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
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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 fi‐
61 nal classified map.
62 For all raster maps used to generate signature file it is recommended
64 to have semantic label set. Use r.support to set semantic labels of
65 each member of the imagery group. Signatures generated for one scene
66 are suitable for classification of other scenes as long as they consist
67 of same raster bands (semantic labels match). If semantic labels are
68 not set, it will be possible to use obtained signature file to classify
69 only the same imagery group used for generating signatures.
70 An usage example can be found in i.smap documentation.
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74 Parameters
75 trainingmap=name
76 ground truth training map
77 This raster layer, supplied as input by the user, has some of its pix‐
79 els already classified, and the rest (probably most) of the pixels un‐
80 classified. Classified means that the pixel has a non-zero value and
81 unclassified means that the pixel has a zero value.
82 This map must be prepared by the user in advance by using a combination
84 of wxGUI vector digitizer and v.to.rast, or some other import/develop‐
85 ment process (e.g., v.transects) to define the areas representative of
86 the classes in the image.
87 At present, there is no fully-interactive tool specifically designed
89 for producing this layer.
90 group=name
92 imagery group
93 This is the name of the group that contains the band files which com‐
95 prise the image to be analyzed. The i.group command is used to con‐
96 struct groups of raster layers which comprise an image.
97 subgroup=name
99 subgroup containing image files
100 This names the subgroup within the group that selects a subset of the
102 bands to be analyzed. The i.group command is also used to prepare this
103 subgroup. The subgroup mechanism allows the user to select a subset of
104 all the band files that form an image.
105 signaturefile=name
107 resultant signature file
108 This is the resultant signature file (containing the means and covari‐
110 ance matrices) for each class in the training map that is associated
111 with the band files in the subgroup selected.
112 maxsig=value
114 maximum number of sub-signatures in any class
115 default: 5
116 The spectral signatures which are produced by this program are "mixed"
118 signatures (see NOTES). Each signature contains one or more subsigna‐
119 tures (represeting subclasses). The algorithm in this program starts
120 with a maximum number of subclasses and reduces this number to a mini‐
121 mal number of subclasses which are spectrally distinct. The user has
122 the option to set this starting value with this option.
123
125 The algorithm in i.gensigset determines the parameters of a spectral
126 class model known as a Gaussian mixture distribution. The parameters
127 are estimated using multispectral image data and a training map which
128 labels the class of a subset of the image pixels. The mixture class
129 parameters are stored as a class signature which can be used for subse‐
130 quent segmentation (i.e., classification) of the multispectral image.
131 The Gaussian mixture class is a useful model because it can be used to
133 describe the behavior of an information class which contains pixels
134 with a variety of distinct spectral characteristics. For example, for‐
135 est, grasslands or urban areas are examples of information classes that
136 a user may wish to separate in an image. However, each of these infor‐
137 mation classes may contain subclasses each with its own distinctive
138 spectral characteristic. For example, a forest may contain a variety
139 of different tree species each with its own spectral behavior.
140 The objective of mixture classes is to improve segmentation performance
142 by modeling each information class as a probabilistic mixture with a
143 variety of subclasses. The mixture class model also removes the need
144 to perform an initial unsupervised segmentation for the purposes of
145 identifying these subclasses. However, if misclassified samples are
146 used in the training process, these erroneous samples may be grouped as
147 a separate undesired subclass. Therefore, care should be taken to pro‐
148 vided accurate training data.
149 This clustering algorithm estimates both the number of distinct sub‐
151 classes in each class, and the spectral mean and covariance for each
152 subclass. The number of subclasses is estimated using Rissanen’s mini‐
153 mum description length (MDL) criteria [1]. This criteria attempts to
154 determine the number of subclasses which "best" describe the data. The
155 approximate maximum likelihood estimates of the mean and covariance of
156 the subclasses are computed using the expectation maximization (EM) al‐
157 gorithm [2,3].
158
160 If warnings like this occur, reducing the remaining classes to 0:
161 ...
162 WARNING: Removed a singular subsignature number 1 (4 remain)
163 WARNING: Removed a singular subsignature number 1 (3 remain)
164 WARNING: Removed a singular subsignature number 1 (2 remain)
165 WARNING: Removed a singular subsignature number 1 (1 remain)
166 WARNING: Unreliable clustering. Try a smaller initial number of clusters
167 WARNING: Removed a singular subsignature number 1 (-1 remain)
168 WARNING: Unreliable clustering. Try a smaller initial number of clusters
169 Number of subclasses is 0
170 then the user should check for:
171 • the range of the input data should be between 0 and 100 or 255
173 but not between 0.0 and 1.0 (r.info and r.univar show the
174 range)
175 • the training areas need to contain a sufficient amount of pix‐
177 els
178
180 • J. Rissanen, "A Universal Prior for Integers and Estimation by
181 Minimum Description Length," Annals of Statistics, vol. 11, no.
182 2, pp. 417-431, 1983.
183 • A. Dempster, N. Laird and D. Rubin, "Maximum Likelihood from
185 Incomplete Data via the EM Algorithm," J. Roy. Statist. Soc. B,
186 vol. 39, no. 1, pp. 1-38, 1977.
187 • E. Redner and H. Walker, "Mixture Densities, Maximum Likelihood
189 and the EM Algorithm," SIAM Review, vol. 26, no. 2, April 1984.
190
192 r.support, i.group, i.smap, r.info, r.univar, wxGUI vector digitizer
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195 Charles Bouman, School of Electrical Engineering, Purdue University
196 Michael Shapiro, U.S.Army Construction Engineering Research Laboratory
197 Semantic label support: Maris Nartiss, University of Latvia
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200 Available at: i.gensigset source code (history)
201 Accessed: Saturday Oct 28 18:19:04 2023
203 Main index | Imagery index | Topics index | Keywords index | Graphical
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206 © 2003-2023 GRASS Development Team, GRASS GIS 8.3.1 Reference Manual
208GRASS 8.3.1 i.gensigset(1)