
55 has some of the pixels or regions already classified. i.gensigset will
56 then extract spectral signatures from an image based on the classifica‐
57 tion of the pixels in the training map and make these signatures avail‐
58 able to i.smap.
59 The user would then execute the GRASS program i.smap to create the
61 final classified map.
62
64 Parameters
65 trainingmap=name
66 ground truth training map
67 This raster layer, supplied as input by the user, has some of its pix‐
69 els already classified, and the rest (probably most) of the pixels
70 unclassified. Classified means that the pixel has a non-zero value and
71 unclassified means that the pixel has a zero value.
72 This map must be prepared by the user in advance by using a combination
74 of wxGUI vector digitizer and v.to.rast, or some other import/develop‐
75 ment process (e.g., v.transects) to define the areas representative of
76 the classes in the image.
77 At present, there is no fully-interactive tool specifically designed
79 for producing this layer.
80 group=name
82 imagery group
83 This is the name of the group that contains the band files which com‐
85 prise the image to be analyzed. The i.group command is used to con‐
86 struct groups of raster layers which comprise an image.
87 subgroup=name
89 subgroup containing image files
90 This names the subgroup within the group that selects a subset of the
92 bands to be analyzed. The i.group command is also used to prepare this
93 subgroup. The subgroup mechanism allows the user to select a subset of
94 all the band files that form an image.
95 signaturefile=name
97 resultant signature file
98 This is the resultant signature file (containing the means and covari‐
100 ance matrices) for each class in the training map that is associated
101 with the band files in the subgroup selected.
102 maxsig=value
104 maximum number of sub-signatures in any class
105 default: 5
106 The spectral signatures which are produced by this program are "mixed"
108 signatures (see NOTES). Each signature contains one or more subsigna‐
109 tures (represeting subclasses). The algorithm in this program starts
110 with a maximum number of subclasses and reduces this number to a mini‐
111 mal number of subclasses which are spectrally distinct. The user has
112 the option to set this starting value with this option.
113
115 If none of the arguments are specified on the command line, i.gensigset
116 will interactively prompt for the names of these maps and files.
117 It should be noted that interactive mode here only means interactive
119 prompting for maps and files. It does not mean visualization of the
120 signatures that result from the process.
121
123 The algorithm in i.gensigset determines the parameters of a spectral
124 class model known as a Gaussian mixture distribution. The parameters
125 are estimated using multispectral image data and a training map which
126 labels the class of a subset of the image pixels. The mixture class
127 parameters are stored as a class signature which can be used for subse‐
128 quent segmentation (i.e., classification) of the multispectral image.
129 The Gaussian mixture class is a useful model because it can be used to
131 describe the behavior of an information class which contains pixels
132 with a variety of distinct spectral characteristics. For example, for‐
133 est, grasslands or urban areas are examples of information classes that
134 a user may wish to separate in an image. However, each of these infor‐
135 mation classes may contain subclasses each with its own distinctive
136 spectral characteristic. For example, a forest may contain a variety
137 of different tree species each with its own spectral behavior.
138 The objective of mixture classes is to improve segmentation performance
140 by modeling each information class as a probabilistic mixture with a
141 variety of subclasses. The mixture class model also removes the need
142 to perform an initial unsupervised segmentation for the purposes of
143 identifying these subclasses. However, if misclassified samples are
144 used in the training process, these erroneous samples may be grouped as
145 a separate undesired subclass. Therefore, care should be taken to pro‐
146 vided accurate training data.
147 This clustering algorithm estimates both the number of distinct sub‐
149 classes in each class, and the spectral mean and covariance for each
150 subclass. The number of subclasses is estimated using Rissanen’s mini‐
151 mum description length (MDL) criteria [1]. This criteria attempts to
152 determine the number of subclasses which "best" describe the data. The
153 approximate maximum likelihood estimates of the mean and covariance of
154 the subclasses are computed using the expectation maximization (EM)
155 algorithm [2,3].
156
158 If warnings like this occur, reducing the remaining classes to 0:
159 ...
160 WARNING: Removed a singular subsignature number 1 (4 remain)
161 WARNING: Removed a singular subsignature number 1 (3 remain)
162 WARNING: Removed a singular subsignature number 1 (2 remain)
163 WARNING: Removed a singular subsignature number 1 (1 remain)
164 WARNING: Unreliable clustering. Try a smaller initial number of clusters
165 WARNING: Removed a singular subsignature number 1 (-1 remain)
166 WARNING: Unreliable clustering. Try a smaller initial number of clusters
167 Number of subclasses is 0
168 then the user should check for:
169 · the range of the input data should be between 0 and 100 or 255
171 but not between 0.0 and 1.0 (r.info and r.univar show the
172 range)
173 · the training areas need to contain a sufficient amount of pix‐
175 els
176
178 · J. Rissanen, "A Universal Prior for Integers and Estimation by
179 Minimum Description Length," Annals of Statistics, vol. 11, no.
180 2, pp. 417-431, 1983.
181 · A. Dempster, N. Laird and D. Rubin, "Maximum Likelihood from
183 Incomplete Data via the EM Algorithm," J. Roy. Statist. Soc. B,
184 vol. 39, no. 1, pp. 1-38, 1977.
185 · E. Redner and H. Walker, "Mixture Densities, Maximum Likelihood
187 and the EM Algorithm," SIAM Review, vol. 26, no. 2, April 1984.
188
190 i.group, i.smap, r.info, r.univar, wxGUI vector digitizer
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193 Charles Bouman, School of Electrical Engineering, Purdue University
194 Michael Shapiro, U.S.Army Construction Engineering Research Laboratory
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197 Available at: i.gensigset source code (history)
198 Main index | Imagery index | Topics index | Keywords index | Graphical
200 index | Full index
201 © 2003-2020 GRASS Development Team, GRASS GIS 7.8.5 Reference Manual
203GRASS 7.8.5 i.gensigset(1)