1i.smap(1) Grass User's Manual i.smap(1)
2
6 i.smap - Performs contextual image classification using sequential
7 maximum a posteriori (SMAP) estimation.
8
10 imagery, classification, supervised classification, segmentation, SMAP
11
13 i.smap
14 i.smap --help
15 i.smap [-m] group=name subgroup=name signaturefile=name output=name
16 [goodness=name] [blocksize=integer] [--overwrite] [--help]
17 [--verbose] [--quiet] [--ui]
18 Flags:
20 -m
21 Use maximum likelihood estimation (instead of smap)
22 --overwrite
24 Allow output files to overwrite existing files
25 --help
27 Print usage summary
28 --verbose
30 Verbose module output
31 --quiet
33 Quiet module output
34 --ui
36 Force launching GUI dialog
37 Parameters:
39 group=name [required]
40 Name of input imagery group
41 subgroup=name [required]
43 Name of input imagery subgroup
44 signaturefile=name [required]
46 Name of input file containing signatures
47 Generated by i.gensigset
48 output=name [required]
50 Name for output raster map holding classification results
51 goodness=name
53 Name for output raster map holding goodness of fit (lower is bet‐
54 ter)
55 blocksize=integer
57 Size of submatrix to process at one time
58 Default: 1024
59
61 The i.smap program is used to segment multispectral images using a
62 spectral class model known as a Gaussian mixture distribution. Since
63 Gaussian mixture distributions include conventional multivariate Gauss‐
64 ian distributions, this program may also be used to segment multispec‐
65 tral images based on simple spectral mean and covariance parameters.
66 i.smap has two modes of operation. The first mode is the sequential
68 maximum a posteriori (SMAP) mode [1,2]. The SMAP segmentation algo‐
69 rithm attempts to improve segmentation accuracy by segmenting the image
70 into regions rather than segmenting each pixel separately (see NOTES).
71 The second mode is the more conventional maximum likelihood (ML) clas‐
73 sification which classifies each pixel separately, but requires some‐
74 what less computation. This mode is selected with the -m flag (see
75 below).
76
78 Flags:
79 -m
80 Use maximum likelihood estimation (instead of smap). Normal opera‐
81 tion is to use SMAP estimation (see NOTES).
82 Parameters:
84 group=name
85 imagery group
86 The imagery group that defines the image to be classified.
87 subgroup=name
89 imagery subgroup
90 The subgroup within the group specified that specifies the subset
91 of the band files that are to be used as image data to be classi‐
92 fied.
93 signaturefile=name
95 imagery signaturefile
96 The signature file that contains the spectral signatures (i.e., the
97 statistics) for the classes to be identified in the image. This
98 signature file is produced by the program i.gensigset (see NOTES).
99 blocksize=value
101 size of submatrix to process at one time
102 default: 1024
103 This option specifies the size of the "window" to be used when
104 reading the image data.
105 This program was written to be nice about memory usage without influ‐
107 encing the resultant classification. This option allows the user to
108 control how much memory is used. More memory may mean faster (or
109 slower) operation depending on how much real memory your machine has
110 and how much virtual memory the program uses.
111 The size of the submatrix used in segmenting the image has a principle
113 function of controlling memory usage; however, it also can have a sub‐
114 tle effect on the quality of the segmentation in the smap mode. The
115 smoothing parameters for the smap segmentation are estimated separately
116 for each submatrix. Therefore, if the image has regions with qualita‐
117 tively different behavior, (e.g., natural woodlands and man-made agri‐
118 cultural fields) it may be useful to use a submatrix small enough so
119 that different smoothing parameters may be used for each distinctive
120 region of the image.
121 The submatrix size has no effect on the performance of the ML segmenta‐
123 tion method.
124 output=name
126 output raster map.
127 The name of a raster map that will contain the classification
128 results. This new raster map layer will contain categories that
129 can be related to landcover categories on the ground.
130
132 If none of the arguments are specified on the command line, i.smap will
133 interactively prompt for the names of the maps and files.
134
136 The SMAP algorithm exploits the fact that nearby pixels in an image are
137 likely to have the same class. It works by segmenting the image at
138 various scales or resolutions and using the coarse scale segmentations
139 to guide the finer scale segmentations. In addition to reducing the
140 number of misclassifications, the SMAP algorithm generally produces
141 segmentations with larger connected regions of a fixed class which may
142 be useful in some applications.
143 The amount of smoothing that is performed in the segmentation is depen‐
145 dent of the behavior of the data in the image. If the data suggests
146 that the nearby pixels often change class, then the algorithm will
147 adaptively reduce the amount of smoothing. This ensures that exces‐
148 sively large regions are not formed.
149 The degree of misclassifications can be investigated with the goodness
151 of fit output map. Lower values indicate a better fit. The largest 5 to
152 15% of the goodness values may need some closer inspection.
153 The module i.smap does not support MASKed or NULL cells. Therefore it
155 might be necessary to create a copy of the classification results using
156 e.g. r.mapcalc:
157 r.mapcalc "MASKed_map = classification_results"
159
161 Supervised classification of LANDSAT
162 g.region raster=lsat7_2002_10 -p
163 # store VIZ, NIR, MIR into group/subgroup
164 i.group group=my_lsat7_2002 subgroup=my_lsat7_2002 \
165 input=lsat7_2002_10,lsat7_2002_20,lsat7_2002_30,lsat7_2002_40,lsat7_2002_50,lsat7_2002_70
166 # Now digitize training areas "training" with the digitizer
167 # and convert to raster model with v.to.rast
168 v.to.rast input=training output=training use=cat label_column=label
169 # calculate statistics
170 i.gensigset trainingmap=training group=my_lsat7_2002 subgroup=my_lsat7_2002 \
171 signaturefile=my_smap_lsat7_2002 maxsig=5
172 i.smap group=my_lsat7_2002 subgroup=my_lsat7_2002 signaturefile=my_smap_lsat7_2002 \
173 output=lsat7_2002_smap_classes
174 # Visually check result
175 d.mon wx0
176 d.rast.leg lsat7_2002_smap_classes
177 # Statistically check result
178 r.kappa -w classification=lsat7_2002_smap_classes reference=training
179
181 · C. Bouman and M. Shapiro, "Multispectral Image Segmentation
182 using a Multiscale Image Model", Proc. of IEEE Int’l Conf. on
183 Acoust., Speech and Sig. Proc., pp. III-565 - III-568, San
184 Francisco, California, March 23-26, 1992.
185 · C. Bouman and M. Shapiro 1994, "A Multiscale Random Field Model
187 for Bayesian Image Segmentation", IEEE Trans. on Image Process‐
188 ing., 3(2), 162-177" (PDF)
189 · McCauley, J.D. and B.A. Engel 1995, "Comparison of Scene Seg‐
191 mentations: SMAP, ECHO and Maximum Likelyhood", IEEE Trans. on
192 Geoscience and Remote Sensing, 33(6): 1313-1316.
193
195 i.group for creating groups and subgroups
196 r.mapcalc to copy classification result in order to cut out MASKed sub‐
197 areas
198 i.gensigset to generate the signature file required by this program
199 g.gui.iclass, i.maxlik, r.kappa
201
203 Charles Bouman, School of Electrical Engineering, Purdue University
204 Michael Shapiro, U.S.Army Construction Engineering Research Laboratory
206
208 Available at: i.smap source code (history)
209 Main index | Imagery index | Topics index | Keywords index | Graphical
211 index | Full index
212 © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual
214GRASS 7.8.2 i.smap(1)